基于搜索空间自适应分割的多目标粒子群优化算法

提出一种基于搜索空间自适应分割的多目标粒子群优化算法, 根据粒子的搜索能力和规模与子搜索空间的体积呈多维标准正态分布变换, 精细分割搜索空间, 向划分出的子搜索空间分布粒子实现优化, 分割在迭代时持续进行, 直至获得最优解集. 实验结果表明: 该方法解决了多目标粒子群优化算法易陷入局部极值的问题; 在反向世代距离性能指标上, 该算法与一些典型的多目标粒子群优化算法相比, 其种群多样性和解的收敛性优势显著.

Multi objective Particle Swarm Optimization Algorithm Based on Self-adaption Partition of Search Space#br#

We proposed a multi objective particle swarm optimization algorithm based on self adaption partition of searching space, according tothe search capability and scale of particles, it was transformed into a multi dimensional standard normal distribution with the volume of subsearch space, which finely partitioned search spaces and optimized the distribution of particlesin the divided subsearch spaces, and the partition continued during iteration until an optimal solution set was obtained. The experimental results show that the algorithm effectively solves the problem that multi objective particle swarmoptimization algorithm is easy to fall into local extremum, compared withsome typical multi objective particle swarm optimization algorithms,the algorithm has significant advantages in diversity of population and convergence of solution on the performance index of inverted generational distance.