{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T06:21:30Z","timestamp":1770963690706,"version":"3.50.1"},"reference-count":41,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2026,2,6]],"date-time":"2026-02-06T00:00:00Z","timestamp":1770336000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Metaheuristic algorithms have become essential tools for solving complex, high-dimensional, and constrained optimization problems. This paper introduces an adaptive R implementation of the parameter-free Jaya algorithm, enhanced with methodological innovations for both single-objective and multi-objective settings. The proposed framework integrates adaptive population management, dynamic constraint-handling, diversity-preserving perturbations, and Pareto-based archiving, while retaining Jaya\u2019s parameter-free simplicity. These extensions are further supported by parallel computation and visualization tools, enabling scalable and reproducible applications. Benchmark evaluations on standard test functions demonstrate improved convergence accuracy, solution diversity, and robustness compared to the classical Jaya and other baseline algorithms. To highlight real-world applicability, the method is applied to a renewable energy planning problem, where trade-offs among cost, emissions, and reliability are explored. The results confirm that the adaptive Jaya approach can generate well-distributed Pareto fronts and provide practical decision support for energy system design. The main contributions of this work are threefold: (i) the development of an adaptive multi-objective extension of the Jaya algorithm that preserves its parameter-free philosophy while incorporating diversity preservation, dynamic constraint handling, and Pareto-based selection; (ii) a unified and openly available R implementation that integrates methodological advances with parallel computation and visualization, addressing the lack of transparent and reusable MO-Jaya tools in the existing literature; and (iii) a systematic evaluation on benchmark test functions and a renewable energy planning case study, demonstrating competitive convergence, robust Pareto diversity, and practical decision-making insights compared to established methods. By openly releasing the software in R (\u22653.5.0), this work contributes both a methodological advance in multi-objective metaheuristics and a transparent tool for applied optimization in engineering and environmental domains.<\/jats:p>","DOI":"10.3390\/a19020133","type":"journal-article","created":{"date-parts":[[2026,2,6]],"date-time":"2026-02-06T16:32:59Z","timestamp":1770395579000},"page":"133","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Adaptive Multi-Objective Jaya Algorithm with Applications in Renewable Energy System Optimization"],"prefix":"10.3390","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3493-9302","authenticated-orcid":false,"given":"Neeraj Dhanraj","family":"Bokde","sequence":"first","affiliation":[{"name":"Renewable and Sustainable Energy Research Center, Technology Innovation Institute, Abu Dhabi 9639, United Arab Emirates"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9512-5903","authenticated-orcid":false,"given":"Manish N.","family":"Kapse","sequence":"additional","affiliation":[{"name":"Department of Electronics and Tele-Communication Engineering, St. Vincent Pallotti College of Engineering and Technology, Nagpur 441108, Maharashtra, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kannaiyan","family":"Surender","sequence":"additional","affiliation":[{"name":"Department of Electronics and Communication Engineering, Visvesvaraya National Institute of Technology, Nagpur 440011, Maharashtra, India"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,2,6]]},"reference":[{"key":"ref_1","first-page":"19","article-title":"Jaya: A simple and new optimization algorithm for solving constrained and unconstrained optimization problems","volume":"7","author":"Rao","year":"2016","journal-title":"Int. J. Ind. Eng. Comput."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1109\/MCI.2006.1597059","article-title":"Evolutionary multi-objective optimization: A historical view of the field","volume":"1","author":"Coello","year":"2006","journal-title":"IEEE Comput. Intell. Mag."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Deb, K. (2011). Multi-objective optimisation using evolutionary algorithms: An introduction. Multi-Objective Evolutionary Optimisation for Product Design and Manufacturing, Springer.","DOI":"10.1007\/978-0-85729-652-8_1"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"4","DOI":"10.5334\/jors.188","article-title":"PyPSA: Python for Power System Analysis","volume":"6","author":"Brown","year":"2018","journal-title":"J. Open Res. Softw."