Biogeography-Based Particle Swarm Optimization with Fuzzy Elitism and Its Applications to Constrained Engineering Problems

In Evolutionary Algorithms (EA), elites are crucial to maintain good features in solutions. However, too many elites could make the evolutionary process stagnate and cannot enhance the performance. In this paper, we employ Particle Swarm Optimization (PSO) and Biogeography-Based Optimization (BBO) to propose a hybrid algorithm termed Biogeography-Based Particle Swarm Optimization (BPSO) which could make a large number of elites effective in searching optimum. In this algorithm, the whole population is split into several subgroups, which BBO is employed for intra-group and PSO is employed for inter-groups. Since not all the population is used in PSO, this structure overcomes the premature in original PSO. By the time complexity analysis, the novel algorithm will not increase the time consuming. Fourteen numerical benchmarks and four engineering problems with constraints are used to test BPSO. To better deal with constrains, a fuzzy strategy for number of elites is investigated. The simulations results validate the feasibility and effectiveness of the proposed algorithm.