文献类型：期刊文献

英文题名：A novel stochastic optimization algorithm

作者：Li, B; Jiang, WS

机构：[1]Tangshan Univ, Automat Dept, Tangshan 063000, Peoples R China;[2]E China Univ Sci & Technol, Res Inst Automat, Shanghai 200237, Peoples R China

年份：2000

卷号：30

期号：1

起止页码：193

外文期刊名：IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS

收录：;WOS:【SCI-EXPANDED(收录号:WOS:000086068000019)】；

语种：英文

外文关键词：artificial neural network; scheduling; stochastic optimization algorithm

摘要：This paper presents a new stochastic approach SAGACIA based on proper integration of simulated annealing algorithm (SAA), genetic algorithm (GA), and chemotaxis algorithm (CA) for solving complex optimization problems. SAGACIA combines the advantages of SAA, GA, and CA together. It has the following features: 1) it is not the simple mix of SAA, GA, and CA; 2) it works from a population; 3) it can be easily used to solve optimization problems either with continuous, variables or with discrete variables, and it does not need coding and decoding,; and 4) it can easily escape from local minima and converge quickly Good solutions can be obtained in a very short time. The search process of SAGACIA can be explained with Markov chains. In this paper, it is proved that SAGACIA has the property of global asymptotical convergence. SAGACIA has been applied to solve such problems as scheduling, the training of artificial neural networks, and the optimizing of complex functions. In all the test cases, the performance of SAGACIA is better than that of SAA, GA, and CA.