Global optimization through randomized group search in contracting regions
Chao Yu, Dipti Srinivasan, Qing‐Guo Wang · 2016
This paper proposes a new method for global optimization through randomized group search in contracting regions. For each iteration, a population is randomly produced within the search region, where the population size is chosen to ensure that the empirical optimum is an estimate of the true optimum within a predefined accuracy with a certain confidence. Fitness values are evaluated at the samples in the population. A very small subset of them with top-ranking fitness values are selected as good points. Neighborhoods of these good points are used to form a new and smaller search region, in which a new population is generated. It is easy to implement the algorithm. Extensive simulation on benchmark problems shows that the proposed method is fast and reasonably accurate.