Cultural Particle Swarm Algorithms for Constrained Multi-objective Optimization

In this paper, we propose to integrate particle swarm optimization algorithm into cultural algorithms frame to develop a more efficient cultural particle swarm algorithms (CPSA) for constrained multi-objective optimization problem. In our CPSA, the population space of cultural algorithms consists of n+1 subswarms which are used to search for the n single-objective optimums and an additional multiobjective optimum. The belief space accepts 20% elite particles form each subswarm and further takes crossover to create Pareto optimums. Niche Pareto tournament selection is further executed to ensure Pareto set to distribute uniformly along Pareto frontier. Additional memory of Pareto optimums spool is allocated and updated in each iteration to keep resultant Pareto solutions. Besides, a direct comparison method is employed to handle constraints without needing penalty functions. Two examples are presented to demonstrate the effectiveness of the proposed algorithm.

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