{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,31]],"date-time":"2026-01-31T10:04:45Z","timestamp":1769853885549,"version":"3.49.0"},"reference-count":156,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2023,12,4]],"date-time":"2023-12-04T00:00:00Z","timestamp":1701648000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004585","name":"Artificial intelligence methods in engineering tasks","doi-asserted-by":"publisher","award":["IGA BUT No. FSI-S-23-8394"],"award-info":[{"award-number":["IGA BUT No. FSI-S-23-8394"]}],"id":[{"id":"10.13039\/501100004585","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computation"],"abstract":"<jats:p>The significance of robot manipulators in engineering applications and scientific research has increased substantially in recent years. The utilization of robot manipulators to save labor and increase production accuracy is becoming a common practice in industry. Evolutionary computation (EC) techniques are optimization methods that have found their use in diverse engineering fields. This state-of-the-art review focuses on recent developments and progress in their applications for industrial robotics, especially for path planning problems that need to satisfy various constraints that are implied by both the geometry of the robot and its surroundings. We discuss the most-used EC method and the modifications that suit this particular purpose, as well as the different simulation environments that are used for their development. Lastly, we outline the possible research gaps and the expected directions future research in this area will entail.<\/jats:p>","DOI":"10.3390\/computation11120245","type":"journal-article","created":{"date-parts":[[2023,12,4]],"date-time":"2023-12-04T12:15:14Z","timestamp":1701692114000},"page":"245","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Evolutionary Computation Techniques for Path Planning Problems in Industrial Robotics: A State-of-the-Art Review"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7943-8659","authenticated-orcid":false,"given":"Martin","family":"Ju\u0159\u00ed\u010dek","sequence":"first","affiliation":[{"name":"Institute of Automation and Computer Science, Brno University of Technology, 602 00 Brno, Czech Republic"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2715-7820","authenticated-orcid":false,"given":"Roman","family":"Par\u00e1k","sequence":"additional","affiliation":[{"name":"Institute of Automation and Computer Science, Brno University of Technology, 602 00 Brno, Czech Republic"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4372-2105","authenticated-orcid":false,"given":"Jakub","family":"K\u016fdela","sequence":"additional","affiliation":[{"name":"Institute of Automation and Computer Science, Brno University of Technology, 602 00 Brno, Czech Republic"}]}],"member":"1968","published-online":{"date-parts":[[2023,12,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Zhang, Y., and Jin, L. (2017). Robot Manipulator Redundancy Resolution, John Wiley & Sons.","DOI":"10.1002\/9781119381440"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"eaat8414","DOI":"10.1126\/science.aat8414","article-title":"Trends and challenges in robot manipulation","volume":"364","author":"Billard","year":"2019","journal-title":"Science"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1109\/70.345948","article-title":"A global approach to path planning for redundant manipulators","volume":"11","author":"Seereeram","year":"1995","journal-title":"IEEE Trans. Robot. Autom."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"338","DOI":"10.1109\/70.143352","article-title":"Real-time obstacle avoidance using harmonic potential functions","volume":"8","author":"Kim","year":"1992","journal-title":"IEEE Trans. Robot. Autom."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"722","DOI":"10.1109\/ROBOT.1985.1087234","article-title":"Kinematic programming alternatives for redundant manipulators","volume":"Volume 2","author":"Baillieul","year":"1985","journal-title":"Proceedings of the 1985 IEEE International Conference on Robotics and Automation"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"777","DOI":"10.1007\/s10846-020-01217-w","article-title":"Analysis of Motion Planning by Sampling in Subspaces of Progressively Increasing Dimension","volume":"100","author":"Xanthidis","year":"2020","journal-title":"J. Intell. Robot. Syst."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"949","DOI":"10.1007\/s00170-021-07985-5","article-title":"Benchmarking and optimization of robot motion planning with motion planning pipeline","volume":"118","author":"Liu","year":"2022","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1108\/IR-06-2016-0166","article-title":"Benchmarking motion planning algorithms for bin-picking applications","volume":"44","author":"Iversen","year":"2017","journal-title":"Ind. Robot. Int. J."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"107603","DOI":"10.1016\/j.cie.2021.107603","article-title":"Deep learning-based optimization for motion planning of dual-arm assembly robots","volume":"160","author":"Ying","year":"2021","journal-title":"Comput. Ind. Eng."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Lobbezoo, A., Qian, Y., and Kwon, H.J. (2021). Reinforcement Learning for Pick and Place Operations in Robotics: A Survey. Robotics, 10.","DOI":"10.3390\/robotics10030105"},{"key":"ref_11","unstructured":"Plappert, M., Andrychowicz, M., Ray, A., McGrew, B., Baker, B., Powell, G., Schneider, J., Tobin, J., Chociej, M., and Welinder, P. (2018). Multi-Goal Reinforcement Learning: Challenging Robotics Environments and Request for Research. arXiv."