基于平衡搜索策略的多目标粒子群优化算法*

鉴于平衡全局和局部搜索在多目标粒子群优化算法获取完整均匀Pareto最优前沿方面的重要性,设计平衡全局和局部搜索策略,进而提出改进的多目标粒子群优化算法(bsMOPSO).文中策略在局部搜索方面设计归档集自挖掘子策略,通过对归档集中均匀分布的部分粒子进行柯西扰动,使归档集涵盖整个前沿面的局部搜索.在全局搜索方面设计边界最优粒子引导搜索子策略,以边界最优粒子替换部分粒子的全局最优解,引导粒子向各维目标的边界区域搜索.选取4种对比算法在ZDT和DTLZ系列的部分测试函数上进行实验,结果表明bsMOPSO具有更快的Pareto最优前沿收敛效率和更好的分布性.

Multi-objective Particle Swarm Optimization Algorithm Based on Balance Search Strategy

Considerating the importance of balancing global and local search for multi-objective particle swarm optimization algorithm(MOPSO) to obtain the complete and uniform Pareto front(PF), a balance search strategy is designed and an improved multi-objective particle swarm optimization algorithm (bsMOPSO) is proposed.The strategy is composed of two novel search sub-strategies. In the local search sub-strategy, self-exploitation of archive set is designed to achieve local search involving the entire Pareto front by disturbing fixed ratio of uniform particles in archive set with Cauchy mutation. In the global search sub-strategy, guided search by the best boundary particle is designed through using the optimal boundary particle as the global optimal solution, and therefore more boundary areas of each objective function are searched by part of particle swarm. By comparing five algorithms on the series of ZDT and DTLZ test functions, the results demonstrate that bsMOPSO achieves better Pareto optimal convergence and distribution.