一种混合优化算法及其性能

结合遗传算法、粒子群优化算法和免疫算法提出了一种实数编码的混合优化算法(IG-PSOA),该方法利用非线性竞争择优的交叉操作和粒子群进化操作来提高算法的搜索效率,通过免疫选择和募集新成员操作保证种群的多样性,以避免早熟和局部收敛。从理论上分析了算法的收敛性和计算复杂度;用数值试验的方法分析了算法的鲁棒性和参数的取值范围。对7个测试函数的数值试验表明,该算法不仅提高了算法的全局搜索能力,提高了收敛的速度,而且提高了求解的质量和优化结果的可靠性,是一种有潜力的优化方法。

Hybrid Optimization algorithm and its performance

A real-coded hybrid optimization algorithm, IGPSOA, was proposed, which was based on genetic algorithm, particle swarm optimization and immune algorithm. The nonlinear competition and selection methods among several crossover offsprings operator and particle swarm optimization operator were proposed to increase the efficiency of the algorithm. Immune selection and new member creation operators were used to retain the diversity of the population and to avoid premature and local convergence. The convergence and computing complexity were theoretically analyzed. The robustness of the algorithm and the range of the parameters were tested by numerical experiments. Testing experiments on 7 benchmark functions show that IGPSOA not only improves the global optimization performance and quickens the convergence speed but also obtains robust solution with better quality, indicating that it is a promising approach for global optimizations.