{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,20]],"date-time":"2026-01-20T03:33:55Z","timestamp":1768880035447,"version":"3.49.0"},"reference-count":32,"publisher":"Fuji Technology Press Ltd.","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JACIII","J. Adv. Comput. Intell. Intell. Inform."],"published-print":{"date-parts":[[2026,1,20]]},"abstract":"<jats:p>In the real world, multiobjective optimization problems require the efficient acquisition of diverse solutions. Various multiobjective evolutionary algorithms (MOEAs) have been developed to address these problems. Typically, MOEAs use the same scoring criteria for both survival and mating selection, despite their different roles. Survival selection should ensure convergence and diversity, whereas mating selection should focus on selecting individuals with higher convergence for crossover. In this article, an efficient selection algorithm is proposed that integrates data envelopment analysis (DEA), Pareto front modeling, and a reference crossover mechanism. In survival selection, algorithms are used to ensure high convergence and diversity. In a previous study, DEA was employed to select individuals with higher convergence in mating selection. This approach balances convergence and diversity. In addition, Pareto front modeling addresses the convexity assumption issue in DEA. In this study, by selecting constraint solutions obtained through DEA as crossover targets, the algorithm makes crossover with superior solutions possible, enhancing optimization speed and diversity. The algorithm is particularly effective for benchmark functions that benefit from neighborhood crossover. In comparisons using the hypervolume metric on the WFG and DTLZ benchmark functions, the proposed algorithm outperformed NSGA-II, NSGA-III, AGE-MOEA-II, DEA-GA, MOEA\/D, and other previous algorithms. The results of a Wilcoxon rank-sum test also showed that the proposed algorithm is statistically superior.<\/jats:p>","DOI":"10.20965\/jaciii.2026.p0046","type":"journal-article","created":{"date-parts":[[2026,1,19]],"date-time":"2026-01-19T15:02:06Z","timestamp":1768834926000},"page":"46-66","source":"Crossref","is-referenced-by-count":0,"title":["Efficient Tournament Selection Using Data Envelopment Analysis in Multiobjective Genetic Algorithms with Pareto Front Modeling and Reference Pairing"],"prefix":"10.20965","volume":"30","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-9869-7668","authenticated-orcid":true,"given":"Mamoru","family":"Doi","sequence":"first","affiliation":[{"name":"Mitsubishi Electric Corporation, 5-1-1 Ofuna, Kamakura, Kanagawa 247-8501, Japan"}]},{"given":"Kenya","family":"Sugihara","sequence":"additional","affiliation":[{"name":"Mitsubishi Electric Corporation, 5-1-1 Ofuna, Kamakura, Kanagawa 247-8501, Japan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2729-8666","authenticated-orcid":true,"given":"Masao","family":"Arakawa","sequence":"additional","affiliation":[{"name":"Waseda University, 2-7 Hibikino, Wakamatsu-ku, Kitakyushu, Fukuoka 808-0135, Japan"}]}],"member":"8550","published-online":{"date-parts":[[2026,1,20]]},"reference":[{"key":"key-10.20965\/jaciii.2026.p0046-1","unstructured":"J. D. Schaffer, \u201cMultiple objective optimization with vector evaluated genetic algorithms,\u201d Proc. of the 1st Int. Conf. on Genetic Algorithms, pp. 93-100, 1985."