Particle Swarm Optimizer with Simulated Binary Crossover and Polynomial Mutation and its Application
基于二进制交叉和变异的粒子群算法及应用

PSO may easily get trapped in a local optimum, when it comes to solving multimodal problems. In view of the default, we presented a variant of particle swarm optimizer(PSO) with simulated binary crossover and polynomial mutation(SPDPSO for short). In SPDPSO, additionally, the external archive was introduced to store the personal best performing particle(pbest) , and simulated binary crossover and polynomial mutation were used to produce new particles. In benchmark function, the results demonstrate good performance of the SPDPSO algorithm in solving complex multimodal problems compared with the other algorithms. In practical application, the experimental results show that the SPDPSO algorithm can achieve better solutions that other PSOs.