A new multi-objective particle swarm optimizer using empirical movement and diversified 
search strategies

Abstract

Most real-world optimization problems involve the optimization task of more than a single 
objective function and, therefore, require a great amount of computational effort as the 
solution procedure is designed to anchor multiple compromised optimal solutions. Abundant 
multi-objective evolutionary algorithms (MOEAs) for multi-objective optimization have appeared 
in the literature over the past two decades. In this article, a new proposal by means of 
particle swarm optimization is addressed for solving multi-objective optimization problems. 
The proposed algorithm is constructed based on the concept of Pareto dominance, taking both the 
diversified search and empirical movement strategies into account. The proposed particle swarm 
MOEA with these two strategies is thus dubbed the empirical-movement diversified-search multi-objective 
particle swarm optimizer (EMDS-MOPSO). Its performance is assessed in terms of a suite of standard 
benchmark functions taken from the literature and compared to other four state-of-the-art MOEAs. 
The computational results demonstrate that the proposed algorithm shows great promise in solving 
multi-objective optimization problems.