摘要： 针对差异演化算法求解复杂优化问题效率不高问题，提出一种改进的差异演化算法。该算法采用单种群机制提高全局搜索能力，利用二次局部变异操作使当前种群中的部分个体在当前最优个体附近寻优，增强局部搜索能力。利用不同类型的标准测试函数对该算法进行测试，并与差异演化算法、动态差异演化算法和粒子群优化算法进行比较。仿真结果表明，改进的差异演化算法显著提高了搜索效率。
关键词:
全局优化,
差异演化,
二次局部变异操作
Abstract: An improved Differential Evolution(DE) algorithm is proposed to improve the convergence speed of the traditional DE in solving the complex optimization problems. In the new algorithm, only one array is used to improve the exploration ability. And a second local mutation operator is proposed to improve the exploration ability, which makes some individuals of the current population search the field around the current best individual. Several different kinds of benchmark functions are used to test the algorithm. And the results are compared with that of DE algorithm, of Dynamic Differential Evolution(DDE) and Particle Swarm Optimization(PSO). Simulation results show that the efficiency of the improved differential algorithm is improved greatly.
Key words:
global optimization,
Differential Evolution(DE),
second local mutation operation
中图分类号:
邓长寿;赵秉岩;梁昌勇. 改进的差异演化算法[J]. 计算机工程, 2009, 35(24): 194-195.
DENG Chang-shou; ZHAO Bing-yan; LIANG Chang-yong. Improved Differential Evolution Algorithm[J]. Computer Engineering, 2009, 35(24): 194-195.