Adaptive Isolation Model using Data Clustering for Multimodal Function Optimization


In this paper, we propose a GA model called Adaptive Isolation Model(AIM), for multimodal optimization. It uses a data clustering algorithm to detect clusters in GA population, which identifies the attractors in the fitness landscape. Then, subpopulations which makes-up the clusters are isolated and optimized independently. Meanwhile, the region of the isolated subpopulations in the original landscape are suppressed. The isolation increases comprehensiveness, i.e., the probability of finding weaker attractors, and the overall efficiency of multimodal search. The advantage of the AIM is that it does not require distance between the optima as a presumed parameter, as it is estimated from the variance/covariance matrix of the subpopulation.
Further, AIM's behavior and efficiency is equivalent to basic GA in unimodal landscape, in terms of number of evaluation. Therefore, it is applied recursively to all subpopulations until they converge to a suboptima. This makes AIM suitable for locally-multimodal landscapes, which have closely located attractors that are difficult to distinguish in the initial run.
The performance of AIM is evaluated in several benchmark problems and compared to iterated hill-climbing methods.