PSO with Predatory Escaping Behavior and Its Application on Shortest Path Routing Problems

Particle swarm optimization (PSO) algorithms are now being practiced for more than a decade and have been extended to solve various types of optimization problems. However, straightforward application of PSO suffers from premature convergence and lacks of intensification around the local best locations. In this paper, we propose a new particle swarm optimization strategy, namely, particle swarm optimization with predatory escaping behavior (PSO-PE), to solve shortest path routing problems (SPR). PSO-PE uses the predatory particles to enlarge the escaping particles' predation risk. After taking a tradeoff between predation risk and their energy, escaping particles would take different escaping behaviors. This disturbance makes particle swarm achieve social cognition symmetrically, keep the diversity, balance the exploration and exploitation, and avoid the premature convergence. Simulation experiments of solving SPR using PSO-PE have been carried out on the different network topologies consisting of 15-50 nodes. Our approach can find out the optimal path with high success rates. The performance of proposed PSO-PE-based searching method surpasses the straightforward application of PSO and genetic algorithm (GA) for this problem.