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首页> 外文期刊>Systems and Computers in Japan >The Niching Method for Obtaining Global Optima and Local Optima in Multimodal Functions
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The Niching Method for Obtaining Global Optima and Local Optima in Multimodal Functions

机译：多峰函数中获得全局最优和局部最优的小生境方法

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摘要

Sometimes it is desirable to know the secondary candidates as well as the global optima of multimodal functions. The Deterministic Crowding (DC) method is an effective type of genetic algorithm for discovering multiple global optima, but it has difficulties discovering local optima. An improvement on this method called "Dispersing Deterministic Crowding" (DDC) is therefore proposed which encourages dispersion of individuals and creation of species within the population in order to increase the discovery of local optima, as well as of global optima. A method for preferential identification of solutions with a fitness exceeding some demanded level is also developed. The performance of DDC is compared with those of other niching methods, DC, sharing, RTS, GA with tabu search, and the immune algorithm, to show its effectiveness.
机译：有时，希望了解二级候选者以及多峰函数的全局最优性。确定性拥挤（DC）方法是一种有效的遗传算法，可用于发现多个全局最优解，但很难发现局部最优解。因此，提出了对这种称为“分散确定性拥挤”（DDC）的方法的改进，该方法鼓励个体分散和种群内物种的创造，以便增加对局部最优和全局最优的发现。还开发了一种适用性超过某些需求水平的优先识别解决方案的方法。将DDC的性能与其他禁忌方法（DC，共享，RTS，带有禁忌搜索的GA）和免疫算法的性能进行比较，以证明其有效性。

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