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1016\/j.neucom.2018.01.002","article-title":"Robot manipulator control using neural networks: A survey","volume":"285","author":"Jin","year":"2018","journal-title":"Neurocomputing"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Liu, R., Nageotte, F., Zanne, P., de Mathelin, M., and Dresp-Langley, B. (2021). Deep reinforcement learning for the control of robotic manipulation: A focussed mini-review. Robotics, 10.","DOI":"10.3390\/robotics10010022"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"198","DOI":"10.1007\/s11465-015-0335-0","article-title":"A systematic review of current and emergent manipulator control approaches","volume":"10","author":"Ajwad","year":"2015","journal-title":"Front. Mech. Eng."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"188","DOI":"10.1016\/j.arcontrol.2017.02.002","article-title":"A review on model reference adaptive control of robotic manipulators","volume":"43","author":"Zhang","year":"2017","journal-title":"Annu. Rev. Control"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Starke, S., Hendrich, N., Magg, S., and Zhang, J. (2016, January 3\u20137). An efficient hybridization of Genetic Algorithms and Particle Swarm Optimization for inverse kinematics. Proceedings of the 2016 IEEE International Conference on Robotics and Biomimetics (ROBIO), Qingdao, China.","DOI":"10.1109\/ROBIO.2016.7866587"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Ruppel, P., Hendrich, N., Starke, S., and Zhang, J. (2018, January 21\u201325). Cost Functions to Specify Full-Body Motion and Multi-Goal Manipulation Tasks. Proceedings of the 2018 IEEE International Conference on Robotics and Automation (ICRA), Brisbane, Australia.","DOI":"10.1109\/ICRA.2018.8460799"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Xiao, Y., Fan, Z., Li, W., Chen, S., Zhao, L., and Xie, H. (2016, January 3\u20134). A Manipulator Design Optimization Based on Constrained Multi-objective Evolutionary Algorithms. Proceedings of the 2016 International Conference on Industrial Informatics\u2014Computing Technology, Intelligent Technology, Industrial Information Integration (ICIICII), Wuhan, China.","DOI":"10.1109\/ICIICII.2016.0056"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Park, J.H., and Lee, K.H. (2021). Computational Design of Modular Robots Based on Genetic Algorithm and Reinforcement Learning. Symmetry, 13.","DOI":"10.3390\/sym13030471"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Hsiao, J.C., Shivam, K., Chou, C.L., and Kam, T.Y. (2020). Shape Design Optimization of a Robot Arm Using a Surrogate-Based Evolutionary Approach. Appl. Sci., 10.","DOI":"10.3390\/app10072223"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Xuan, D.T., Huynh, T.V., Hung, N.T., and Thang, V.T. (2023). Applying Digital Twin and Multi-Adaptive Genetic Algorithms in Human\u2013Robot Cooperative Assembly Optimization. Appl. Sci., 13.","DOI":"10.3390\/app13074229"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"24185","DOI":"10.1109\/ACCESS.2023.3254896","article-title":"Using a Digital Twin as the Objective Function for Evolutionary Algorithm Applications in Large Scale Industrial Processes","volume":"11","author":"Eklund","year":"2023","journal-title":"IEEE Access"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Liu, X., Jiang, D., Tao, B., Jiang, G., Sun, Y., Kong, J., Tong, X., Zhao, G., and Chen, B. (2022). Genetic Algorithm-Based Trajectory Optimization for Digital Twin Robots. Front. Bioeng. Biotechnol., 9.","DOI":"10.3389\/fbioe.2021.793782"},{"key":"ref_24","first-page":"3981081","article-title":"Evolutionary Robot Calibration and Nonlinear Compensation Methodology Based on GA-DNN and an Extra Compliance Error Model","volume":"2020","author":"Chen","year":"2020","journal-title":"Math. Probl. Eng."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"286","DOI":"10.1016\/j.promfg.2018.10.048","article-title":"Path Planning of Cooperating Industrial Robots Using Evolutionary Algorithms","volume":"17","author":"Larsen","year":"2018","journal-title":"Procedia Manuf."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Vargas, P.A., Di Paolo, E.A., Harvey, I., and Husbands, P. (2014). The Horizons of Evolutionary Robotics, MIT Press.","DOI":"10.7551\/mitpress\/8493.001.0001"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"4","DOI":"10.3389\/frobt.2015.00004","article-title":"Evolutionary robotics: What, why, and where to","volume":"2","author":"Doncieux","year":"2015","journal-title":"Front. Robot. AI"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1162\/EVCO_a_00172","article-title":"Open issues in evolutionary robotics","volume":"24","author":"Silva","year":"2016","journal-title":"Evol. Comput."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1","DOI":"10.13164\/mendel.2021.1.001","article-title":"Comparison of multiple reinforcement learning and deep reinforcement learning methods for the task aimed at achieving the goal","volume":"27","author":"Parak","year":"2021","journal-title":"MENDEL J."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"32","DOI":"10.13164\/mendel.2022.1.032","article-title":"Intelligent sampling of anterior human nasal swabs using a collaborative robotic arm","volume":"28","author":"Parak","year":"2022","journal-title":"MENDEL J."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Nof, S.Y. (1999). Handbook of Industrial Robotics, John Wiley & Sons.","DOI":"10.1002\/9780470172506"},{"key":"ref_32","unstructured":"Bonev, I. (2023, October 01). Delta Parallel Robot-the Story of Success. Newsletter. Available online: http:\/\/www.parallelmic.org."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"224","DOI":"10.1109\/41.982266","article-title":"On the trajectory tracking control of industrial SCARA robot manipulators","volume":"49","author":"Visioli","year":"2002","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1007\/s11701-006-0002-x","article-title":"Evolution of robotic arms","volume":"1","author":"Moran","year":"2007","journal-title":"J. Robot. Surg."