A Distributed Bi-behaviors Crow Search Algorithm for Dynamic
Multi-Objective Optimization and Many-Objective Optimization
Abstract
Dynamic multi-objective optimization problems (DMOPs) and Many-Objective
Optimization Problems (MaOPs) are two classes of the optimization filed
which have potential applications in engineering. Modified
Multi-Objective Evolutionary Algorithms hybrid approaches seem to be
suitable to effectively deal with such problems. However, the Crow
Search Algorithm has not yet considered for both DMOP and MaOP. This
paper proposes a Distributed Bi-behaviorsCrow Search Algorithm
(DB-CSA) with two different mechanisms, one corresponding to the search
behavior and another to the exploitative behavior with a dynamic switch
mechanism. The bi-behaviors CSA chasing profile is defined based on a
large Gaussian-like Beta-1 function which ensures diversity enhancement,
while the narrow Gaussian Beta-2 function is used to improve the
solution tuning and convergence behavior. The DB-CSA approach is
developed to solve several types of DMOPs and a set of MaOPs with 2, 3,
5, 7, 8, 10 and 15 objectives. The Inverted General Distance, the Mean
Inverted General Distance and the Hypervolume Difference are the main
measurement metrics are used to compare the DB-CSA approach to the
state-of-the-art MOEAs. All quantitative results are analyzed using the
nonparametric Wilcoxon signed rank test with 0.05 significance level
which proving the efficiency of the proposed method for solving both 44
DMOPs and MaOPs utilized.