{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T08:32:23Z","timestamp":1758270743397,"version":"3.37.3"},"reference-count":53,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2023,3,16]],"date-time":"2023-03-16T00:00:00Z","timestamp":1678924800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,3,16]],"date-time":"2023-03-16T00:00:00Z","timestamp":1678924800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61922072","61876169","61976237","62176238"],"award-info":[{"award-number":["61922072","61876169","61976237","62176238"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62106230"],"award-info":[{"award-number":["62106230"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"crossref","award":["2021T140616","2021M692920"],"award-info":[{"award-number":["2021T140616","2021M692920"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100006407","name":"Natural Science Foundation of Henan Province","doi-asserted-by":"publisher","award":["222300420088"],"award-info":[{"award-number":["222300420088"]}],"id":[{"id":"10.13039\/501100006407","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Program for Science & Technology Innovation Talents in Universities of Henan Province","award":["23HASTIT023"],"award-info":[{"award-number":["23HASTIT023"]}]},{"name":"Program for Science & Technology Innovation Teams in Universities of Henan Province","award":["23IRTSTHN010"],"award-info":[{"award-number":["23IRTSTHN010"]}]},{"name":"Key R &D and Promotion Projects in Henan Province","award":["212102210510","212102310083"],"award-info":[{"award-number":["212102210510","212102310083"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Complex Intell. Syst."],"published-print":{"date-parts":[[2023,10]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Fitness landscape analysis devotes to characterizing different properties of optimization problems, such as evolvability, sharpness, and neutrality. Although several landscape features have been proposed, only a few of them can be used in practice as predictors of algorithm performance. In this study, the keenness (<jats:inline-formula><jats:alternatives><jats:tex-math>$$\\textrm{KEE}_{s}$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                  <mml:msub>\n                    <mml:mtext>KEE<\/mml:mtext>\n                    <mml:mi>s<\/mml:mi>\n                  <\/mml:msub>\n                <\/mml:math><\/jats:alternatives><\/jats:inline-formula>) is proposed to characterize the sharpness of the fitness landscape for continuous optimization problems and predict the performance of the differential evolution algorithm. Specifically, a mirror simple random walk algorithm is designed to construct the relevance between the front and back search points in the sampling. The fitness value of each point is replaced by the specific integer. The values in the set of integers with the same circumstance are computed as the feature scalar using the cumulative calculation mechanism. The results of experimental studies in various functions demonstrate the superiority of <jats:inline-formula><jats:alternatives><jats:tex-math>$$\\textrm{KEE}_{s}$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                  <mml:msub>\n                    <mml:mtext>KEE<\/mml:mtext>\n                    <mml:mi>s<\/mml:mi>\n                  <\/mml:msub>\n                <\/mml:math><\/jats:alternatives><\/jats:inline-formula> in terms of accuracy, reliability, and coverage of samples. Moreover, <jats:inline-formula><jats:alternatives><jats:tex-math>$$\\textrm{KEE}_{s}$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                  <mml:msub>\n                    <mml:mtext>KEE<\/mml:mtext>\n                    <mml:mi>s<\/mml:mi>\n                  <\/mml:msub>\n                <\/mml:math><\/jats:alternatives><\/jats:inline-formula> has shown excellent practicability