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Todorov, E., Erez, T., and Tassa, Y. (2012, January 7\u201312). MuJoCo: A physics engine for model-based control. Proceedings of the 2012 IEEE\/RSJ International Conference on Intelligent Robots and Systems, Vilamoura-Algarve, Portugal.","DOI":"10.1109\/IROS.2012.6386109"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"51416","DOI":"10.1109\/ACCESS.2021.3068769","article-title":"A Review of Physics Simulators for Robotic Applications","volume":"9","author":"Collins","year":"2021","journal-title":"IEEE Access"},{"key":"ref_37","first-page":"151","article-title":"How to start a heuristic? Utilizing lower bounds for solving the quadratic assignment problem","volume":"13","author":"Matousek","year":"2022","journal-title":"Int. J. Ind. Eng. Comput."},{"key":"ref_38","unstructured":"Campelo, F., and Aranha, C.d.C. (2021, January 30). Sharks, zombies and volleyball: Lessons from the evolutionary computation bestiary. Proceedings of the CEUR Workshop Proceedings, Milan, Italy."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1841","DOI":"10.1007\/s10462-020-09893-8","article-title":"Nature inspired optimization algorithms or simply variations of metaheuristics?","volume":"54","author":"Tzanetos","year":"2021","journal-title":"Artif. Intell. Rev."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"105792","DOI":"10.1016\/j.dib.2020.105792","article-title":"A comprehensive database of Nature-Inspired Algorithms","volume":"31","author":"Tzanetos","year":"2020","journal-title":"Data Brief"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1016\/j.knosys.2019.01.018","article-title":"The defect of the Grey Wolf optimization algorithm and its verification method","volume":"171","author":"Niu","year":"2019","journal-title":"Knowl.-Based Syst."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"104930","DOI":"10.1016\/j.engappai.2022.104930","article-title":"Commentary on: \u201cSTOA: A bio-inspired based optimization algorithm for industrial engineering problems\u201d [EAAI, 82 (2019), 148\u2013174] and \u201cTunicate Swarm Algorithm: A new bio-inspired based metaheuristic paradigm for global optimization\u201d [EAAI, 90 (2020), no. 103541]","volume":"113","author":"Kudela","year":"2022","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1238","DOI":"10.1038\/s42256-022-00579-0","article-title":"A critical problem in benchmarking and analysis of evolutionary computation methods","volume":"4","author":"Kudela","year":"2022","journal-title":"Nat. Mach. Intell."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"121544","DOI":"10.1016\/j.eswa.2023.121544","article-title":"Deficiencies of the whale optimization algorithm and its validation method","volume":"237","author":"Deng","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Camacho Villal\u00f3n, C.L., St\u00fctzle, T., and Dorigo, M. (2020, January 14\u201320). Grey wolf, firefly and bat algorithms: Three widespread algorithms that do not contain any novelty. Proceedings of the International Conference on Swarm Intelligence, Belgrade, Serbia.","DOI":"10.1007\/978-3-030-60376-2_10"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s11721-021-00202-9","article-title":"Metaphor-based metaheuristics, a call for action: The elephant in the room","volume":"16","author":"Aranha","year":"2022","journal-title":"Swarm Intell."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"13709","DOI":"10.1007\/s00500-022-07362-8","article-title":"Recent advances and applications of surrogate models for finite element method computations: A review","volume":"26","author":"Kudela","year":"2022","journal-title":"Soft Comput."},{"key":"ref_48","unstructured":"Tzanetos, A., and Dounias, G. (2020). Machine Learning Paradigms: Advances in Deep Learning-Based Technological Applications, Springer."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"105521","DOI":"10.1016\/j.engappai.2022.105521","article-title":"A qualitative systematic review of metaheuristics applied to tension\/compression spring design problem: Current situation, recommendations, and research direction","volume":"118","author":"Tzanetos","year":"2023","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"12","DOI":"10.13164\/mendel.2021.2.012","article-title":"Advances in evolutionary optimization of quantum operators","volume":"27","author":"Bidlo","year":"2021","journal-title":"MENDEL J."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"63","DOI":"10.13164\/mendel.2022.1.063","article-title":"Improving initial aerofoil geometry using aerofoil particle swarm optimisation","volume":"28","author":"Muller","year":"2022","journal-title":"MENDEL J."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"105150","DOI":"10.1016\/j.engappai.2022.105150","article-title":"A comprehensive comparison among metaheuristics (MHs) for geohazard modeling using machine learning: Insights from a case study of landslide displacement prediction","volume":"114","author":"Ma","year":"2022","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"41","DOI":"10.13164\/mendel.2022.1.041","article-title":"Meta-heuristics based inverse kinematics of robot manipulator\u2019s path tracking capability under joint limits","volume":"28","author":"Kanagaraj","year":"2022","journal-title":"MENDEL J."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"76","DOI":"10.13164\/mendel.2022.2.076","article-title":"Approximate Solution for Barrier Option Pricing Using Adaptive Differential Evolution With Learning Parameter","volume":"28","author":"Febrianti","year":"2022","journal-title":"MENDEL J."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"503","DOI":"10.1080\/0952813X.2013.782347","article-title":"Metaheuristics: Review and application","volume":"25","author":"Gogna","year":"2013","journal-title":"J. Exp. Theor. Artif. Intell."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"1767","DOI":"10.1007\/s10462-019-09719-2","article-title":"A survey of swarm and evolutionary computing approaches for deep learning","volume":"53","author":"Darwish","year":"2020","journal-title":"Artif. Intell. Rev."},{"key":"ref_57","unstructured":"Kennedy, J., and Eberhart, R. (December, January 27). Particle swarm optimization. Proceedings of the ICNN\u201995\u2014International Conference on Neural Networks, Perth, Australia."