实数遗传算法的改进及性能研究

提出一种粒子群优化方法(PSO)与实数编码遗传算法(GA)相结合的混合改进遗传算法(HIGAPSO).该方法采用混沌序列产生初始种群、非线性排序选择、多个交叉后代竞争择优和变异尺度自适应变化等改进遗传操作;并通过精英个体保留、粒子群优化及改进遗传算法(IGA)三种策略共同作用产生种群新个体,来克服常规算法中收敛速度慢、早熟及局部收敛等缺陷.通过四个高维典型函数测试结果表明该方法不但显著提高了算法的全局搜索能力,加快了收敛速度;而且也改善了求解的质量及其优化结果的可靠性,是求解优化问题的一种有潜力的算法.

Improvement of Real-valued Genetic Algorithm and Performance Study

A new evolutionary learning algorithm(HIGAPSO) based on a hybrid of real-code genetic algorithm(GA) and particle swarm optimization(PSO) is proposed in this paper.In this hybrid algorithm some improved genetic mechanisms,for example initial population produced by chaos sequence,non-linear ranking selection,competition and selection among several crossover offspring and adaptive change of mutation scaling are adopted;also the new population is produced through three approaches,i.e.elitist strategy,PSO strategy and the improved genetic algorithm(IGA) strategy.Through testing four benchmark functions with large dimensionality,the experimental results show that this new algorithm not only improves the global optimization performance and quickens the convergence speed,but also obtains robust results with good quality,which indicates it is a promising approach for solving global optimization problems.