in the application of differential evolution algorithm performance prediction for continuous optimization problems. Thus, <jats:inline-formula><jats:alternatives><jats:tex-math>$$\\textrm{KEE}_{s}$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                  <mml:msub>\n                    <mml:mtext>KEE<\/mml:mtext>\n                    <mml:mi>s<\/mml:mi>\n                  <\/mml:msub>\n                <\/mml:math><\/jats:alternatives><\/jats:inline-formula> is a new landscape feature for fitness landscape analysis of continuous optimization problems and algorithm performance prediction within limited prior knowledge of the unknown problem.<\/jats:p>","DOI":"10.1007\/s40747-023-01005-7","type":"journal-article","created":{"date-parts":[[2023,3,16]],"date-time":"2023-03-16T09:03:43Z","timestamp":1678957423000},"page":"5251-5266","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Keenness for characterizing continuous optimization problems and predicting differential evolution algorithm performance"],"prefix":"10.1007","volume":"9","author":[{"given":"Yaxin","family":"Li","sequence":"first","affiliation":[]},{"given":"Jing","family":"Liang","sequence":"additional","affiliation":[]},{"given":"Kunjie","family":"Yu","sequence":"additional","affiliation":[]},{"given":"Caitong","family":"Yue","sequence":"additional","affiliation":[]},{"given":"Yingjie","family":"Zhang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,3,16]]},"reference":[{"key":"1005_CR1","doi-asserted-by":"publisher","first-page":"546","DOI":"10.1016\/j.swevo.2018.06.010","volume":"44","author":"KR Opara","year":"2019","unstructured":"Opara KR, Arabas J (2019) Differential evolution: a survey of theoretical analyses. Swarm Evolut Comput 44:546\u2013558","journal-title":"Swarm Evolut Comput"},{"key":"1005_CR2","doi-asserted-by":"publisher","first-page":"2347","DOI":"10.1007\/s40747-021-00421-x","volume":"7","author":"H Yu","year":"2021","unstructured":"Yu H, Kang L, Tan Y, Zeng J, Sun C (2021) A multi-model assisted differential evolution algorithm for computationally expensive optimization problems. Complex Intell Syst 7:2347\u20132371","journal-title":"Complex Intell Syst"},{"key":"1005_CR3","doi-asserted-by":"publisher","first-page":"2051","DOI":"10.1007\/s40747-022-00734-5","volume":"8","author":"J Li","year":"2022","unstructured":"Li J, Gao Y, Zhang H, Yang Q (2022) Self-adaptive opposition-based differential evolution with subpopulation strategy for numerical and engineering optimization problems. Complex Intell Syst 8:2051\u20132089","journal-title":"Complex Intell Syst"},{"key":"1005_CR4","doi-asserted-by":"crossref","unstructured":"Liang J, Li Y, Qu B, Yu K, Hu Y (2019) Mutation strategy selection based on fitness landscape analysis: A preliminary study. In: International Conference on Bio-Inspired Computing: Theories and Applications, pp. 284\u2013298","DOI":"10.1007\/978-981-15-3425-6_23"},{"key":"1005_CR5","doi-asserted-by":"crossref","unstructured":"Li Y, Liang J, Yu K, Chen K, Guo Y, Yue C, Zhang L (2022) Adaptive local landscape feature vector for problem classification and algorithm selection. Appl Soft Comput: 109751","DOI":"10.1016\/j.asoc.2022.109751"},{"key":"1005_CR6","doi-asserted-by":"crossref","unstructured":"Malan KM et\u00a0al. (2014) Characterising continuous optimisation problems for particle swarm optimisation performance prediction, Ph.D. thesis, University of Pretoria","DOI":"10.1007\/s11721-014-0099-x"},{"key":"1005_CR7","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1109\/TAI.2020.3022339","volume":"1","author":"Y Tian","year":"2020","unstructured":"Tian Y, Peng S, Zhang X, Rodemann T, Tan KC, Jin Y (2020) A recommender system for metaheuristic algorithms for continuous optimization based on deep recurrent neural networks. IEEE Trans Artif Intell 1:5\u201318","journal-title":"IEEE