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Evolving cognitive and social experience in Particle Swarm Optimization through Differential Evolution: A hybrid approach

<22> This paper proposes a new, simple, and general memetic framework to hybridise two well-known families of stochastic search algorithms, the Particle Swarm Optimisation and Differential Evolution. It exploits the dynamics and the behaviour of the algorithms and efficiently combines them with a procedure that promotes cooperation and coevolution of its elements. Its novelty is based on the simple and widely applicable memetic framework and this has resulted in new memetic/hybrid algorithms with significant performance gains. This paper has attracted major research attention, as evidenced by the high citation count.