Abstract :
This paper presents a new stochastic optimization algorithm based on a simulated annealing algorithm (SAA), genetic algorithm (GA), and chemotaxis algorithm (CA) which is called SAGACIA. It can be used to solve some complicated optimization problems. SAGACIA integrates some advantages of SAA, GA and CA. It can not only easily escape from local minima, but also converge quickly. Good solutions can be obtained in a short time. SAGACIA has been applied to solve some practical problems, such as scheduling problems, training artificial neural networks, and so on. In all the test cases, the performance of SAGACIA is better than SAA, GA and CA
Keywords :
convergence; genetic algorithms; learning (artificial intelligence); neural nets; problem solving; scheduling; simulated annealing; stochastic programming; SAGACIA; artificial neural networks; chemotaxis algorithm; complex problems; convergence; genetic algorithm; local minima; performance; scheduling problems; simulated annealing; stochastic optimization algorithm; training; Artificial neural networks; Cost function; Electrical engineering; Genetic algorithms; Genetic mutations; Processor scheduling; Simulated annealing; Stochastic processes; Temperature; Testing;
