一种极坐标下的改进多目标粒子群优化

对现有多目标粒子群优化算法的全局最优解选择机制进行分析,指出其不足。在此基础上设计一种全新的极坐标下的选择机制:利用极坐标下解和粒子的角度信息计算适应度角度,选择适应度角度最大的解作为粒子的全局最优解。并针对多目标粒子群优化算法在迭代后期收敛变慢的问题改进位置更新公式:将位置更新过程产生的中间点也作为粒子新位置的候选解,有效提高算法收敛速度。对测试函数的仿真试验表明,所提出的改进算法在解集的分布性和收敛性上较其它典型算法有明显提高。

Improved Multi-objective Particle Swarm Optimization in Polar Coordinates

Representative global best solution selecting strategies for Multi-objective Particle Swarm Optimization was analyzed first.Defects of these strategies were pointed out.A new selecting strategy in polar coordinates was proposed.The new strategy used inclination angles of particles and solutions to calculate "fitness angle".Solution with maximum fitness angle was selected as particle’s global best.To overcome slowdown of convergence during MOPSO’s later iterations,a new position update equation was also designed: the relay points produced during position updating were selected as the candidates for new position.The new position update equation helps to improve convergence speed.Results on test functions show that the proposed algorithm can improve the distribution and convergence performance effectively for MOPSO algorithms.