A new gravitational search optimization algorithm to solve single and multiobjective optimization problems

Abstract

This paper proposes a sufficient improved optimization algorithm based on gravitational search algorithm (GSA) to handle complex multi-objective (MO) optimization problems. The main idea behind the GSA is based on the Newton's law which has shown great success in recent years. Nevertheless, the existence of some deficiencies such as dependency of the algorithm on its adjusting parameters, stagnation and the probability of trapping in local optima has reduced the searching ability of the GSA. In order to overcome the above deficiencies, this paper suggests a novel mutation technique which is implemented based on four types of solutions. The first type belongs to the non-dominated solutions. The second type includes those solutions which are dominated by only one solution in the search space. Similarly, the third and the forth types belong to those solutions which are dominated by only three and four solutions respectively. After that, the initial GSA population is constructed by choosing from these four types of solutions. This procedure will let the next movement of the algorithm to reach more optimal solutions through the improvisation stage. The satisfying performance of the proposed algorithm is examined on several well-known benchmarks including both the single-objective and multi-objective optimization problems.