Horizontal Structure Competition-Mutually Beneficial Community Optimization Algorithm

Abstract

To solve global optimal solutions of nonlinear optimization problems, a new horizontal structure competition- mutually beneficial community optimization algorithm (HS-CBCO), is proposed based on the theory of horizontal structure competition-mutually beneficial community dynamics. In this algorithm, each biological population is composed of many biological individuals, the interaction across populations is mainly competition and mutual benefit.Within a population, there are interactions among individuals. Six operators are developed by using the community dynamics, among them, the competition and mutually beneficial operator can exchange information among individuals across populations, while the general and strong influence operator can exchange information among individuals within a population, thus realizing the full exchange of information among individuals. The newborn operator can timely supplement new individuals to a population, and the death operator can timely eliminate weak individuals from a population, thus greatly improving the ability of the algorithm to escape from local traps. The test results show that HS-CBCO has excellent exploitation ability, exploration ability and coordination between them, and has the characteristics of global convergence. The algorithm provides solutions for solving global optimal solutions of some complex function optimization problems.

Keywords

• swarm intelligence optimization algorithm
• horizontal structure competition-mutually beneficial community dynamics
• population dynamics
• global optimal solution