A Comparative Study of Evolutionary Algorithms and Particle Swarm Optimization Approaches for Constrained Multi-Objective Optimization Problems

Abstract

Many real-world problems in the fields of science and engineering contain multiple conflicting objectives which need to optimized simultaneously, as well as additional problem constraints. These problems are referred to as constrained multi-objective optimization problems (CMOPs). CMOPs do not have a single optimal solution, but instead a set of optimal trade-off solutions where an improvement in one objective worsens another. Recently, many constrained multi-objective evolutionary algorithms (CMOEAs) have been introduced for solving CMOPs. Each of these computational intelligence algorithms can be classified into one of four different approaches, which are the classic CMOEAs, co-evolutionary approaches, multi-stage approaches, and multi-tasking approaches. An extensive survey and comparative study of the aforementioned algorithms on a variety of benchmark test problems, including real-world CMOPs, is carried out in order to determine the current state-of-the-art CMOEAs. Additionally, this work proposes a multi-guide particle swarm optimization (MGPSO) for the constrained multi-objective problems. MGPSO is a multi-swarm approach, which has previously been effectively applied to other challenging optimization problems in the literature. This work adapts MGPSO for solving CMOPs and compares its performance against the aforementioned existing computational intelligence techniques. The comparative study showed that the algorithmic performance was problem-dependent. Lastly, while the proposed MGPSO approach was likewise problem-dependent, it was found to perform best for some of the real-world problems, namely the process, design and synthesis problems, and had competitive performance in the power system optimization problems.