czzc829z的博客

A simulated annealing based niche genetic algorithm (SANGA) has been presented to strength the optimization ability of niche genetic algorithm (NGA). The improved idea is to define niche formation using probability condition rather than simply distance condition. Individuals who only have close neighbors are inclined to build up niche; individuals who only have far neighbors are likely to depart from niche. The feasibility and validity of the proposed method is proved by the contrast between current NGA based on penalty, NGA based on fitness sharing, NGA based on deterministic crowding and SANGA in some simulation experiments and applications of 0-1 knapsack problem.

An improved particle swarm optimization (PSO) algorithm is designed for the grid based wireless homo-sensor network position problem. The proposed method, called guided method, introduces the simulation of migration process to PSO and its mutation algorithm, using a previous designed sparse position plan to guide the swarm to the optimization solution, and accelerates the search process. Experiments show not only the feasibility and validity of the proposed method but also a marked improvement in performance over traditional PSO.