Multi-Objective Volleyball Premier League algorithm

被引：18

作者：

Moghdani, Reza ^{[1
]}

Salimifard, Khodakaram ^{[1
]}

Demir, Emrah ^{[2
]}

Benyettou, Abdelkader ^{[3
]}

机构：

[1] Persian Gulf Univ, Computat Intelligence & Intelligent Optimizat Res, Bushehr, Iran

[2] Cardiff Univ, Cardiff Business Sch, Cardiff, Wales

[3] USTOran, Dept Informat, BP 1505, El Mnaouer, Oran, Algeria

来源：

KNOWLEDGE-BASED SYSTEMS | 2020年 / 196卷

关键词：

Multi-objective evolutionary algorithm; Global optimization; Pareto solution; Engineering design optimization problems; PARTICLE SWARM OPTIMIZATION; LOCATION-ALLOCATION PROBLEM; GENETIC ALGORITHM; EVOLUTIONARY ALGORITHMS; OBJECTIVE OPTIMIZATION; OPTIMAL-DESIGN; SEARCH; DECOMPOSITION; PERFORMANCE;

D O I：

10.1016/j.knosys.2020.105781

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper proposes a novel optimization algorithm called the Multi-Objective Volleyball Premier League (MOVPL) algorithm for solving global optimization problems with multiple objective functions. The algorithm is inspired by the teams competing in a volleyball premier league. The strong point of this study lies in extending the multi-objective version of the Volleyball Premier League algorithm (VPL), which is recently used in such scientific researches, with incorporating the well-known approaches including archive set and leader selection strategy to obtain optimal solutions for a given problem with multiple contradicted objectives. To analyze the performance of the algorithm, ten multi-objective benchmark problems with complex objectives are solved and compared with two well-known multi-objective algorithms, namely Multi-Objective Particle Swarm Optimization (MOPSO) and Multi-Objective Evolutionary Algorithm Based on Decomposition (MOEA/D). Computational experiments highlight that the MOVPL outperforms the two state-of-the-art algorithms on multi-objective benchmark problems. In addition, the MOVPL algorithm has provided promising results on well-known engineering design optimization problems. (C) 2020 Elsevier B.V. All rights reserved.

引用

页数：22

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