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"2531","DOI":"10.1007\/s11831-021-09694-4","article-title":"Particle Swarm Optimization Algorithm and Its Applications: A Systematic Review","volume":"29","author":"Gad","year":"2022","journal-title":"Arch. Comput. Methods Eng."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Zhang, W., and Fu, S. (2020, January 22\u201324). Time-optimal Trajectory Planning of Dulcimer Music Robot Based on PSO Algorithm. Proceedings of the 2020 Chinese Control and Decision Conference (CCDC), Hefei, China.","DOI":"10.1109\/CCDC49329.2020.9164017"},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Shi, B., and Zeng, H. (2021, January 26\u201328). Time-Optimal Trajectory Planning for Industrial Robot based on Improved Hybrid-PSO. Proceedings of the 2021 40th Chinese Control Conference (CCC), Shanghai, China.","DOI":"10.23919\/CCC52363.2021.9549441"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"906","DOI":"10.1049\/cje.2021.00.373","article-title":"Time Optimal Trajectory Planning Algorithm for Robotic Manipulator Based on Locally Chaotic Particle Swarm Optimization","volume":"31","author":"Du","year":"2022","journal-title":"Chin. J. Electron."},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Jiang, Z., and Zhang, Q. (2022, January 8\u201310). Time optimal trajectory planning of five degrees of freedom manipulator based on PSO algorithm. Proceedings of the 2022 4th International Conference on Intelligent Control, Measurement and Signal Processing (ICMSP), Hangzhou, China.","DOI":"10.1109\/ICMSP55950.2022.9858972"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"561","DOI":"10.1007\/s10015-022-00779-2","article-title":"Research on motion trajectory planning of the robotic arm of a robot","volume":"27","author":"Miao","year":"2022","journal-title":"Artif. Life Robot."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Huang, P., and Xu, Y. (2006, January 17\u201320). PSO-Based Time-Optimal Trajectory Planning for Space Robot with Dynamic Constraints. Proceedings of the 2006 IEEE International Conference on Robotics and Biomimetics, Kunming, China.","DOI":"10.1109\/ROBIO.2006.340134"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"620","DOI":"10.1109\/TII.2015.2416435","article-title":"Trajectory Optimization with Particle Swarm Optimization for Manipulator Motion Planning","volume":"11","author":"Kim","year":"2015","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"012185","DOI":"10.1088\/1742-6596\/1820\/1\/012185","article-title":"Industrial robot trajectory planning based on improved pso algorithm","volume":"1820","author":"Han","year":"2021","journal-title":"J. Physics Conf. Ser."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1007\/s41693-021-00057-w","article-title":"Optimal trajectory planning of complicated robotic timber joints based on particle swarm optimization and an adaptive genetic algorithm","volume":"5","author":"Meng","year":"2021","journal-title":"Constr. Robot."},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Zhou, W., Fan, C., Wang, L., Xie, C., Tang, T., and Liu, R. (2022, January 15\u201317). Path planning of manipulator based on improved particle swarm optimization. Proceedings of the 2022 34th Chinese Control and Decision Conference (CCDC), Hefei, China.","DOI":"10.1109\/CCDC55256.2022.10033524"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"6592","DOI":"10.1109\/ACCESS.2022.3141448","article-title":"Serial Manipulator Time-Jerk Optimal Trajectory Planning Based on Hybrid IWOA-PSO Algorithm","volume":"10","author":"Zhao","year":"2022","journal-title":"IEEE Access"},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"106099","DOI":"10.1016\/j.engappai.2023.106099","article-title":"Trajectory planning for a 6-axis robotic arm with particle swarm optimization algorithm","volume":"122","author":"Ekrem","year":"2023","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"1091","DOI":"10.1007\/s00170-019-04421-7","article-title":"An improved PSO algorithm for time-optimal trajectory planning of Delta robot in intelligent packaging","volume":"107","author":"Liu","year":"2020","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_72","unstructured":"Lin, C.J., and Li, M.Y. (2018, January 17\u201320). Motion planning with obstacle avoidance of an UR3 robot using charge system search. Proceedings of the 2018 18th International Conference on Control, Automation and Systems (ICCAS), PyeongChang, Republic of Korea."},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Vysock\u00fd, A., Pap\u0159ok, R., \u0160afa\u0159\u00edk, J., Kot, T., Bobovsk\u00fd, Z., Nov\u00e1k, P., and Sn\u00e1\u0161el, V. (2020). Reduction in Robotic Arm Energy Consumption by Particle Swarm Optimization. Appl. Sci., 10.","DOI":"10.3390\/app10228241"},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"527","DOI":"10.1016\/j.promfg.2020.02.174","article-title":"Time-energy Optimal Trajectory Planning for Collaborative Welding Robot with Multiple Manipulators","volume":"43","author":"Liu","year":"2020","journal-title":"Procedia Manuf."},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Gao, R., Zhou, Q., Cao, S., and Jiang, Q. (2023). Apple-Picking Robot Picking Path Planning Algorithm Based on Improved PSO. Electronics, 12.","DOI":"10.3390\/electronics12081832"},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"Cao, X., Yan, H., Huang, Z., Ai, S., Xu, Y., Fu, R., and Zou, X. (2021). A Multi-Objective Particle Swarm Optimization for Trajectory Planning of Fruit Picking Manipulator. Agronomy, 11.","DOI":"10.3390\/agronomy11112286"},{"key":"ref_77","unstructured":"Sun, F., Zhang, J., Tan, Y., Cao, J., and Yu, W. (2008, January 24\u201328). Multi-Objective Optimal Trajectory Planning of Space Robot Using Particle Swarm Optimization. Proceedings of the Advances in Neural Networks\u2014ISNN 2008, Beijing, China."