Papers by Author: Yong Quan Zhou

Abstract: Focused on the disadvantages of some current constrained optimization algorithm, glowworm swarm multi-objective optimization algorithm ( GSMOA） is proposed in this paper. The main character of this algorithm conforms to feasibility rules and adapts self- adaptive penalty function to search feasible solutions. This algorithm has been tested on 4 standard functions and it shows that the proposed algorithm has more advantage in the convergence rate and the solution precision.

Abstract: This paper presents a new hybrid mean particle swarm optimization algorithm with improved NEH heuristic approach and local search strategies by using an immune mechanism. This hybrid mean particle swarm optimization algorithm is used for permutation flow shop scheduling problems. Finally, twenty-five problems are used to test the performance of the algorithm, the experimental results show that the proposed approach is an effective and practical.

Abstract: In this paper, an improved particle swarm optimization-ant colony algorithm (PSO-ACO) is presented by inserting delete-crossover strategy into it for the shortcoming which PSO-ACO can’t solve the large-scale TSP. The experiments results show that the PSO-ACO has better performance than ant colony algorithm (ACO) on searching the shortest paths, error and robustness for the TSP.

Abstract: In this paper, an artificial glowworm swarm optimization algorithm for solving 0-1 knapsack problem is proposed, and the detailed realization of the algorithm is illustrated. According to intelligent algorithm for knapsack problem, the question of sensitive parameter’s choice is avoided under the greed idea. Simulation results show that the artificial glowworm swarm optimization algorithm for solving 0-1 knapsack problems is feasible and effective.

Abstract: In this paper, a novel chaotic cultural-based particle swarm optimization algorithm (CCPSO) is proposed for constrained optimization problems by employing cultural-based particle swarm optimization (CPSO) algorithm and the notion of chaotic local search strategy. In the CCPSO, the shortcoming of cultural-based particle swarm optimization (CPSO) that it is easy to trap into local minimum be overcome, the chaotic local search strategy is introduced in the influence functions of cultural algorithm. Simulation results based on well-known constrained engineering design problems demonstrate the effectiveness, efficiency and robustness on initial populations of the proposed method.