Journal Article10.1016/J.SWEVO.2020.100825
A decomposition-based multiobjective evolutionary algorithm with weight vector adaptation
Xin Zhou,Xuewu Wang,Xingsheng Gu +2 more
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TL;DR: In this article, a decomposition-based multi-objective evolutionary algorithm with weight vector adaptation (WVA-MOEA/D) is proposed to adjust the weight vectors uniformly distribute in the solution space.
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Abstract: Multi-objective Evolutionary Algorithms (MOEAs) have been concerned and studied with great achievements in the last two decades. As a typical decomposition-based MOEA, MOEA/D aims to decompose a multi-objective optimization problem (MOP) into several subproblems through a set of predefined weight vectors and then optimizes these problems simultaneously. However, performance degradation occurs when complex optimization problems with complicated Pareto Front shape (i.e., irregular and discontinuous PF) are handled. This paper proposes a decomposition-based multi-objective evolutionary algorithm with weight vector adaptation (WVA-MOEA/D) to adjust the weight vectors uniformly distribute in the solution space. The algorithm decomposes a MOP into several subproblems, the new environment selection mechanism defines several neighborhoods with weight vectors as the center of the circle, and elite solutions are selected based on the density of each neighborhood. Weight vector adaptation is employed to guide solution selection and obtain a set of uniformly distributed solutions. The proposed WVA-MOEA/D can improve the performance of MOEA/D on MOPs and many-objective problems with irregular PFs. Besides, the neighborhood adaptation strategy used in the algorithm aims to maintain the diversity solutions and decrease the selection pressure in many-objective optimization problems. Experimental results indicate that WVA-MOEA/D could further effectively solve MOPs with various types of Pareto Fronts for multi-objective and many-objective optimization compared with several state-of-the-art evolutionary algorithms.
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John H. Holland
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Kalyanmoy Deb,Deb Kalyanmoy +1 more
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TL;DR: This text provides an excellent introduction to the use of evolutionary algorithms in multi-objective optimization, allowing use as a graduate course text or for self-study.
MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition
Qingfu Zhang,Hui Li +1 more
TL;DR: Experimental results have demonstrated that MOEA/D with simple decomposition methods outperforms or performs similarly to MOGLS and NSGA-II on multiobjective 0-1 knapsack problems and continuous multiobjectives optimization problems.
An Evolutionary Many-Objective Optimization Algorithm Using Reference-Point-Based Nondominated Sorting Approach, Part I: Solving Problems With Box Constraints
Kalyanmoy Deb,Himanshu Jain +1 more
TL;DR: A reference-point-based many-objective evolutionary algorithm that emphasizes population members that are nondominated, yet close to a set of supplied reference points is suggested that is found to produce satisfactory results on all problems considered in this paper.