Gravitational Co-evolution and Opposition-based Optimization Algorithm

Abstract

In this paper, a Gravitational Co-evolution and Opposition-based Optimization (GCOO) algorithm is proposed for solving unconstrained optimization problems. Firstly, under the framework of gravitation based co-evolution, individuals of the population are divided into two subpopulations according to their fitness values (objective function values), i.e., the elitist subpopulation and the common subpopulation, and then three types of gravitation-based update methods are implemented. With the cooperation of opposition-based operation, the proposed algorithm conducts the optimizing process collaboratively. Three benchmark algorithms and fifteen typical benchmark functions are utilized to evaluate the performance of GCOO, where the substantial experimental data shows that the proposed algorithm has better performance with regards to effectiveness and robustness in solving unconstrained optimization problems.

Keywords

• Gravitation
• Evolution algorithm
• Co-evolution
• Opposition-based
• Optimization