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"27","DOI":"10.5772\/5605","article-title":"The Cartesian Path Planning of Free-Floating Space Robot using Particle Swarm Optimization","volume":"5","author":"Xu","year":"2008","journal-title":"Int. J. Adv. Robot. Syst."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1016\/j.actaastro.2015.03.008","article-title":"Trajectory planning of free-floating space robot using Particle Swarm Optimization (PSO)","volume":"112","author":"Wang","year":"2015","journal-title":"Acta Astronaut."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1016\/j.actaastro.2018.03.012","article-title":"Coordinated trajectory planning of dual-arm space robot using constrained particle swarm optimization","volume":"146","author":"Wang","year":"2018","journal-title":"Acta Astronaut."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"379","DOI":"10.1016\/j.actaastro.2022.03.024","article-title":"Coordinated trajectory planning of a dual-arm space robot with multiple avoidance constraints","volume":"195","author":"Ni","year":"2022","journal-title":"Acta Astronaut."},{"key":"ref_82","doi-asserted-by":"crossref","unstructured":"Zhang, J., Zhang, J., Zhang, Q., and Wei, X. (2022). Obstacle Avoidance Path Planning of Space Robot Based on Improved Particle Swarm Optimization. Symmetry, 14.","DOI":"10.3390\/sym14050938"},{"key":"ref_83","doi-asserted-by":"crossref","unstructured":"Grefenstette, J.J. (1993, January 26\u201328). Genetic algorithms and machine learning. Proceedings of the Sixth Annual Conference on Computational Learning Theory, Santa Cruz, CA, USA.","DOI":"10.1145\/168304.168305"},{"key":"ref_84","unstructured":"Khoogar, A., and Parker, J. (1991, January 7\u201310). Obstacle avoidance of redundant manipulators using genetic algorithms. Proceedings of the IEEE Proceedings of the SOUTHEASTCON \u201991, Williamsburg, VA, USA."},{"key":"ref_85","unstructured":"Toogood, R., Hao, H., and Wong, C. (1995, January 22\u201325). Robot path planning using genetic algorithms. Proceedings of the 1995 IEEE International Conference on Systems, Man and Cybernetics, Intelligent Systems for the 21st Century, Vancouver, BC, Canada."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1016\/S0952-1976(02)00067-2","article-title":"Optimization techniques applied to multiple manipulators for path planning and torque minimization","volume":"15","author":"Garg","year":"2002","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1017\/S0263574701003861","article-title":"Point-to-Point trajectory planning of flexible redundant robot manipulators using genetic algorithms","volume":"20","author":"Yue","year":"2002","journal-title":"Robotica"},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"455","DOI":"10.1016\/j.mechatronics.2003.10.001","article-title":"An effective robot trajectory planning method using a genetic algorithm","volume":"14","author":"Tian","year":"2004","journal-title":"Mechatronics"},{"key":"ref_89","doi-asserted-by":"crossref","unstructured":"Pires, E., Tenreiro Machado, J., and Moura Oliveira, P. (2004, January 26\u201330). Robot Trajectory Planning Using Multi-objective Genetic Algorithm Optimization. Proceedings of the Genetic and Evolutionary Computation\u2014GECCO 2004, Seattle, WA, USA.","DOI":"10.1007\/978-3-540-24854-5_64"},{"key":"ref_90","unstructured":"Kazem, B.I., Mahdi, A.I., and Oudah, A.T. (2023, October 01). Motion Planning for a Robot Arm by Using Genetic Algorithm. Available online: https:\/\/api.semanticscholar.org\/CorpusID:957663."},{"key":"ref_91","doi-asserted-by":"crossref","unstructured":"Ren, T.R., Kwok, N.M., Liu, D.K., and Huang, S.D. (2008, January 20\u201323). Path planning for a robotic arm sand-blasting system. Proceedings of the 2008 International Conference on Information and Automation, Changsha, China.","DOI":"10.1109\/ICINFA.2008.4608157"},{"key":"ref_92","first-page":"315","article-title":"Optimization of energy in robotic arm using genetic algorithm","volume":"2","author":"Sharma","year":"2011","journal-title":"Int. J. Comput. Sci. Technol."},{"key":"ref_93","first-page":"50902","article-title":"Continuous Genetic Algorithms for Collision-Free Cartesian Path Planning of Robot Manipulators","volume":"8","author":"Alsmadi","year":"2011","journal-title":"Int. J. Adv. Robot. Syst."},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"1099","DOI":"10.1016\/j.asoc.2012.09.025","article-title":"Polynomial joint angle arm robot motion planning in complex geometrical obstacles","volume":"13","author":"Machmudah","year":"2013","journal-title":"Appl. Soft Comput."},{"key":"ref_95","doi-asserted-by":"crossref","unstructured":"Tsai, C.C., Hung, C.C., and Chang, C.F. (2014, January 6\u20138). Trajectory planning and control of a 7-DOF robotic manipulator. Proceedings of the 2014 International Conference on Advanced Robotics and Intelligent Systems (ARIS), Taipei, Taiwan.","DOI":"10.1109\/ARIS.2014.6871496"},{"key":"ref_96","unstructured":"\u0160tevo, S., Sekaj, I., and Dekan, M. (2014, January 24\u201329). Optimization of Robotic Arm Trajectory Using Genetic Algorithm. Proceedings of the IFAC World Congress 2014, Cape Town, South Africa."},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1016\/j.procs.2016.04.080","article-title":"Trajectory Path Planning of EEG Controlled Robotic Arm Using GA","volume":"84","author":"Roy","year":"2016","journal-title":"Procedia Comput. Sci."},{"key":"ref_98","doi-asserted-by":"crossref","unstructured":"Yang, M., Jiang, Y., and Sun, J. (August, January 31). Research on Trajectory Planning of Manipulator Based on GA\u2014APF Algorithm. Proceedings of the 2017 IEEE 7th Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems (CYBER), Honolulu, HI, USA.","DOI":"10.1109\/CYBER.2017.8446214"},{"key":"ref_99","doi-asserted-by":"crossref","first-page":"3702916","DOI":"10.1155\/2018\/3702916","article-title":"GA Based Adaptive Singularity-Robust Path Planning of Space Robot for On-Orbit Detection","volume":"2018","author":"Wu","year":"2018","journal-title":"Complexity"},{"key":"ref_100","doi-asserted-by":"crossref","first-page":"146301","DOI":"10.1109\/ACCESS.2019.2945824","article-title":"Online Time-Optimal Trajectory Planning for Robotic Manipulators Using Adaptive Elite Genetic Algorithm With Singularity Avoidance","volume":"7","author":"Liu","year":"2019","journal-title":"IEEE Access"},{"key":"ref_101","first-page":"1","article-title":"Path planning optimization of six-degree-of-freedom robotic manipulators using evolutionary algorithms","volume":"17","author":"Lorencin","year":"2020","journal-title":"Int. J. Adv. Robot. Syst."