利用种群平均信息的二进制粒子群优化算法

在Kennedy和Eberhart的二进制粒子群优化算法(BPSO)的基础上提出一种利用种群平均信息的二进制粒子群优化算法。新算法利用种群个体极值的平均信息和粒子的个体极值决定粒子当前取值的概率,使粒子可以充分利用整个种群的信息。通过测试函数优化和0—1背包问题,结果表明该算法具有较好的收敛速度和稳定性,求解结果要优于BPSO和一些改进算法。

Binary Particle Swarm Optimization Using Average Information of Swarm

Particle swarm optimization using average information of swarm is presented based on Kennedy and Eberhart’s binary particle swarm optimization.The probability of the position vector depends on the values of the average individual best position and the individual best position.The new algorithm can makes each particle use the whole swarm information effectively.The test of function optimization and 0—1 knapsack problem show that this new algorithm has a good speed of convergence,a high stability and performs better than BPSO and some modified algorithms.