Trans Artif Intell"},{"key":"1005_CR8","doi-asserted-by":"publisher","first-page":"148","DOI":"10.1016\/j.ins.2013.04.015","volume":"241","author":"KM Malan","year":"2013","unstructured":"Malan KM, Engelbrecht AP (2013) A survey of techniques for characterising fitness landscapes and some possible ways forward. Inform Sci 241:148\u2013163","journal-title":"Inform Sci"},{"key":"1005_CR9","doi-asserted-by":"publisher","first-page":"40","DOI":"10.3390\/a14020040","volume":"14","author":"KM Malan","year":"2021","unstructured":"Malan KM (2021) A survey of advances in landscape analysis for optimisation. Algorithms 14:40","journal-title":"Algorithms"},{"key":"1005_CR10","doi-asserted-by":"crossref","unstructured":"Malan KM, Engelbrecht AP (2013) Steep gradients as a predictor of pso failure. In: Proceedings of the 15th Annual Conference Companion on Genetic and Evolutionary Computation, pp. 9\u201310","DOI":"10.1145\/2464576.2464582"},{"key":"1005_CR11","doi-asserted-by":"publisher","first-page":"561","DOI":"10.1109\/TEVC.2006.886448","volume":"11","author":"WB Langdon","year":"2007","unstructured":"Langdon WB, Poli R (2007) Evolving problems to learn about particle swarm optimizers and other search algorithms. IEEE Trans Evolut Comput 11:561\u2013578","journal-title":"IEEE Trans Evolut Comput"},{"key":"1005_CR12","doi-asserted-by":"crossref","unstructured":"Malan KM, Engelbrecht AP (2014) Particle swarm optimisation failure prediction based on fitness landscape characteristics. In: Proceedings of the IEEE Symposium on Swarm Intelligence, pp. 1\u20139","DOI":"10.1109\/SIS.2014.7011789"},{"key":"1005_CR13","doi-asserted-by":"publisher","first-page":"1063","DOI":"10.1109\/TEVC.2019.2940828","volume":"24","author":"A Liefooghe","year":"2019","unstructured":"Liefooghe A, Daolio F, Verel S, Derbel B, Aguirre H, Tanaka K (2019) Landscape-aware performance prediction for evolutionary multiobjective optimization. IEEE Trans Evolut Comput 24:1063\u20131077","journal-title":"IEEE Trans Evolut Comput"},{"key":"1005_CR14","doi-asserted-by":"crossref","unstructured":"Ventresca M, Ombuki-Berman B, Runka A (2013) Predicting genetic algorithm performance on the vehicle routing problem using information theoretic landscape measures. In: European Conference on Evolutionary Computation in Combinatorial Optimization, pp. 214\u2013225","DOI":"10.1007\/978-3-642-37198-1_19"},{"key":"1005_CR15","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1162\/1063656041774956","volume":"12","author":"P Merz","year":"2004","unstructured":"Merz P (2004) Advanced fitness landscape analysis and the performance of memetic algorithms. Evolut Comput 12:303\u2013325","journal-title":"Evolut Comput"},{"key":"1005_CR16","doi-asserted-by":"publisher","first-page":"337","DOI":"10.1109\/4235.887234","volume":"4","author":"P Merz","year":"2000","unstructured":"Merz P, Freisleben B (2000) Fitness landscape analysis and memetic algorithms for the quadratic assignment problem. IEEE Trans Evolut Comput 4:337\u2013352","journal-title":"IEEE Trans Evolut Comput"},{"key":"1005_CR17","doi-asserted-by":"crossref","unstructured":"Jankovic A, Doerr C (2020) Landscape-aware fixed-budget performance regression and algorithm selection for modular cma-es variants. In: Proceedings of the Genetic and Evolutionary Computation Conference, pp. 841\u2013849","DOI":"10.1145\/3377930.3390183"},{"key":"1005_CR18","doi-asserted-by":"crossref","unstructured":"Mu\u00f1oz MA, Kirley M, Halgamuge SK (2012) A meta-learning prediction model of algorithm performance for continuous optimization problems. In: International Conference on Parallel Problem Solving from Nature, pp. 226\u2013235","DOI":"10.1007\/978-3-642-32937-1_23"},{"key":"1005_CR19","doi-asserted-by":"crossref","unstructured":"Yang S, Li K, Li W, Chen W, Chen Y (2016) Dynamic fitness landscape analysis on differential