},{"key":"ref_102","doi-asserted-by":"crossref","first-page":"102053","DOI":"10.1016\/j.rcim.2020.102053","article-title":"Path planning of cooperating industrial robots using evolutionary algorithms","volume":"67","author":"Larsen","year":"2021","journal-title":"Robot. Comput.-Integr. Manuf."},{"key":"ref_103","doi-asserted-by":"crossref","first-page":"835","DOI":"10.1108\/IR-09-2021-0194","article-title":"Intelligent obstacle avoidance path planning method for picking manipulator combined with artificial potential field method","volume":"49","author":"Fang","year":"2022","journal-title":"Ind. Robot. Int. J. Robot. Res. Appl."},{"key":"ref_104","doi-asserted-by":"crossref","unstructured":"Nonoyama, K., Liu, Z., Fujiwara, T., Alam, M.M., and Nishi, T. (2022). Energy-Efficient Robot Configuration and Motion Planning Using Genetic Algorithm and Particle Swarm Optimization. Energies, 15.","DOI":"10.3390\/en15062074"},{"key":"ref_105","doi-asserted-by":"crossref","first-page":"707","DOI":"10.1007\/s41315-022-00240-4","article-title":"Trajectory optimisation in collaborative robotics based on simulations and genetic algorithms","volume":"6","author":"Zanchettin","year":"2022","journal-title":"Int. J. Intell. Robot. Appl."},{"key":"ref_106","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1109\/MCI.2006.329691","article-title":"Ant colony optimization","volume":"1","author":"Dorigo","year":"2006","journal-title":"IEEE Comput. Intell. Mag."},{"key":"ref_107","doi-asserted-by":"crossref","unstructured":"Gendreau, M., and Potvin, J.Y. (2019). Handbook of Metaheuristics, Springer International Publishing.","DOI":"10.1007\/978-3-319-91086-4"},{"key":"ref_108","doi-asserted-by":"crossref","unstructured":"Mohamad, M., Dunnigan, M., and Taylor, N. (2005, January 21\u201324). Ant Colony Robot Motion Planning. Proceedings of the EUROCON 2005\u2014The International Conference on \u201cComputer as a Tool\u201d, Belgrade, Serbia.","DOI":"10.1109\/EURCON.2005.1629898"},{"key":"ref_109","doi-asserted-by":"crossref","unstructured":"Mohamad, M., Taylor, N., and Dunnigan, M. (2006, January 4\u20136). Articulated Robot Motion Planning Using Ant Colony Optimisation. Proceedings of the 2006 3rd International IEEE Conference Intelligent Systems, London, UK.","DOI":"10.1109\/IS.2006.348503"},{"key":"ref_110","doi-asserted-by":"crossref","first-page":"560","DOI":"10.1016\/j.proeng.2017.02.434","article-title":"Optimization of Arm Manipulator Trajectory Planning in the Presence of Obstacles by Ant Colony Algorithm","volume":"181","author":"Baghli","year":"2017","journal-title":"Procedia Eng."},{"key":"ref_111","doi-asserted-by":"crossref","unstructured":"Wang, J., Guo, M., Li, L., Sun, S., and Gu, S. (2009, January 17\u201319). Collision-free path planning of Dual-arm robots based on improved ant colony algorithm. Proceedings of the 2009 Chinese Control and Decision Conference, Guilin, China.","DOI":"10.1109\/CCDC.2009.5192261"},{"key":"ref_112","doi-asserted-by":"crossref","unstructured":"Huadong, Z., Chaofan, L., and Nan, J. (2019, January 12\u201314). A Path Planning Method of Robot Arm Obstacle Avoidance Based on Dynamic Recursive Ant Colony Algorithm. Proceedings of the 2019 IEEE International Conference on Power, Intelligent Computing and Systems (ICPICS), Shenyang, China.","DOI":"10.1109\/ICPICS47731.2019.8942495"},{"key":"ref_113","first-page":"96","article-title":"Ant Colony Algorithm Improvement for Robot Arm Path Planning Optimization Based on D* Strategy","volume":"21","author":"Sadiq","year":"2021","journal-title":"Int. J. Mech. Mechatronics Eng."},{"key":"ref_114","doi-asserted-by":"crossref","unstructured":"Meng, X., and Zhu, X. (2022). Autonomous Obstacle Avoidance Path Planning for Grasping Manipulator Based on Elite Smoothing Ant Colony Algorithm. Symmetry, 14.","DOI":"10.3390\/sym14091843"},{"key":"ref_115","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1023\/A:1008202821328","article-title":"Differential Evolution\u2014A Simple and Efficient Heuristic for global Optimization over Continuous Spaces","volume":"11","author":"Storn","year":"1997","journal-title":"J. Glob. Optim."},{"key":"ref_116","unstructured":"Price, K. (1996, January 19\u201322). Differential evolution: A fast and simple numerical optimizer. Proceedings of the North American Fuzzy Information Processing, Berkeley, CA, USA."},{"key":"ref_117","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1007\/s10846-008-9202-0","article-title":"Evolutionary Minimum Cost Trajectory Planning for Industrial Robots","volume":"52","author":"Saravanan","year":"2008","journal-title":"J. Intell. Robot. Syst."},{"key":"ref_118","doi-asserted-by":"crossref","first-page":"190","DOI":"10.1007\/s11633-010-0190-8","article-title":"Evolutionary trajectory planning for an industrial robot","volume":"7","author":"Saravanan","year":"2010","journal-title":"Int. J. Autom. Comput."},{"key":"ref_119","doi-asserted-by":"crossref","unstructured":"Gonzalez, C., Blanco, D., and Moreno, L. (2009, January 18\u201321). Optimum robot manipulator path generation using Differential Evolution. Proceedings of the 2009 IEEE Congress on Evolutionary Computation, Trondheim, Norway.","DOI":"10.1109\/CEC.2009.4983366"},{"key":"ref_120","doi-asserted-by":"crossref","unstructured":"Das, S.D., Bain, V., and Rakshit, P. (2018, January 14\u201315). Energy Optimized Robot Arm Path Planning Using Differential Evolution in Dynamic Environment. Proceedings of the 2018 Second International Conference on Intelligent Computing and Control Systems (ICICCS), Madurai, India.","DOI":"10.1109\/ICCONS.2018.8663106"},{"key":"ref_121","doi-asserted-by":"crossref","first-page":"1525","DOI":"10.1016\/j.asr.2018.01.011","article-title":"Optimal trajectory planning of free-floating space manipulator using differential evolution algorithm","volume":"61","author":"Wang","year":"2018","journal-title":"Adv. Space Res."