基于二进制交叉和变异的粒子群算法及应用

粒子群算法在求解多峰问题时极易陷入局部最优解，提出了基于模拟二进制交叉和多项式变异的粒子群算法(sPDPSO>。在该算法中，为了更好地利用每个粒子的历史信息，引入了外部存档存储每个粒子的最优位置( pbest) ;同时，对外部存档中的pbest进行二进制交叉，而对新产生的全局最优粒子进行多项式变异。基准函数的测试结果显示，SPDPS()算法在求解多峰问题上有一定的优势。在实际应用中，以TSP为研究对象，结果显示SPDPSO算法获得了比其它算法更好的解。

Particle Swarm Optimizer with Simulated Binary Crossover and Polynomial Mutation and its Application

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.