},{"key":"key-10.20965\/jaciii.2026.p0046-2","unstructured":"C. M. Fonseca and P. J. Fleming, \u201cMultiobjective genetic algorithms,\u201d IEE Colloquium on Genetic Algorithms for Control Systems Engineering, 1993."},{"key":"key-10.20965\/jaciii.2026.p0046-3","doi-asserted-by":"crossref","unstructured":"J. Horn, N. Nafpliotis, and D. E. Goldberg, \u201cA niched Pareto genetic algorithm for multiobjective optimization,\u201d Proc. of the 1st IEEE Conf. on Evolutionary Computation, Vol.1, pp. 82-87, 1994. https:\/\/doi.org\/10.1109\/ICEC.1994.350037","DOI":"10.1109\/ICEC.1994.350037"},{"key":"key-10.20965\/jaciii.2026.p0046-4","doi-asserted-by":"crossref","unstructured":"N. Srinivas and K. Deb, \u201cMuiltiobjective optimization using nondominated sorting in genetic algorithms,\u201d Evolutionary Computation, Vol.2, No.3, pp. 221-248, 1994. https:\/\/doi.org\/10.1162\/evco.1994.2.3.221","DOI":"10.1162\/evco.1994.2.3.221"},{"key":"key-10.20965\/jaciii.2026.p0046-5","doi-asserted-by":"crossref","unstructured":"K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan, \u201cA fast and elitist multiobjective genetic algorithm: NSGA-II,\u201d IEEE Trans. on Evolutionary Computation, Vol.6, No.2, pp. 182-197, 2002. https:\/\/doi.org\/10.1109\/4235.996017","DOI":"10.1109\/4235.996017"},{"key":"key-10.20965\/jaciii.2026.p0046-6","doi-asserted-by":"crossref","unstructured":"Q. Zhang and H. Li, \u201cMOEA\/D: A multiobjective evolutionary algorithm based on decomposition,\u201d IEEE Trans. on Evolutionary Computation, Vol.11, No.6, pp. 712-731, 2007. https:\/\/doi.org\/10.1109\/TEVC.2007.892759","DOI":"10.1109\/TEVC.2007.892759"},{"key":"key-10.20965\/jaciii.2026.p0046-7","doi-asserted-by":"crossref","unstructured":"H. Li and Q. Zhang, \u201cMultiobjective optimization problems with complicated Pareto sets, MOEA\/D and NSGA-II,\u201d IEEE Trans. on Evolutionary Computation, Vol.13, No.2, pp. 284-302, 2008. https:\/\/doi.org\/10.1109\/TEVC.2008.925798","DOI":"10.1109\/TEVC.2008.925798"},{"key":"key-10.20965\/jaciii.2026.p0046-8","doi-asserted-by":"crossref","unstructured":"K. Deb and J. Sundar, \u201cReference point based multi-objective optimization using evolutionary algorithms,\u201d Proc. of the 8th Annual Conf. on Genetic and Evolutionary Computation, pp. 635-642, 2006. https:\/\/doi.org\/10.1145\/1143997.1144112","DOI":"10.1145\/1143997.1144112"},{"key":"key-10.20965\/jaciii.2026.p0046-9","doi-asserted-by":"crossref","unstructured":"K. Deb and H. Jain, \u201cAn evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, part i: Solving problems with box constraints,\u201d IEEE Trans. on Evolutionary Computation, Vol.18, No.4, pp. 577-601, 2014. https:\/\/doi.org\/10.1109\/TEVC.2013.2281535","DOI":"10.1109\/TEVC.2013.2281535"},{"key":"key-10.20965\/jaciii.2026.p0046-10","doi-asserted-by":"crossref","unstructured":"H. Jain and K. 