CAJ | 학술논문

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.
침대차이연화산법구해복잡우화문제효솔불고문제,제출일충개진적차이연화산법.해산법채용단충군궤제제고전국수색능력,이용이차국부변이조작사당전충군중적부분개체재당전최우개체부근심우,증강국부수색능력.이용불동류형적표준측시함수대해산법진행측시,병여차이연화산법、동태차이연화산법화입자군우화산법진행비교.방진결과표명,개진적차이연화산법현저제고료수색효솔.
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.