Abstract :
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
Keywords :
Markov processes; neural nets; simulated annealing; stochastic processes; Markov chains; chemotaxis algorithm; complex optimization problems; discrete variables; genetic algorithm; optimization problems; simulated annealing algorithm; stochastic approach SAGACIA; stochastic optimization algorithm; Artificial neural networks; Automation; Cooling; Cost function; Decoding; Genetic algorithms; Land surface temperature; Simulated annealing; Solid modeling; Stochastic processes;
Journal_Title :
Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on
