MOCSA: A Multi-Objective Crow Search Algorithm for Multi-Objective optimization

Abstract

In this paper, an extension of the recently developed Crow Search Algorithm (CSA) to multi-objective optimization problems is presented. The proposed algorithm, called Multi-Objective Crow Search Algorithm (MOCSA), defines the fitness function using a set of determined weight vectors, employing the max-min strategy. In order to improve the efficiency of the search space, the performance space is regionalized using specific control points. A new chasing operator is also employed in order to improve the convergence process. Numerical results show that MOCSA is closely comparable to well-known multi-objective algorithms.