A Meta-Objective Approach for Many-Objective Evolutionary Optimization

被引:29
作者
Gong, Dunwei [1 ]
Liu, Yiping [1 ,2 ]
Yen, Gary G. [3 ]
机构
[1] China Univ Min & Technol, Sch Informat & Control Engn, Xuzhou 221116, Jiangsu, Peoples R China
[2] Osaka Prefecture Univ, Grad Sch Engn, Dept Comp Sci & Intelligent Syst, Sakai, Osaka 5998531, Japan
[3] Oklahoma State Univ, Sch Elect & Comp Engn, Stillwater, OK 74078 USA
基金
中国国家自然科学基金;
关键词
Many-objective optimization; evolutionary multi-objective optimization; meta-objective; convergence; diversity; MULTIOBJECTIVE OPTIMIZATION; ALGORITHM; DIVERSITY; CONVERGENCE; SELECTION; MOEA/D;
D O I
10.1162/evco_a_00243
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Pareto-based multi-objective evolutionary algorithms experience grand challenges in solving many-objective optimization problems due to their inability to maintain both convergence and diversity in a high-dimensional objective space. Exiting approaches usually modify the selection criteria to overcome this issue. Different from them, we propose a novel meta-objective (MeO) approach that transforms the many-objective optimization problems in which the new optimization problems become easier to solve by the Pareto-based algorithms. MeO converts a given many-objective optimization problem into a new one, which has the same Pareto optimal solutions and the number of objectives with the original one. Each meta-objective in the new problem consists of two components which measure the convergence and diversity performances of a solution, respectively. Since MeO only converts the problem formulation, it can be readily incorporated within any multi-objective evolutionary algorithms, including those non-Pareto-based ones. Particularly, it can boost the Pareto-based algorithms' ability to solve many-objective optimization problems. Due to separately evaluating the convergence and diversity performances of a solution, the traditional density-based selection criteria, for example, crowding distance, will no longer mistake a solution with poor convergence performance for a solution with low density value. By penalizing a solution in term of its convergence performance in the meta-objective space, the Pareto dominance becomes much more effective for a many-objective optimization problem. Comparative study validates the competitive performance of the proposed meta-objective approach in solving many-objective optimization problems.
引用
收藏
页码:1 / 25
页数:25
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