Chaos small-world optimal algorithm based on population entropy

To avoid trapping into local minimum and improve searching efficiency of local short-range operator during the function optimization, a chaos small-world optimal algorithm based on population entropy is presented. The individual density, constructed according to the information entropy, and fitness are taken as the evaluation criterion, and the individuals of high density are replaced by new initial individuals, which achieve the self-adjustability and diversity of population. The characteristics of ergodicity and randomness of chaotic variables are considered to produce the initial population with logistic mapping, and the individual local search is performed by chaos disturbance after local short-range search, thus the searching efficiency and accuracy are obviously heightened. The simulation results show that the proposed algorithm remarkably improved the searching capacity and efficiency in small-world algorithm.