GRAVITATIONAL SEARCH ALGORITHM WITH CHAOTIC MAP (GSA-CM) FOR SOLVING OPTIMIZATION PROBLEMS

Abstract

Gravitational Search Algorithm (GSA) is a newly heuristic algorithm inspired by nature which utilizes Newtonian gravity law and mass interactions. It has captured much attention since it has provided higher performance in solving various optimization problems. This study hybridizes the GSA and chaotic equations. Ten chaotic-based GSA (GSA-CM) methods, which define the random selections by different chaotic maps, have been developed. The proposed methods have been applied to the minimization of benchmark problems and the results have been compared. The obtained numeric results show that most of the proposed algorithms have increased the performance of GSA and have developed its quality of solution.