A Study of Convergence Speed in Multi-objective Metaheuristics Export

@incollection{citeulike:3292284, abstract = {An open issue in multi-objective optimization is designing metaheuristics that reach the Pareto front using a low number of function evaluations. In this paper, we adopt a benchmark composed of three well-known problem families (ZDT, DTLZ, and WFG) and analyze the behavior of six state-of-the-art multi-objective metaheuristics, namely, NSGA-II, SPEA2, PAES, OMOPSO, AbYSS, and MOCell, according to their convergence speed, i.e., the number of evaluations required to obtain an accurate Pareto front. By using the hypervolume as a quality indicator, we measure the algorithms converging faster, as well as their hit rate over 100 independent runs. Our study reveals that modern multi-objective metaheuristics such as MOCell, OMOPSO, and AbYSS provide the best overall performance, while NSGA-II and MOCell achieve the best hit rates.}, author = {Nebro, Antonio and Durillo, Juan and Coello Coello, Carlos and Luna, Francisco and Alba, Enrique}, citeulike-article-id = {3292284}, citeulike-linkout-0 = {http://dx.doi.org/10.1007/978-3-540-87700-4_76}, citeulike-linkout-1 = {http://www.springerlink.com/content/p25w896722185312}, doi = {10.1007/978-3-540-87700-4_76}, journal = {Parallel Problem Solving from Nature – PPSN X}, keywords = {benchmark, comparison, convergence, evolutionary-computation, metaheuristic, multi-objective}, pages = {763--772}, posted-at = {2008-09-19 09:42:34}, priority = {3}, title = {A Study of Convergence Speed in Multi-objective Metaheuristics}, url = {http://dx.doi.org/10.1007/978-3-540-87700-4_76}, year = {2008} }

TY - CHAP ID - citeulike:3292284 L3 - citeulike-article-id:3292284 N2 - An open issue in multi-objective optimization is designing metaheuristics that reach the Pareto front using a low number of function evaluations. In this paper, we adopt a benchmark composed of three well-known problem families (ZDT, DTLZ, and WFG) and analyze the behavior of six state-of-the-art multi-objective metaheuristics, namely, NSGA-II, SPEA2, PAES, OMOPSO, AbYSS, and MOCell, according to their convergence speed, i.e., the number of evaluations required to obtain an accurate Pareto front. By using the hypervolume as a quality indicator, we measure the algorithms converging faster, as well as their hit rate over 100 independent runs. Our study reveals that modern multi-objective metaheuristics such as MOCell, OMOPSO, and AbYSS provide the best overall performance, while NSGA-II and MOCell achieve the best hit rates. JF - Parallel Problem Solving from Nature – PPSN X EP - 772 TI - A Study of Convergence Speed in Multi-objective Metaheuristics SP - 763 KW - benchmark KW - comparison KW - convergence KW - evolutionary-computation KW - metaheuristic KW - multi-objective AU - Nebro, Antonio AU - Durillo, Juan AU - Coello Coello, Carlos AU - Luna, Francisco AU - Alba, Enrique PY - 2008/// UR - http://dx.doi.org/10.1007/978-3-540-87700-4_76 ER -