MOCOVIDOA: a novel multi-objective coronavirus disease optimization algorithm for solving multi-objective optimization problems

被引：14

作者：

Khalid, Asmaa M. M. ^{[1
]}

Hamza, Hanaa M. M. ^{[1
]}

Mirjalili, Seyedali ^{[2
]}

Hosny, Khaid M. M. ^{[1
]}

机构：

[1] Zagazig Univ, Fac Comp & Informat, Dept Informat Technol, Zagazig 44519, Egypt

[2] Torrens Univ Australia, Ctr Artificial Intelligence Res & Optimisat, Brisbane, Qld 4006, Australia

来源：

NEURAL COMPUTING & APPLICATIONS | 2023年 / 35卷 / 23期

关键词：

Coronavirus; Multi-objective; Frameshifting; Dominance; Convergence; Coverage; EVOLUTIONARY ALGORITHMS; GENETIC ALGORITHMS; OPTIMAL-DESIGN; OBJECTIVES; DISCRETE; BEAM;

D O I：

10.1007/s00521-023-08587-w

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

A novel multi-objective Coronavirus disease optimization algorithm (MOCOVIDOA) is presented to solve global optimization problems with up to three objective functions. This algorithm used an archive to store non-dominated POSs during the optimization process. Then, a roulette wheel selection mechanism selects the effective archived solutions by simulating the frameshifting technique Coronavirus particles use for replication. We evaluated the efficiency by solving twenty-seven multi-objective (21 benchmarks & 6 real-world engineering design) problems, where the results are compared against five common multi-objective metaheuristics. The comparison uses six evaluation metrics, including IGD, GD, MS, SP, HV, and delta p (DP). The obtained results and the Wilcoxon rank-sum test show the superiority of this novel algorithm over the existing algorithms and reveal its applicability in solving multi-objective problems.

引用

页码：17319 / 17347

页数：29

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