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Smith, \u201cMulti-objective optimization using genetic algorithms: A tutorial,\u201d Reliability Engineering & System Safety, Vol.91, No.9, pp. 992-1007, 2006. https:\/\/doi.org\/10.1016\/j.ress.2005.11.018","DOI":"10.1016\/j.ress.2005.11.018"},{"key":"key-10.20965\/jaciii.2026.p0046-15","doi-asserted-by":"crossref","unstructured":"K. Deb, \u201cMulti-objective optimisation using evolutionary algorithms: An introduction,\u201d L. Wang, A. H. C. Ng, and K. Deb (Eds.), \u201cMulti-Objective Evolutionary Optimisation for Product Design and Manufacturing,\u201d pp. 3-34, Springer, 2011. https:\/\/doi.org\/10.1007\/978-0-85729-652-8_1","DOI":"10.1007\/978-0-85729-652-8_1"},{"key":"key-10.20965\/jaciii.2026.p0046-16","doi-asserted-by":"crossref","unstructured":"K. Deb, \u201cMulti-objective evolutionary algorithms,\u201d J. Kacprzyk and W. Pedrycz (Eds.), \u201cSpringer Handbook of Computational Intelligence,\u201d pp. 995-1015, Springer, 2015. https:\/\/doi.org\/10.1007\/978-3-662-43505-2_49","DOI":"10.1007\/978-3-662-43505-2_49"},{"key":"key-10.20965\/jaciii.2026.p0046-17","doi-asserted-by":"crossref","unstructured":"Z. Liu, F. Han, Q. Ling, H. Han, and J. Jiang, \u201cA many-objective optimization evolutionary algorithm based on hyper-dominance degree,\u201d Swarm and Evolutionary Computation, Vol.83, Article No.101411, 2023. https:\/\/doi.org\/10.1016\/j.swevo.2023.101411","DOI":"10.1016\/j.swevo.2023.101411"},{"key":"key-10.20965\/jaciii.2026.p0046-18","doi-asserted-by":"crossref","unstructured":"Y. Yun, H. Nakayama, T. Tanino, and M. Arakawa, \u201cA multi-objective optimization method combining generalized data envelopment analysis and genetic algorithms,\u201d Trans. of the Institute of Systems, Control and Information Engineers, Vol.13, No.4, pp. 179-185, 2000 (in Japanese). https:\/\/doi.org\/10.5687\/iscie.13.4_179","DOI":"10.5687\/iscie.13.4_179"},{"key":"key-10.20965\/jaciii.2026.p0046-19","doi-asserted-by":"crossref","unstructured":"E. Takeda, \u201cAn extended DEA model: Appending an additional input to make all DMUs at least weakly efficient,\u201d European J. of Operational Research, Vol.125, No.1, pp. 25-33, 2000. https:\/\/doi.org\/10.1016\/S0377-2217(99)00195-2","DOI":"10.1016\/S0377-2217(99)00195-2"},{"key":"key-10.20965\/jaciii.2026.p0046-20","doi-asserted-by":"crossref","unstructured":"A. Charnes, W. W. Cooper, and E. Rhodes, \u201cMeasuring the efficiency of decision making units,\u201d European J. of Operational Research, Vol.2, No.6, pp. 429-444, 1978. https:\/\/doi.org\/10.1016\/0377-2217(78)90138-8","DOI":"10.1016\/0377-2217(78)90138-8"},{"key":"key-10.20965\/jaciii.2026.p0046-21","unstructured":"M. Doi, T. Onishi, and M. Arakawa, \u201cGenetic algorithm with efficient selection using data envelopment analysis,\u201d Proc. of the Japanese Society for Evolutionary Computation, pp. 116-135, 2023."},{"key":"key-10.20965\/jaciii.2026.p0046-22","doi-asserted-by":"crossref","unstructured":"M. Doi, K. Sugihara, and M. Arakawa, \u201cGenetic algorithm with efficient selection using Pareto front modeling and data envelopment analysis,\u201d 2024 Joint 13th Int. Conf. on Soft Computing and Intelligent Systems and 25th Int. 