Cultural MOPSO: A cultural framework to adapt parameters of multiobjective particle swarm optimization

Multiobjective particle swarm optimization algorithms (MOPSO) have been widely used to solve multiobjective optimization problems. Most of MOPSOs use fixed momentum and acceleration for all particles throughout the evolutionary process. In this paper, we introduce a cultural framework to adapt the flight parameters of the MOPSO namely momentum, personal, and global acceleration for each individual particle based upon the various types of knowledge in belief space, specifically situational knowledge, normative knowledge, and topographical knowledge. Movement of the particles using the adapted parameters helps the MOPSO to perform efficiently and effectively in multiobjective optimization.

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