A Clustering Based Archive Multi Objective Gravitational Search Algorithm

Abstract

Gravitational search algorithm (GSA) is a recent created metaheuristic optimization algo- rithm with good results in function optimization as well as real world optimization problems. Many real world problems involve multiple (often conflicting) ob jectives, which should be optimized si- multaneously. Therefore, the aim of this paper is to propose a multi-objective version of GSA, namely clustering based archive multi-objective GSA (CA-MOGSA). Proposed method is created based on the Pareto principles. Selected non-dominated solutions are stored in an external archive. To control the size of archive, the solutions with less crowding distance are removed. These strate- gies guarantee the elitism and diversity as two important features of multi-objective algorithms. The archive is clustered and a cluster is randomly selected for e ach agent to apply the gravitational force to attract it. The selection of the proper cluster is based on the distance between clusters represen- tatives and population member (the agent). Therefore, suitable trade-off between exploration and exploitation is provided. The experimental results on eight standard benchmark functions reveal that CA-MOGSA is a well-organized multi-objective version of GSA. It is comparable with the state-of- the-art algorithms including non-dominated sorting genetic algorithm-II (NSGA-II), strength Pareto evolutionary algorithm (SPEA2) and better than multi-objective GSA (MOGSA), time-variant par- ticle swarm optimization (TV-PSO), and non-dominated sorting GSA (NSGSA).