A Comprehensive Review on Hybrid Swarm Intelligence for Optimization

摘要

With the rapid development of swarm intelligence research field, a large number of algorithms in swarm intelligence are proposed one after another. The strong points and the drawbacks of a specific swarm intelligence algorithm becomes clear to be seen when the number of its application increases. To overcome the handicaps, some hybrid methods are invented. In this review, three hybrid swarm intelligence algorithms, namely, Adaptive Simulated Annealing - Particle Swarm Optimization (ASA-PSO), Bacterial-GA Foraging, and Enhanced Parallel Cat Swarm Optimization (EPCSO), for solving numerical problems in optimization are summarized. The objective of this review is to provide a comprehensive introduction for new researchers to get into the hybrid swarm intelligence research field.