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

Abstract

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 ($$\Delta \mathrm{P}$$ Δ P ). 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.

Cheetah Optimizer for Multi-objective Optimization Problems

“…Multiobjective grey Wolf optimizer (MOGWO) [19], Multi-objective Chaos Game Optimization [20], and multi-objective spotted hyena optimizer (MOSHO) [13] use grid, archive, and leader selection mechanisms. Multi-objective Coronavirus disease optimization algorithm [21] makes use of ND sorting, archive and leader selection methods. MOEA/D [7] uses decomposition methods and weight vectors.…”

Parameter estimation of photovoltaic module relied on golden jackal optimization

“…It can be seen that the number of heuristic-based methods is larger than that of deterministic methods. In addition, there are many newly developed algorithms such as Henry gas solubility optimization (HGSO) [19], a coronavirus disease optimization algorithm [20], prairie dog optimization [21], evolutionary mating algorithm [22], etc. and they have also been successfully applied to many problems in electrical engineering such as power quality disturbance [23], optimal power flow [24][25][26] and distributed generator placement [27].…”