evolution algorithm. In: International Conference on Bio-Inspired Computing: Theories and Applications, pp. 179\u2013184","DOI":"10.1007\/978-981-10-3614-9_23"},{"key":"1005_CR20","doi-asserted-by":"crossref","unstructured":"Zhang Z, Duan N, Zou K, Sun Z (2018) Predictive models of problem difficulties for differential evolutionary algorithm based on fitness landscape analysis. In: Proceedings of the 37th Chinese Control Conference, pp. 3221\u20133226","DOI":"10.23919\/ChiCC.2018.8483931"},{"key":"1005_CR21","doi-asserted-by":"publisher","first-page":"192","DOI":"10.1016\/j.asoc.2016.11.041","volume":"51","author":"H Lu","year":"2017","unstructured":"Lu H, Shi J, Fei Z, Zhou Q, Mao K (2017) Measures in the time and frequency domains for fitness landscape analysis of dynamic optimization problems. Appl Soft Comput 51:192\u2013208","journal-title":"Appl Soft Comput"},{"key":"1005_CR22","doi-asserted-by":"publisher","first-page":"809","DOI":"10.1016\/j.ejor.2018.01.051","volume":"270","author":"H Lu","year":"2018","unstructured":"Lu H, Shi J, Fei Z, Zhou Q, Mao K (2018) Analysis of the similarities and differences of job-based scheduling problems. Eur J Oper Res 270:809\u2013825","journal-title":"Eur J Oper Res"},{"key":"1005_CR23","doi-asserted-by":"publisher","first-page":"835","DOI":"10.1007\/s40747-021-00380-3","volume":"8","author":"Y Sun","year":"2022","unstructured":"Sun Y, Li Y, Yang Y, Yue H (2022) Differential evolution algorithm with population knowledge fusion strategy for image registration. Complex Intell Syst 8:835\u2013850","journal-title":"Complex Intell Syst"},{"key":"1005_CR24","doi-asserted-by":"publisher","first-page":"299","DOI":"10.1007\/s40747-020-00149-0","volume":"6","author":"Y Yang","year":"2020","unstructured":"Yang Y, Duan Z (2020) An effective co-evolutionary algorithm based on artificial bee colony and differential evolution for time series predicting optimization. Complex Intell Syst 6:299\u2013308","journal-title":"Complex Intell Syst"},{"key":"1005_CR25","doi-asserted-by":"publisher","first-page":"945","DOI":"10.1109\/TEVC.2009.2014613","volume":"13","author":"J Zhang","year":"2009","unstructured":"Zhang J, Sanderson AC (2009) Jade: adaptive differential evolution with optional external archive. IEEE Trans Evolut Comput 13:945\u2013958","journal-title":"IEEE Trans Evolut Comput"},{"key":"1005_CR26","doi-asserted-by":"crossref","unstructured":"Tanabe R, Fukunaga A (2013) Success-history based parameter adaptation for differential evolution. In: Proceedings of the IEEE Congress on Evolutionary Computation, pp. 71\u201378","DOI":"10.1109\/CEC.2013.6557555"},{"key":"1005_CR27","doi-asserted-by":"crossref","unstructured":"Tanabe R, Fukunaga AS (2014) Improving the search performance of shade using linear population size reduction. In: Proceedings of the IEEE Congress on Evolutionary Computation, pp. 1658\u20131665","DOI":"10.1109\/CEC.2014.6900380"},{"key":"1005_CR28","doi-asserted-by":"crossref","unstructured":"Qiao K, Liang J, Yu K, Qu B, Yue C, Xu R (2020) Parameter extraction of the photovoltaic model via an improved composite differential evolution. In: Proceedings of the Chinese Automation Congress, pp. 4868\u20134873","DOI":"10.1109\/CAC51589.2020.9326878"},{"key":"1005_CR29","doi-asserted-by":"crossref","unstructured":"Awad NH, Ali MZ, Suganthan PN, Reynolds RG (2016) An ensemble sinusoidal parameter adaptation incorporated with l-shade for solving cec2014 benchmark problems. In: Proceedings of the IEEE Congress on Evolutionary Computation pp. 2958\u20132965","DOI":"10.1109\/CEC.2016.7744163"},{"key":"1005_CR30","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2020.106693","volume":"96","author":"Y Huang","year":"2020","unstructured":"Huang Y, Li W, Tian F, Meng X (2020) A fitness landscape ruggedness multiobjective differential evolution algorithm with a reinforcement