In allusion to the deficiencies of the ant colony optimization algorithm for solving the complex problem, the genetic algorithm is introduced into the ant colony optimization algorithm in order to propose a novel hybrid optimization (NHGACO) algorithm in this paper. In the NHGACO algorithm, the genetic algorithm is used to update the global optimal solution and the ant colony optimization algorithm is used to dynamically balance the global search ability and local search ability in order to improve the convergence speed. Finally, some complex benchmark functions are selected to prove the validity of the proposed NHGACO algorithm. The experiment results show that the proposed NHGACO algorithm can obtain the global optimal solution and avoid the phenomena of the stagnation, and take on the fast convergence and the better robustness.

In order to improve the problem of premature convergence and computational efficiency of traditional differential evolution algorithm in solving high-dimensional problems, an improved differential evolution (HMSDE) algorithm based on combing elite synergy strategy, multi-population strategy and dynamic adaptive strategy is proposed in this paper. In the proposed HMSDE algorithm, the population is dynamically divided into multi-populations in order to keep the diversity of the population, elite synergy strategy is used to achieve information exchange among different sub-populations, and dynamic adaptive strategy is used to dynamically control the parameter values of scaling factor and crossover factor in order to improve the stability and robustness of the HMSDE algorithm. In order to test the performance of the HMSDE algorithm, a set of 10 benchmark functions are selected in here. The results show that the HMSDE algorithm takes on remarkable optimized ability, faster convergence speed and higher search accuracy. And the HMSDE algorithm can avoid the premature convergence and outperforms several state-of-the-art performances.

For the premature convergence and initial pheromone distribution problem of ant colony optimization algorithm, an improved particle swarm optimization (MPSO) algorithm is introduced into ant colony optimization algorithm in order to propose a novel hybrid evolution optimization (HEACO) algorithm in this paper. In the proposed HEACO algorithm, the ergodicity of the chaos is used to initialize the swarm in order to enhance the diversity of the particle swarm, and adjust the mutation probability and inertia weighting factor in order to improve the capability of local and global search.