},{"key":"ref_5","unstructured":"Bokde, N.D., and Fanara, C. (2025). The PyPSA Handbook: Integrated Power System Analysis and Renewable Energy Modeling, Elsevier."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"182","DOI":"10.1109\/4235.996017","article-title":"A fast and elitist multiobjective genetic algorithm: NSGA-II","volume":"6","author":"Deb","year":"2002","journal-title":"IEEE Trans. Evol. Comput."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1162\/106365600568167","article-title":"Approximating the nondominated front using the Pareto archived evolution strategy","volume":"8","author":"Knowles","year":"2000","journal-title":"Evol. Comput."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1162\/106365600568202","article-title":"Comparison of multiobjective evolutionary algorithms: Empirical results","volume":"8","author":"Zitzler","year":"2000","journal-title":"Evol. Comput."},{"key":"ref_9","first-page":"825","article-title":"Scalable multi-objective optimization test problems","volume":"Volume 1","author":"Deb","year":"2002","journal-title":"Proceedings of the 2002 Congress on Evolutionary Computation (CEC\u201902)"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"4329","DOI":"10.1007\/s10462-022-10234-0","article-title":"A comprehensive review on Jaya optimization algorithm","volume":"56","author":"Coelho","year":"2023","journal-title":"Artif. Intell. Rev."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Houssein, E.H., Gad, A.G., and Wazery, Y.M. (2020). Jaya algorithm and applications: A comprehensive review. Metaheuristics and Optimization in Computer and Electrical Engineering, Springer.","DOI":"10.1007\/978-3-030-56689-0_2"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"298","DOI":"10.1016\/j.jmsy.2021.05.018","article-title":"A hybrid Jaya algorithm for solving flexible job shop scheduling problem considering multiple critical paths","volume":"60","author":"Fan","year":"2021","journal-title":"J. Manuf. Syst."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"182078","DOI":"10.1109\/ACCESS.2019.2955683","article-title":"A novel hybrid fuzzy-JAYA optimization algorithm for efficient ORPD solution","volume":"7","author":"Gafar","year":"2019","journal-title":"IEEE Access"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"107654","DOI":"10.1016\/j.asoc.2021.107654","article-title":"Optimising the job-shop scheduling problem using a multi-objective Jaya algorithm","volume":"111","author":"He","year":"2021","journal-title":"Appl. Soft Comput."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"114567","DOI":"10.1016\/j.eswa.2021.114567","article-title":"A Pareto based discrete Jaya algorithm for multi-objective flexible job shop scheduling problem","volume":"170","author":"Caldeira","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"111924","DOI":"10.1016\/j.asoc.2024.111924","article-title":"A memory-guided Jaya algorithm to solve multi-objective optimal power flow integrating renewable energy sources","volume":"164","author":"Ahmadipour","year":"2024","journal-title":"Appl. Soft Comput."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Barakat, A., El-Sehiemy, R.A., Elsayd, M.I., and Osman, E. (2019, January 2\u20134). An enhanced Jaya optimization algorithm (EJOA) for solving multi-objective ORPD problem. Proceedings of the 2019 International Conference on Innovative Trends in Computer Engineering (ITCE), Aswan, Egypt.","DOI":"10.1109\/ITCE.2019.8646363"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"107555","DOI":"10.1016\/j.knosys.2021.107555","article-title":"Enhanced Jaya algorithm: A simple but efficient optimization method for constrained engineering design problems","volume":"233","author":"Zhang","year":"2021","journal-title":"Knowl.-Based Syst."},{"key":"ref_19","unstructured":"Bokde, N. (2025, February 06). Jaya: Gradient-Free Optimization Algorithm for Single and Multi-Objective Problems; R Package Version 1.0.3. Available online: https:\/\/CRAN.R-project.org\/package=Jaya."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"124","DOI":"10.1109\/4235.771166","article-title":"Parameter control in evolutionary algorithms","volume":"3","author":"Eiben","year":"2002","journal-title":"IEEE Trans. Evol. Comput."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Eiben, A.E., and Smit, S.K. (2012). Evolutionary algorithm parameters and methods to tune them. Autonomous Search, Springer.","DOI":"10.1007\/978-3-642-21434-9_2"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Lobo, F.G., and Lima, C.F. (2007). Adaptive population sizing schemes in genetic algorithms. Parameter Setting in Evolutionary Algorithms, Springer.","DOI":"10.1007\/978-3-540-69432-8_9"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"263","DOI":"10.1162\/106365602760234108","article-title":"Combining convergence and diversity in evolutionary multiobjective optimization","volume":"10","author":"Laumanns","year":"2002","journal-title":"Evol. Comput."