Daneshyari Moayed, Yen Gary G
Department of Electrical and Computer Engineering, Oklahoma State University, Stillwater, OK 74075, USA.
IEEE Trans Syst Man Cybern B Cybern. 2011 Apr;41(2):553-67. doi: 10.1109/TSMCB.2010.2068046. Epub 2010 Sep 9.
Multiobjective particle swarm optimization (MOPSO) algorithms have been widely used to solve multiobjective optimization problems. Most MOPSOs use fixed momentum and acceleration for all particles throughout the evolutionary process. In this paper, we introduce a cultural framework to adapt the personalized flight parameters of the mutated particles in a MOPSO, namely momentum and personal and global accelerations, for each individual particle based upon various types of knowledge in "belief space," specifically situational, normative, and topographical knowledge. A comprehensive comparison of the proposed algorithm with chosen state-of-the-art MOPSOs on benchmark test functions shows that the movement of the individual particle using the adapted parameters assists the MOPSO to perform efficiently and effectively in exploring solutions close to the true Pareto front while exploiting a local search to attain diverse solutions.
多目标粒子群优化（MOPSO）算法已被广泛用于解决多目标优化问题。大多数MOPSO在整个进化过程中对所有粒子使用固定的动量和加速度。在本文中，我们引入了一种文化框架，根据“信念空间”中的各种知识，即情境知识、规范知识和地形知识，为MOPSO中变异粒子的个性化飞行参数（即动量、个体加速度和全局加速度）进行调整，以适应每个单独的粒子。在基准测试函数上，将所提出的算法与选定的先进MOPSO进行的全面比较表明，使用调整后的参数的单个粒子的移动有助于MOPSO在探索接近真实帕累托前沿的解时高效且有效地执行，同时利用局部搜索来获得多样化的解。
