Improved chicken swarm optimization

Considering the problem that the original chicken swarm optimization algorithm is easy to fall into local optimum because of premature convergence for high-dimensional complex problems, an improved chicken swarm optimization was proposed. In this algorithm, the part of chicks learning from the rooster in their subgroup is added to chick´s position update equation, and the inertia weight and learning factor are also introduced. Then eight benchmark functions are used to test the proposed algorithm and the comparison with particle swarm optimization, bat algorithm, and original chicken swarm optimization are also performed. The simulated experimental results showed that the proposed algorithm is able to avoid premature convergence and therefore can escape from local optimum. Especially, the proposed method outperforms other evolutionary algorithms in finding the global optimum for high-dimensional problems.