A Particle Swarm Optimization Algorithm with Rich Social Cognition

A particle swarm optimization with rich social cognition is developed for solving the premature convergence of particle swarm optimization. In this algorithm, the optimum from the particles’ experiments is determined by learning probability and selective probability. The learning probability is adjusted to balance between the personal cognition and the social cognition. Experimental results for complex function optimization show this algorithm improves the global convergence ability and efficiently prevents the algorithm from the local optimization and early maturation.

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