Multi-Objective Particle Swarm Optimization Algorithms - A Leader Selection Overview

Multi Objective Optimization (MOO) problem involves simultaneous minimization or maximization of many objective functions. Various MOO algorithms have been introduced to solve the MOO problem. Traditional gradient-based techniques are one of the methods used to solve MOO problems. However, in the traditional gradient-based technique only one solution is generated. Thus, an alternative approach such as Particle Swarm Optimization (PSO), which able to produce a number of possible solutions are highly desirable. In PSO, particles search the optimum solution under the influence of a better solution known as leader. This leader facilitates cooperation between all particles. However, this strategy to select the leader has to be changed when it is used for MOO problems. This paper presents an overview of the multi-objective PSO algorithms, which emphasize on the leader selection. In addition, the description of PSO and multi-objective optimization problems are also provided.

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