MOGOA algorithm for constrained and unconstrained multi-objective optimization problems

被引：62

作者：

Tharwat, Alaa ^{[1
,2
,5
]}

Houssein, Essam H. ^{[3
,5
]}

Ahmed, Mohammed M. ^{[3
,5
]}

Hassanien, Aboul Ella ^{[4
,5
]}

Gabel, Thomas ^{[1
]}

机构：

[1] Frankfurt Univ Appl Sci, Fac Comp Sci & Engn, D-60318 Frankfurt, Germany

[2] Suez Canal Univ, Fac Engn, Ismailia, Egypt

[3] Minia Univ, Fac Comp & Informat, Al Minya, Egypt

[4] Cairo Univ, Fac Comp & Informat, Giza, Egypt

[5] SRGE, Cairo, Egypt

来源：

APPLIED INTELLIGENCE | 2018年 / 48卷 / 08期

关键词：

Multi-objective optimization; Grasshopper optimization algorithm; Pareto optimal solutions; Evolutionary algorithm; Constrained optimization; Unconstrained optimization; EVOLUTIONARY ALGORITHMS; PARAMETER OPTIMIZATION; DESIGN;

D O I：

10.1007/s10489-017-1074-1

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Grasshopper Optimization Algorithm (GOA) was modified in this paper, to optimize multi-objective problems, and the modified version is called Multi-Objective Grasshopper Optimization Algorithm (MOGOA). An external archive is integrated with the GOA for saving the Pareto optimal solutions. The archive is then employed for defining the social behavior of the GOA in the multi-objective search space. To evaluate and verify the effectiveness of the MOGOA, a set of standard unconstrained and constrained test functions are used. Moreover, the proposed algorithm was compared with three well-known optimization algorithms: Multi-Objective Particle Swarm Optimization (MOPSO), Multi-Objective Ant Lion Optimizer (MOALO), and Non-dominated Sorting Genetic Algorithm version 2 (NSGA-II); and the obtained results show that the MOGOA algorithm is able to provide competitive results and outperform other algorithms.

引用

页码：2268 / 2283

页数：16

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