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"27","DOI":"10.32614\/RJ-2011-005","article-title":"Differential evolution with DEoptim: An application to non-convex portfolio optimization","volume":"3","author":"Ardia","year":"2011","journal-title":"R J."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1","DOI":"10.18637\/jss.v040.i06","article-title":"DEoptim: An R package for global optimization by differential evolution","volume":"40","author":"Mullen","year":"2011","journal-title":"J. Stat. Softw."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"187","DOI":"10.32614\/RJ-2017-008","article-title":"On Some Extensions to GA Package: Hybrid Optimisation, Parallelisation and Islands Evolution","volume":"9","author":"Scrucca","year":"2017","journal-title":"R J."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1","DOI":"10.18637\/jss.v053.i04","article-title":"GA: A package for genetic algorithms in R","volume":"53","author":"Scrucca","year":"2013","journal-title":"J. Stat. Softw."},{"key":"ref_28","unstructured":"R Core Team (2024). R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing."},{"key":"ref_29","unstructured":"Bendtsen, C. (2022). pso: Particle Swarm Optimization; R package version 1.0.4, Comprehensive R Archive Network (CRAN). Available online: https:\/\/CRAN.R-project.org\/package=pso."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Yang, X.S. (2010). Engineering Optimization: An Introduction with Metaheuristic Applications, John Wiley & Sons.","DOI":"10.1002\/9780470640425"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1093\/comjnl\/3.3.175","article-title":"An automatic method for finding the greatest or least value of a function","volume":"3","author":"Rosenbrock","year":"1960","journal-title":"Comput. J."},{"key":"ref_32","unstructured":"Ackley, D. (2012). A Connectionist Machine for Genetic Hillclimbing, Springer Science & Business Media."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1007\/BF00933356","article-title":"Generalized descent for global optimization","volume":"34","author":"Griewank","year":"1981","journal-title":"J. Optim. Theory Appl."},{"key":"ref_34","unstructured":"Bokde, N.D. (2025, July 24). Jaya Case Studies and Comparisons. Available online: https:\/\/github.com\/neerajdhanraj\/Jaya_Case_Studies."},{"key":"ref_35","unstructured":"de JONG Kenneth, A. (1975). Analysis of the Behavior of a Class of Genetic Adaptive Systems, Department of Computer and Communication Sciences, University of Michigan. Technical Report No. 185."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"524","DOI":"10.1016\/j.energy.2008.04.003","article-title":"Energy system analysis of 100% renewable energy systems\u2014The case of Denmark in years 2030 and 2050","volume":"34","author":"Lund","year":"2009","journal-title":"Energy"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1059","DOI":"10.1016\/j.apenergy.2009.09.026","article-title":"A review of computer tools for analysing the integration of renewable energy into various energy systems","volume":"87","author":"Connolly","year":"2010","journal-title":"Appl. Energy"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1016\/j.rser.2014.02.003","article-title":"Energy systems modeling for twenty-first century energy challenges","volume":"33","author":"Pfenninger","year":"2014","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"440","DOI":"10.1016\/j.rser.2018.08.002","article-title":"A review of modelling tools for energy and electricity systems with large shares of variable renewables","volume":"96","author":"Haugan","year":"2018","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Bokde, N.D. (2026). Power-to-Hydrogen Systems: Stochastic Optimization for Renewable Energy Integration. National Academy Science Letters, Springer.","DOI":"10.1007\/s40009-025-01915-9"},{"key":"ref_41","unstructured":"Bokde, N.D., Pedersen, T.T., and Andresen, G.B. (2021). Optimal scheduling of flexible power-to-x technologies in the day-ahead electricity market. arXiv."}],"container-title":["Algorithms"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-4893\/19\/2\/133\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T05:35:18Z","timestamp":1770960918000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-4893\/19\/2\/133"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2,6]]},"references-count":41,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2026,2]]}},"alternative-id":["a19020133"],"URL":"https:\/\/doi.org\/10.3390\/a19020133","relation":{},"ISSN":["1999-4893"],"issn-type":[{"value":"1999-4893","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2,6]]}}}