Abstract:To overcome the drawbacks that the chicken swarm algorithm is liable to fall into the local optimum and is slow when solving the high-dimension problems, this paper proposes a hybrid chicken swarm algorithm with dissipative structure and differential mutation. A dissipative structure is adapted in cock position to enlarge the search space of the whole flock, so as to enhance the global searching ability. Meanwhile, differential mutation is applied in some randomly selected individuals to improve the convergence of the chicken swarm algorithm. Numerical experiments conducted on the 18 classical test functions that the proposed algorithm is superior to other algorithms.
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