},{"key":"ref_122","doi-asserted-by":"crossref","first-page":"414","DOI":"10.1017\/S0263574722000224","article-title":"Geometrically constrained path planning for robotic grasping with Differential Evolution and Fast Marching Square","volume":"41","author":"Quevedo","year":"2023","journal-title":"Robotica"},{"key":"ref_123","unstructured":"Karaboga, D. (2005). An Idea Based on Honey Bee Swarm for Numerical Optimization, Erciyes University, Engineering Faculty, Computer Engineering Department. Technical Report, Technical Report-tr06."},{"key":"ref_124","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1007\/s10462-012-9328-0","article-title":"A comprehensive survey: Artificial bee colony (ABC) algorithm and applications","volume":"42","author":"Karaboga","year":"2014","journal-title":"Artif. Intell. Rev."},{"key":"ref_125","doi-asserted-by":"crossref","unstructured":"Jin, F., and Shu, G. (2012, January 29\u201331). Path planning of free-flying space robot based on artificial bee colony algorithm. Proceedings of the 2012 2nd International Conference on Computer Science and Network Technology, Changchun, China.","DOI":"10.1109\/ICCSNT.2012.6525987"},{"key":"ref_126","doi-asserted-by":"crossref","unstructured":"Savsani, P., Jhala, R., and Savsani, V. (2013, January 15\u201318). Optimized trajectory planning of a robotic arm using teaching learning based optimization (TLBO) and artificial bee colony (ABC) optimization techniques. Proceedings of the 2013 IEEE International Systems Conference (SysCon), Orlando, FL, USA.","DOI":"10.1109\/SysCon.2013.6549910"},{"key":"ref_127","doi-asserted-by":"crossref","unstructured":"Zhou, Z., Zhao, J., Zhang, Z., and Li, X. (2023, January 21\u201323). Motion Planning of Dual-Chain Manipulator Based on Artificial Bee Colony Algorithm. Proceedings of the 2023 9th International Conference on Control, Automation and Robotics (ICCAR), Beijing, China.","DOI":"10.1109\/ICCAR57134.2023.10151753"},{"key":"ref_128","doi-asserted-by":"crossref","first-page":"104976","DOI":"10.1016\/j.engappai.2022.104976","article-title":"Optimal scheduling for palletizing task using robotic arm and artificial bee colony algorithm","volume":"113","author":"Szczepanski","year":"2022","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_129","doi-asserted-by":"crossref","unstructured":"Chen, Z., Ma, L., and Shao, Z. (2019, January 22\u201324). Path Planning for Obstacle Avoidance of Manipulators Based on Improved Artificial Potential Field. Proceedings of the 2019 Chinese Automation Congress (CAC), Hangzhou, China.","DOI":"10.1109\/CAC48633.2019.8996467"},{"key":"ref_130","doi-asserted-by":"crossref","first-page":"3171","DOI":"10.1007\/s12206-021-0638-5","article-title":"Time-optimal trajectory planning of serial manipulator based on adaptive cuckoo search algorithm","volume":"35","author":"Zhang","year":"2021","journal-title":"J. Mech. Sci. Technol."},{"key":"ref_131","doi-asserted-by":"crossref","first-page":"138788","DOI":"10.1109\/ACCESS.2020.3012564","article-title":"Motion Planning of Redundant Manipulator With Variable Joint Velocity Limit Based on Beetle Antennae Search Algorithm","volume":"8","author":"Cheng","year":"2020","journal-title":"IEEE Access"},{"key":"ref_132","doi-asserted-by":"crossref","unstructured":"Zhang, X., and Ming, Z. (2019). Trajectory Planning and Optimization for a Par4 Parallel Robot Based on Energy Consumption. Appl. Sci., 9.","DOI":"10.3390\/app9132770"},{"key":"ref_133","first-page":"3424313","article-title":"Optimal Trajectory Planning of Grinding Robot Based on Improved Whale Optimization Algorithm","volume":"2020","author":"Wang","year":"2020","journal-title":"Math. Probl. Eng."},{"key":"ref_134","doi-asserted-by":"crossref","first-page":"2945","DOI":"10.1111\/itor.13176","article-title":"Exposing the grey wolf, moth-flame, whale, firefly, bat, and antlion algorithms: Six misleading optimization techniques inspired by bestial metaphors","volume":"30","author":"Dorigo","year":"2023","journal-title":"Int. Trans. Oper. Res."},{"key":"ref_135","doi-asserted-by":"crossref","unstructured":"Wichapong, K., Pholdee, N., Bureerat, S., and Radpukdee, T. (2018, January 21\u201323). Trajectory planning of a 6D robot based on Meta Heuristic algorithms. Proceedings of the MATEC Web of Conferences, EDP Sciences, Moscow, Russia.","DOI":"10.1051\/matecconf\/201822006004"},{"key":"ref_136","unstructured":"Hansen, N. (2016). The CMA Evolution Strategy: A Tutorial. arXiv."},{"key":"ref_137","doi-asserted-by":"crossref","unstructured":"Tanabe, R., and Fukunaga, A.S. (2014, January 6\u201311). Improving the search performance of SHADE using linear population size reduction. Proceedings of the 2014 IEEE Congress on Evolutionary Computation (CEC), Beijing, China.","DOI":"10.1109\/CEC.2014.6900380"},{"key":"ref_138","doi-asserted-by":"crossref","unstructured":"K\u016fdela, J., Ju\u0159\u00ed\u010dek, M., and Par\u00e1k, R. (2023, January 12\u201314). A Collection of Robotics Problems for Benchmarking Evolutionary Computation Methods. Proceedings of the International Conference on the Applications of Evolutionary Computation (Part of EvoStar), Brno, Czech Republic.","DOI":"10.1007\/978-3-031-30229-9_24"},{"key":"ref_139","doi-asserted-by":"crossref","unstructured":"Mohamed, A.W., Hadi, A.A., Mohamed, A.K., and Awad, N.H. (2020, January 19\u201324). Evaluating the performance of adaptive gainingsharing knowledge based algorithm on cec 2020 benchmark problems. Proceedings of the 2020 IEEE Congress on Evolutionary Computation (CEC), Glasgow, UK.","DOI":"10.1109\/CEC48606.2020.9185901"},{"key":"ref_140","doi-asserted-by":"crossref","first-page":"927","DOI":"10.1016\/j.swevo.2018.10.002","article-title":"Benchmarking evolutionary algorithms for single objective real-valued constrained optimization\u2013a critical review","volume":"44","author":"Hellwig","year":"2019","journal-title":"Swarm Evol. Comput."