求解约束优化问题的中心引力优化算法及其工程应用

为了平衡算法的全局探测能力和局部搜索能力,提出一种基于交叉与变异的中心引力优化算法用于求解约束优化问题。该算法首先利用佳点集方法构造初始种群以保证粒子的多样性。以一定概率随机选择粒子与当前最优粒子进行算术交叉操作,引导粒子向全局最优解靠拢。对当前最优粒子进行多样性变异以避免算法陷入局部最优。标准测试函数和工程优化应用问题的实验结果表明,新算法能有效求解不同的约束优化问题。

Central force optimization algorithm for constrained optimizationproblems and its engineering applications

In order to balance the abilities of global detective and local search, this paper proposed a modified central force optimization (MCFO) algorithm based on crossover and mutation for solving constrained optimization problems. The proposed algorithm constructed the initial population by using the principles of the good point set method was to strengthen the diversity of particles. The optimal particle and the randomly selected particle are carried out arithmetic crossover operator which led gradually the population to the global optimum. It utilized diversity mutation operator to avoid the premature convergence, and tested two well-known benchmark functions and two engineering optimization application problems. The results demonstrate that the MCFO algorithm is an effective method for differential constrained optimization problems.