CAJ | 학술논문

将遗传算法与混沌算法相结合,提出了一种新颖的基于猫映射的混沌遗传算法(CGA),解释了猫映射的遍历性,分析了猫映射的混沌分布优越性.该算法利用猫映射的初值敏感性扩大搜索范围,利用猫映射的遍历性进行混沌变量的优化搜索,从而减少了数据冗余,保持了种群多样性,有效地解决了局部收敛问题.理论分析和数值仿真表明,该算法具有更好的收敛性能.
장유전산법여혼돈산법상결합,제출료일충신영적기우묘영사적혼돈유전산법(CGA),해석료묘영사적편력성,분석료묘영사적혼돈분포우월성.해산법이용묘영사적초치민감성확대수색범위,이용묘영사적편력성진행혼돈변량적우화수색,종이감소료수거용여,보지료충군다양성,유효지해결료국부수렴문제.이론분석화수치방진표명,해산법구유경호적수렴성능.
The novel Chaos-Genetic Algorithm(CGA) based on the cat map is proposed which combines Genetic Algorithm(GA) and Chaos AIgorithm(CA).This paper explains the ergodicity of the cat map,analyzes the chaotic distributed superiority of the cat map.The algorithm uses the initial sensitivity of the eat map to expand the scope of the search,and uses the ergodicity of the cat map to search the chaotic variables.Thus,the data redundancy is reduced,the diversity of population is maintained,and the problem of local optimum is effectively solved.Theoretical analysis and numerical simulation demonstrate that CGA has better convergence performance.