Research on distributed clustering algorithm Weka4WS-based in grid environment
网格环境下基于Weka4WS的分布式聚类算法*

A grey particle swarm optimization algorithm based on TOPSIS for solving multi-objective optimization problems, which proposed by taking advantage of technique for order preference by similarity to ideal solution (TOPSIS) trace Pareto front for distance and grey correlation degree distinguish similarity between curves of non-inferior solution sets and curves of Pareto front solution sets. The algorithm took advantage of TOPSIS theory and grey correlation degree theory to acquire relative fitness coefficient and gray correlation coefficient, and defined their sum as relatively ideal degree, which distinguished advantages and disadvantages of particles and determines individual extreme and global extreme. Validated the algorithm using four different types benchmark cases. The experimental results show that grey particle swarm optimization based on TOPSIS, compared with objective weighting method and grey PSO algorithms, it can find many Pareto optimal solutions distributed onto the Pareto front and do not increase the complexity of the algorithm.