},{"key":"ref_141","doi-asserted-by":"crossref","unstructured":"Kudela, J., and Juricek, M. (2023, January 15\u201319). Computational and Exploratory Landscape Analysis of the GKLS Generator. Proceedings of the GECCO \u201923 Companion: Proceedings of the Companion Conference on Genetic and Evolutionary Computation, Lisbon, Portugal.","DOI":"10.1145\/3583133.3590653"},{"key":"ref_142","doi-asserted-by":"crossref","unstructured":"Brest, J., Mau\u010dec, M.S., and Bo\u0161kovi\u0107, B. (2017, January 5\u20138). Single objective real-parameter optimization: Algorithm jSO. Proceedings of the 2017 IEEE Congress on Evolutionary Computation (CEC), Donostia, Spain.","DOI":"10.1109\/CEC.2017.7969456"},{"key":"ref_143","doi-asserted-by":"crossref","first-page":"100490","DOI":"10.1016\/j.swevo.2019.01.006","article-title":"Comparison of nature-inspired population-based algorithms on continuous optimisation problems","volume":"50","author":"Bujok","year":"2019","journal-title":"Swarm Evol. Comput."},{"key":"ref_144","doi-asserted-by":"crossref","first-page":"8262","DOI":"10.1109\/ACCESS.2022.3144067","article-title":"New benchmark functions for single-objective optimization based on a zigzag pattern","volume":"10","author":"Kudela","year":"2022","journal-title":"IEEE Access"},{"key":"ref_145","doi-asserted-by":"crossref","unstructured":"Del Ser, J., Osaba, E., Martinez, A.D., Bilbao, M.N., Poyatos, J., Molina, D., and Herrera, F. (2021, January 5\u20137). More is not always better: Insights from a massive comparison of meta-heuristic algorithms over real-parameter optimization problems. Proceedings of the 2021 IEEE Symposium Series on Computational Intelligence (SSCI), Virtual.","DOI":"10.1109\/SSCI50451.2021.9660030"},{"key":"ref_146","doi-asserted-by":"crossref","first-page":"122373","DOI":"10.1016\/j.eswa.2023.122373","article-title":"Assessment of the performance of metaheuristic methods used for the inverse identification of effective heat capacity of phase change materials","volume":"238","author":"Kudela","year":"2023","journal-title":"Expert Syst. Appl."},{"key":"ref_147","doi-asserted-by":"crossref","unstructured":"Kudela, J. (2023). Chance-Constrained Optimization Formulation for Ship Conceptual Design: A Comparison of Metaheuristic Algorithms. Computers, 12.","DOI":"10.3390\/computers12110225"},{"key":"ref_148","doi-asserted-by":"crossref","first-page":"45","DOI":"10.13164\/mendel.2023.1.045","article-title":"Differential Evolution and Engineering Problems","volume":"29","author":"Bujok","year":"2023","journal-title":"MENDEL J."},{"key":"ref_149","doi-asserted-by":"crossref","first-page":"114","DOI":"10.1080\/10556788.2020.1808977","article-title":"COCO: A Platform for Comparing Continuous Optimizers in a Black-Box Setting","volume":"36","author":"Hansen","year":"2021","journal-title":"Optim. Methods Softw."},{"key":"ref_150","doi-asserted-by":"crossref","first-page":"325","DOI":"10.1177\/105971239700600205","article-title":"Evolutionary robotics and the radical envelope-of-noise hypothesis","volume":"6","author":"Jakobi","year":"1997","journal-title":"Adapt. Behav."},{"key":"ref_151","doi-asserted-by":"crossref","first-page":"122","DOI":"10.1109\/TEVC.2012.2185849","article-title":"The transferability approach: Crossing the reality gap in evolutionary robotics","volume":"17","author":"Koos","year":"2012","journal-title":"IEEE Trans. Evol. Comput."},{"key":"ref_152","doi-asserted-by":"crossref","first-page":"153171","DOI":"10.1109\/ACCESS.2021.3126658","article-title":"Crossing the reality gap: A survey on sim-to-real transferability of robot controllers in reinforcement learning","volume":"9","author":"Salvato","year":"2021","journal-title":"IEEE Access"},{"key":"ref_153","doi-asserted-by":"crossref","first-page":"442","DOI":"10.1109\/TEVC.2018.2869001","article-title":"Data-driven evolutionary optimization: An overview and case studies","volume":"23","author":"Jin","year":"2018","journal-title":"IEEE Trans. Evol. Comput."},{"key":"ref_154","doi-asserted-by":"crossref","first-page":"457","DOI":"10.1016\/j.ins.2022.11.045","article-title":"Combining Lipschitz and RBF surrogate models for high-dimensional computationally expensive problems","volume":"619","author":"Kudela","year":"2023","journal-title":"Inf. Sci."},{"key":"ref_155","doi-asserted-by":"crossref","unstructured":"Kononova, A.V., Vermetten, D., Caraffini, F., Mitran, M.A., and Zaharie, D. (2023). The Importance of Being Constrained: Dealing with Infeasible Solutions in Differential Evolution and Beyond. Evol. Comput., 1\u201346.","DOI":"10.1162\/evco_a_00333"},{"key":"ref_156","doi-asserted-by":"crossref","unstructured":"Corke, P., and Haviland, J. (5, January 30). Not your grandmother\u2019s toolbox\u2013the Robotics Toolbox reinvented for Python. Proceedings of the 2021 IEEE International Conference on Robotics and Automation (ICRA), Xi\u2019an, China.","DOI":"10.1109\/ICRA48506.2021.9561366"}],"container-title":["Computation"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2079-3197\/11\/12\/245\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T21:37:51Z","timestamp":1760132271000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2079-3197\/11\/12\/245"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,4]]},"references-count":156,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2023,12]]}},"alternative-id":["computation11120245"],"URL":"https:\/\/doi.org\/10.3390\/computation11120245","relation":{},"ISSN":["2079-3197"],"issn-type":[{"value":"2079-3197","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,12,4]]}}}