CAJ | 학술논문

差分进化算法求解复杂优化问题时，由于进化后期种群多样性降低，算法极易陷入局部最优值无法跳出。论文针对该问题，将差分进化算法和混沌优化方法耦合，构建了混沌差分进化算法。算法利用混沌序列的遍历性和内部迭代的随机性，弥补差分进化算法容易陷入局部最优的缺陷，从而提高算法的搜索性能。对几种典型函数的测试结果表明：混沌差分进化算法的全局搜索性能有了显著提高，能有效避免算法陷入局部最优。因此，与标准差分进化算法和混沌优化算法相比，该算法在求解复杂优化问题时更加可行、有效。
차분진화산법구해복잡우화문제시，유우진화후기충군다양성강저，산법겁역함입국부최우치무법도출。논문침대해문제，장차분진화산법화혼돈우화방법우합，구건료혼돈차분진화산법。산법이용혼돈서렬적편력성화내부질대적수궤성，미보차분진화산법용역함입국부최우적결함，종이제고산법적수색성능。대궤충전형함수적측시결과표명：혼돈차분진화산법적전국수색성능유료현저제고，능유효피면산법함입국부최우。인차，여표준차분진화산법화혼돈우화산법상비，해산법재구해복잡우화문제시경가가행、유효。
When differential evolution algorithm is used in solving the complex optimization problems, diversity of species is decreased in the later evolution period, therefore the algorithm can easily fall into local optimum. A novel chaos differential evolution algorithm based on the differential evolution and chaos optimization algorithm, which made use of the ergodicity and internal randomness of chaos iterations, was presented to overcome the defect of premature local optimum and enhance the global searching capacity of differential evolution with that of powerful local searching capacity of the chaos optimization algorithm. The experimental results indicated that the chaos differential evolution algorithm could improve the global searching capacity significantly and avoid falling into local optimum. Thus, the proposed approach was more feasible and effective in solving the complex optimization problem compared with differential evolution and chaos optimization algorithm.