A Distributed Bi-Behaviors Crow Search Algorithm for Dynamic Multi-Objective Optimization and Many-Objective Optimization Problems

Dynamic Multi-Objective Optimization Problems (DMOPs) and Many-Objective (MaOPs) are two classes of the optimization field that have potential applications in engineering. Modified Evolutionary Algorithms hybrid approaches seem to be suitable effectively deal with such problems. However, standard Crow Search Algorithm has not been considered for either DMOPs or MaOPs date. This paper proposes a Distributed Bi-behaviors (DB-CSA) different mechanisms, one corresponding search behavior another exploitative dynamic switch mechanism. The bi-behaviors CSA chasing profile is defined based on large Gaussian-like Beta-1 function, which ensures diversity enhancement, while narrow Gaussian Beta-2 function used improve solution tuning convergence behavior. Two variants proposed DB-CSA approach developed: first variant solve set 2, 3, 5, 7, 8, 10,15 objectives, second aims several types time-varying Pareto optimal sets front. algorithm (DB-CSA-II) DMOPs, including process detect react change. Inverted General Distance, Mean Distance Hypervolume Difference main measurement metrics compare state-of-the-art MOEAs. Taguchi method manage meta-parameters algorithm. All quantitative results analyzed using non-parametric Wilcoxon signed rank test 0.05 significance level, validated efficiency solving 44 beds (21 23 MaOPS).