改进型粒子群算法解决多维背包问题

Improved Particle Swarm Algorithm for the Multi-dimensional Knapsack Problem

微粒群优化算法(PSO)是一种基于种群的随机优化技术.将EDA算法与PSO算法结合起来,形成一种新的改进的算法(EPSO).算法将全局统计信息和全局最优解运用于解空间搜索,以期能更有效解决组合优化问题,最后将EPSO算法用于解决多维背包问题并进行了对比仿真实验.实验结果表明,在解决多维背包问题上,EPSO优于传统的PSO算法以及多种启发式智能算法,与此同时,EPSO算法使用更少的参数,因此更容易实现,运行更加稳定,效果更好.

Particle Swarm Optimization algorithm （PSO） is apopulation-based stochastic optimization techniques. In this paper, the EDA algorithm and PSO algorithm combine to form a new improved algorithm （EPSO）. The EPSO algorithms can effectively global statistics and global optimal solution used in the solution space search, the EPSO algorithm for solving the multidimensional knapsack problem. Experimental results show that solving the multidimensional knapsack problem, the EPSO is better than the traditional PSO algorithm, as well as a variety of heuristic intelligent algorithms, and at the same time, the EPSO Algorithm using fewer parameters, and therefore easy to implement, run more stable.