基于双决策和快速分层的多目标粒子群算法

针对粒子群算法在求解多目标优化问题时存在的收敛性不足和多样性缺失等问题,提出一种基于双决策和快速分层的新型多目标粒子群算法(DDFSMOPSO);在该算法中,采用外部存档对迭代产生的非劣解进行存储,并利用拥挤距离和绝对距离相结合的双决策策略对外部存档规模进行维护,使得优秀粒子在随后的进化过程中易于保留和发展;同时,采用...

Multi objective Particle Swarm Optimization Based on Double Decision and Fast Stratification

In order to solve the problems of insufficient convergence and diversity in multi objective particle swarm optimization, a new multi objective particle swarm optimization based on double decision and fast stratification(DDFSMOPSO) is proposed. In this algorithm, the external archive is used to store the non inferior solutions generated by iteration, and the double decision strategy combining crowding distance and absolute distance is used to maintain the external archive scale, which makes the excellent particles easy retained and developed in the subsequent evolution process. At the same time, the fast stratification strategy is used to select the global learning samples from the external archiving, which can be used to lead the evolution of particles in the population and promote the particles in the population to move to the real Pareto front. DDFSMOPSO algorithm and three classical multi objective particle swarm optimization are compared in simulation experiment on some test functions of ZDT and DTLZ series. Experimental results show that compared with other classical algorithms, DDFSMOPSO algorithm has better convergence and diversity. Therefore, DDFSMOPSO can be used as an effective algorithm for solving multi objective optimization problems.