The Study of Improved Chicken Swarm Optimization Algorithm based on Simulated Annealing

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 introduced, put forward an improved chicken swarm optimization algorithm with the random inertia weight and fixed learning factors to deal with the chicken particles into local optimum easily and can't obtain the global optimal solution in the chicken swarm optimization algorithm, then using simulated annealing algorithm search in the field for the optimal solution when chicken swarm optimization to a standstill, makes the algorithm has the capacity of jumping out of local optimal to obtain the global optimal solution, finally, the improved chicken swarm optimization algorithm based on simulated annealing was tested by four classic functions. The simulation results show that the proposed algorithm have strong capability of global search, high convergence precision and fast convergence speed, the proposed algorithm whose performance of global searching was superior to particle swarm optimization, chicken swarm optimization and improved chicken swarm optimization.