Earticle

In order to improve the global searching ability of differential evolution algorithm in solving complex optimization problem, an improved differential evolution (SMDE) algorithm based on the self-adaptive method and multi-population is proposed in this paper. In the proposed SMDE algorithm, the population is divided into multi-populations in order to keep the diversity, then the self-adaptive method is used to control the parameters of differential evolution algorithm in order to balance the local search and global search ability. Finally, several complex benchmark functions are selected to validate the efficiency of the SMDE algorithm. The experiment results show that the proposed SMDE algorithm is better at the global convergence ability and the searching precision.

Particle swarm optimization (PSO) algorithm is a population-based search algorithm by simulating the social behavior of birds within a flock. It is a simple and efficient optimization algorithm. But it exists the low computational speed and easy falling into local optimal solution in solving the complex problem. So the quantum theory, adaptive inertia weight, disturbance factor and diversity mutation strategy are introduced into the PSO algorithm in order to propose an improved PSO(IWDMDQPSO) algorithm in this paper. In the IWDMDQPSO algorithm, the quantum theory is used to change the updating mode of the particles for guaranteeing the simplification and effectiveness of the algorithm. The adaptive inertia weight is used to improve the premature convergence of the algorithm. The disturbance factor is used to avoid the premature of the algorithm. The diversity mutation strategy is used to improve the global searching ability and computation speed. Finally, the famous benchmark functions are selected to prove the performance and effectiveness of the proposed IWDMDQPSO algorithm. The experiment results show that the proposed IWDMDQPSO algorithm takes on better solving accuracy and higher computation speed in solving the complex function. So it has a remarkable optimization performance.

Ant colony optimization (ACO) algorithm is a new heuristic algorithm which has been demonstrated a successful technology and applied to solving complex optimization problems. But the ACO exists the low solving precision and premature convergence problem, particle swarm optimization (PSO) algorithm is introduced to improve performance of the ACO algorithm. A novel hybrid optimization (HPSACO) algorithm based on combining collaborative strategy, particle swarm optimization and ant colony optimization is proposed for the traveling salesman problems in this paper. The HPSACO algorithm makes use of the exploration capability of the PSO algorithm and stochastic capability of the ACO algorithm. The main idea of the HPSACO algorithm uses the rapidity of the PSO algorithm to obtain a series of initializing optimal solutions for dynamically adjusting the initial pheromone distribution of the ACO algorithm. Then the parallel search ability of the he ACO algorithm are used to obtain the optimal solution of solving problem. Finally, various scale TSP are selected to verify the effectiveness and efficiency of the proposed HPSACO algorithm. The simulation results show that the proposed HPSACO algorithm takes on the better search precision, the faster convergence speed and avoids the stagnation phenomena.

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.