Towards faster convergence of evolutionary multi-criterion optimization algorithms using Karush Kuhn Tucker optimality based local search

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• 作者：Mohamed Abouhawwash^a ; ^c ; ^{saleh1284@mans.edu.eg} ; ^{mhawwash@msu.edu} ; Haitham Seada^b ; ^{seadahai@msu.edu} ; Kalyanmoy Deb^c ; ^{kdeb@egr.msu.edu}
• 关键词：NSGA-III ; Local ; Search ; KKT ; EMO
• 刊名：Computers & Operations Research
• 出版年：2017
• 出版时间：March 2017
• 年：2017
• 卷：79
• 期：Complete
• 页码：331-346
• 全文大小：3373 K
• 卷排序：79

文摘

Utilization of the recently proposed KKTPM to identify poorly converged solutions in multiobjective optimization problems. Scalarizing the multiobjective problem using an Augmented Scalarization Function (ASF), and applying local search on the identified points to enhance overall convergence. Proposing VarDens, a new bi-objective optimization problem with arbitrary number of variables. VarDens represents a category of problems where the Pareto front is disjoint and some sections are easier to attain that others. Weaving KKTPM, NSGA-III and local search into one algorithm that outperforms the original NSGA-III in terms of convergence.