A Clustering Based Archive Multi Objective Gravitational Search Algorithm

Gravitational search algorithm(GSA) is a recent createdmetaheuristic optimization algorithm with good results in function optimization as well as real world optimization problems. Many real world problems involve multiple (often conflicting) objectives, which should be optimized simultaneously. 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 strategies guarantee the elitism and diversity as two important features of multi-objective algorithms. The archive is clustered and a cluster is randomly selected for each agent to apply the gravitational force to attract it. The selection of the proper cluster is based on the distance between clusters representatives 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 particle swarm optimization (TV-PSO), and non-dominated sorting GSA (NSGSA).