learning strategy. Appl Soft Comput 96:106693","journal-title":"Appl Soft Comput"},{"key":"1005_CR31","doi-asserted-by":"publisher","first-page":"129","DOI":"10.1016\/j.neucom.2022.06.084","volume":"503","author":"F Zou","year":"2022","unstructured":"Zou F, Chen D, Liu H, Cao S, Ji X, Zhang Y (2022) A survey of fitness landscape analysis for optimization. Neurocomputing 503:129\u2013139","journal-title":"Neurocomputing"},{"key":"1005_CR32","doi-asserted-by":"crossref","unstructured":"Li Y, Yu K, Liang J, Yue C, Qiao K (2022) A landscape-aware particle swarm optimization for parameter identification of photovoltaic models. Appl Soft Comput: 109793","DOI":"10.1016\/j.asoc.2022.109793"},{"key":"1005_CR33","first-page":"171","volume":"16","author":"D Mokeddem","year":"2021","unstructured":"Mokeddem D (2021) Parameter extraction of solar photovoltaic models using enhanced levy flight based grasshopper optimization algorithm, Journal of Electrical. Eng Technol 16:171\u2013179","journal-title":"Eng Technol"},{"key":"1005_CR34","doi-asserted-by":"publisher","first-page":"315","DOI":"10.1007\/s40747-019-0096-1","volume":"5","author":"Z Zandi","year":"2019","unstructured":"Zandi Z, Mazinan A (2019) Maximum power point tracking of the solar power plants in shadow mode through artificial neural network. Complex Intell Syst 5:315\u2013330","journal-title":"Complex Intell Syst"},{"key":"1005_CR35","doi-asserted-by":"crossref","unstructured":"Lang RD, Engelbrecht AP (2020) Decision space coverage of random walks, in: Proceedings of the IEEE Congress on Evolutionary Computation, pp. 1\u20138","DOI":"10.1109\/CEC48606.2020.9185623"},{"key":"1005_CR36","doi-asserted-by":"publisher","first-page":"921","DOI":"10.1007\/s00500-016-2397-2","volume":"22","author":"ND Jana","year":"2018","unstructured":"Jana ND, Sil J, Das S (2018) Continuous fitness landscape analysis using a chaos-based random walk algorithm. Soft Comput 22:921\u2013948","journal-title":"Soft Comput"},{"key":"1005_CR37","first-page":"184","volume":"95","author":"T Jones","year":"1995","unstructured":"Jones T, Forrest S et al (1995) Fitness distance correlation as a measure of problem difficulty for genetic algorithms. ICGA 95:184\u2013192","journal-title":"ICGA"},{"key":"1005_CR38","doi-asserted-by":"crossref","unstructured":"Borenstein Y, Poli R (2005) Information landscapes. In: Proceedings of the Genetic and Evolutionary Computation, pp. 1515\u20131522","DOI":"10.1145\/1068009.1068248"},{"key":"1005_CR39","doi-asserted-by":"crossref","unstructured":"Lunacek M, Whitley D (2006) The dispersion metric and the cma evolution strategy, In: Proceedings of the Genetic and Evolutionary Computation, pp. 477\u2013484","DOI":"10.1145\/1143997.1144085"},{"key":"1005_CR40","doi-asserted-by":"crossref","unstructured":"Malan KM, Engelbrecht AP (2014) A progressive random walk algorithm for sampling continuous fitness landscapes. In: Proceedings of the IEEE Congress on Evolutionary Computation, pp. 2507\u20132514","DOI":"10.1109\/CEC.2014.6900576"},{"key":"1005_CR41","unstructured":"De Jong KA (1975) An analysis of the behavior of a class of genetic adaptive systems. University of Michigan"},{"key":"1005_CR42","doi-asserted-by":"crossref","unstructured":"Mishra SK (2006) Performance of repulsive particle swarm method in global optimization of some important test functions: a fortran program, Available at SSRN 924339","DOI":"10.2139\/ssrn.924339"},{"key":"1005_CR43","doi-asserted-by":"crossref","unstructured":"Yao X, Liu Y, Lin G (1999) Evolutionary programming made faster. IEEE Transa Evolut Comput 3:82\u2013102","DOI":"10.1109\/4235.771163"},{"key":"1005_CR44","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1162\/evco.2006.14.1.119","volume":"14","author":"Y-W Shang","year":"2006","unstructured":"Shang Y-W, Qiu Y-H (2006) A note on the extended rosenbrock function. Evolut