Efficient Particle Swarm Optimization Algorithm Based on Affinity Propagation

Abstract

Since the 1950s, with the great development of computer technology and bionics, particle swarm optimization (PSO) was raised. The particle swarm optimization mimics the nature biological group behaviors, and has the following advantages compared to classic optimization algorithms: it is a global optimization process and doesn’t depend on the initial state; it can be applied widely without prior knowledge on the optimization problems; the ideas and the implements of PSO are quite simple, the steps are standardization, and it’s very convenient to integrate it with other algorithms; PSO is based on the swarm intelligence theory, and it has very good potential parallelism. Particle swarm optimization has a feature that fitness value is used to exchange information in the population, and guides the population to close the optimal solution. Therefore, a mount of fitness should be calculated in swarm intelligence optimization algorithms in order to find the optimal solution or an approximate one. However, when the calculation of the fitness is quite complex, the time cost of this kind of algorithms will be too large. What’s more, the fitness of optimization problems in the real world is often difficult to calculate. Addressing this problem,Efficient Particle Swarm Optimization Algorithm Based on Affinity Propagation (EAPSO) is proposed in this paper.