Discrete and continuous optimization based on multi-swarm coevolution

This paper presents a novel Multi-swarm Particle Swarm Optimizer called (PSO)-O-2, which is inspired by the coevolution of symbiotic species in natural ecosystems. The main idea of (PSO)-O-2 is to extend the single population PSO to the interacting multi-swarms model by constructing hierarchical interaction topology and enhanced dynamical update equations. With the hierarchical interaction topology, a suitable diversity in the whole population can be maintained. At the same time, the enhanced dynamical update rule significantly speeds up the multi-swarm to converge to the global optimum. The (PSO)-O-2 algorithm, which is conceptually simple and easy to implement, has considerable potential for solving complex optimization problems. With a set of 17 mathematical benchmark functions (including both continuous and discrete cases), (PSO)-O-2 is proved to have significantly better performance than four other successful variants of PSO.