Comput 14:119\u2013126","journal-title":"Evolut Comput"},{"key":"1005_CR45","doi-asserted-by":"crossref","unstructured":"Woolson R (2007) Wilcoxon signed-rank test, Wiley Encyclopedia of Clinical Trials 1\u20133","DOI":"10.1002\/9780471462422.eoct979"},{"key":"1005_CR46","unstructured":"Jones T et\u00a0al. (1995) Evolutionary algorithms, fitness landscapes and search, Ph.D. thesis, University of New Mexico Albuquerque, NM"},{"key":"1005_CR47","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1109\/TEVC.2010.2059031","volume":"15","author":"S Das","year":"2010","unstructured":"Das S, Suganthan PN (2010) Differential evolution: a survey of the state-of-the-art. IEEE Trans Evolut Comput 15:4\u201331","journal-title":"IEEE Trans Evolut Comput"},{"key":"1005_CR48","doi-asserted-by":"publisher","first-page":"221","DOI":"10.1007\/s40747-019-0113-4","volume":"6","author":"CA Coello\u00a0Coello","year":"2020","unstructured":"Coello\u00a0Coello CA, Gonz\u00e1lez\u00a0Brambila S, Figueroa\u00a0Gamboa J, Castillo\u00a0Tapia MG, Hern\u00e1ndez\u00a0G\u00f3mez R (2020) Evolutionary multiobjective optimization: open research areas and some challenges lying ahead. Complex Intell Syst 6:221\u2013236","journal-title":"Complex Intell Syst"},{"key":"1005_CR49","volume-title":"Weka manual for version 3-8-5","author":"RR Bouckaert","year":"2020","unstructured":"Bouckaert RR, Frank E, Hall M, Kirkby R, Reutemann P, Seewald A, Scuse D (2020) Weka manual for version 3-8-5. University of Waikato, Hamilton"},{"key":"1005_CR50","doi-asserted-by":"crossref","unstructured":"Malan KM, Engelbrecht AP (2013) Ruggedness, funnels and gradients in fitness landscapes and the effect on pso performance. In: Proceedings of the IEEE Congress on Evolutionary Computation, pp. 963\u2013970","DOI":"10.1109\/CEC.2013.6557671"},{"key":"1005_CR51","doi-asserted-by":"publisher","first-page":"578","DOI":"10.1080\/01621459.1972.10481251","volume":"67","author":"JH Zar","year":"1972","unstructured":"Zar JH (1972) Significance testing of the spearman rank correlation coefficient. J Am Stat Assoc 67:578\u2013580","journal-title":"J Am Stat Assoc"},{"key":"1005_CR52","doi-asserted-by":"publisher","first-page":"2543","DOI":"10.1007\/s40747-021-00433-7","volume":"7","author":"Y Su","year":"2021","unstructured":"Su Y, Liu J, Xiang X, Zhang X (2021) A responsive ant colony optimization for large-scale dynamic vehicle routing problems via pheromone diversity enhancement. Complex Intell Syst 7:2543\u20132558","journal-title":"Complex Intell Syst"},{"key":"1005_CR53","doi-asserted-by":"crossref","unstructured":"Zhong J, Feng Y, Tang S, Xiong J, Dai X, Zhang N (2022) A collaborative neurodynamic optimization algorithm to traveling salesman problem. Complex Intell Syst: 1\u201313","DOI":"10.1007\/s40747-022-00884-6"}],"container-title":["Complex &amp; Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40747-023-01005-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s40747-023-01005-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40747-023-01005-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,22]],"date-time":"2023-09-22T17:18:39Z","timestamp":1695403119000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s40747-023-01005-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,3,16]]},"references-count":53,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2023,10]]}},"alternative-id":["1005"],"URL":"https:\/\/doi.org\/10.1007\/s40747-023-01005-7","relation":{},"ISSN":["2199-4536","2198-6053"],"issn-type":[{"type":"print","value":"2199-4536"},{"type":"electronic","value":"2198-6053"}],"subject":[],"published":{"date-parts":[[2023,3,16]]},"assertion":[{"value":"3 December 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 February 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 March 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}