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Deb, \u201cPymoo: Multi-objective optimization in Python,\u201d IEEE Access, Vol.8, pp. 89497-89509, 2020. https:\/\/doi.org\/10.1109\/ACCESS.2020.2990567","DOI":"10.1109\/ACCESS.2020.2990567"},{"key":"key-10.20965\/jaciii.2026.p0046-26","doi-asserted-by":"crossref","unstructured":"C. M. Fonseca, L. Paquete, and M. Lopez-Ibanez, \u201cAn improved dimension-sweep algorithm for the hypervolume indicator,\u201d 2006 IEEE Int. Conf. on Evolutionary Computation, pp. 1157-1163, 2006. https:\/\/doi.org\/10.1109\/CEC.2006.1688440","DOI":"10.1109\/CEC.2006.1688440"},{"key":"key-10.20965\/jaciii.2026.p0046-27","doi-asserted-by":"crossref","unstructured":"H. Ishibuchi, H. Masuda, Y. Tanigaki, and Y. Nojima, \u201cModified distance calculation in generational distance and inverted generational distance,\u201d Proc. of the 8th Int. Conf. on Evolutionary Multi-Criterion Optimization, pp. 110-125, 2015. https:\/\/doi.org\/10.1007\/978-3-319-15892-1_8","DOI":"10.1007\/978-3-319-15892-1_8"},{"key":"key-10.20965\/jaciii.2026.p0046-28","unstructured":"K. Deb and R. B. Agrawal, \u201cSimulated binary crossover for continuous search space,\u201d Complex Systems, Vol.9, No.2, pp. 115-148, 1995."},{"key":"key-10.20965\/jaciii.2026.p0046-29","doi-asserted-by":"crossref","unstructured":"K. Deb, K. Sindhya, and T. Okabe, \u201cSelf-adaptive simulated binary crossover for real-parameter optimization,\u201d Proc. of the 9th Annual Conf. on Genetic and Evolutionary Computation, pp. 1187-1194, 2007. https:\/\/doi.org\/10.1145\/1276958.1277190","DOI":"10.1145\/1276958.1277190"},{"key":"key-10.20965\/jaciii.2026.p0046-30","doi-asserted-by":"crossref","unstructured":"H. Ishibuchi, Y. Setoguchi, H. Masuda, and Y. Nojima, \u201cPerformance of decomposition-based many-objective algorithms strongly depends on Pareto front shapes,\u201d IEEE Trans. on Evolutionary Computation, Vol.21, No.2, pp. 169-190, 2017. https:\/\/doi.org\/10.1109\/TEVC.2016.2587749","DOI":"10.1109\/TEVC.2016.2587749"},{"key":"key-10.20965\/jaciii.2026.p0046-31","doi-asserted-by":"crossref","unstructured":"H. Ishibuchi, Y. Nan, and L. M. Pang, \u201cPerformance evaluation of multi-objective evolutionary algorithms using artificial and real-world problems,\u201d Proc. of the 12th Int. Conf. on Evolutionary Multi-Criterion Optimization, pp. 333-347, 2023. https:\/\/doi.org\/10.1007\/978-3-031-27250-9_24","DOI":"10.1007\/978-3-031-27250-9_24"},{"key":"key-10.20965\/jaciii.2026.p0046-32","doi-asserted-by":"crossref","unstructured":"L. M. Pang, H. Ishibuchi, and K. Shang, \u201cAnalysis of algorithm comparison results on real-world multi-objective problems,\u201d 2024 IEEE Congress on Evolutionary Computation, 2024. https:\/\/doi.org\/10.1109\/CEC60901.2024.10612187","DOI":"10.1109\/CEC60901.2024.10612187"}],"container-title":["Journal of Advanced Computational Intelligence and Intelligent Informatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.fujipress.jp\/main\/wp-content\/themes\/Fujipress\/hyosetsu.php?ppno=jacii003000010005","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,19]],"date-time":"2026-01-19T15:02:23Z","timestamp":1768834943000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.fujipress.jp\/jaciii\/jc\/jacii003000010046"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1,20]]},"references-count":32,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,1,20]]},"published-print":{"date-parts":[[2026,1,20]]}},"URL":"https:\/\/doi.org\/10.20965\/jaciii.2026.p0046","relation":{},"ISSN":["1883-8014","1343-0130"],"issn-type":[{"value":"1883-8014","type":"electronic"},{"value":"1343-0130","type":"print"}],"subject":[],"published":{"date-parts":[[2026,1,20]]}}}