Intelligent Optimization Algorithm Integrated With Parameter Fuzzy Control And Its Application

Some factors have directly effect on the performance of intelligent optimization algorithms. For example, whether the algorithm parameters are appropriate, or whether the algorithm parameters are adjusted to obtain adaptive value during the optimization process. Fuzzy logic, which is a kind of self-adaptation adjust tactics with a prior knowledge of experts, is employed for adjusting the algorithm parameters. This article will propose two types of adaptive intelligent optimization algorithm based on fuzzy control of the algorithm parameters (i.e. genetic algorithms and particle swarm optimization). In order to adjust algorithm parameters, the rules, which integrate a priori knowledge, are used. Results of the simulation show that optimization performance is improved.A novel fuzzy-based adaptive genetic algorithm (FAGA), which has adaptive control strategies for parameters of genetic algorithms, is proposed. In FAGA, crossover probability and mutation probability are adjusted dynamically by fuzzy inferences, which are based on the heuristic fuzzy relationship between algorithm performances and control parameters. And, immune theory is used to improve the selection operation of GA and increase diversity. The experiments show that FAGA can efficiently overcome shortcomings of GA, i.e. premature and slow, and obtain the better results than two typical fuzzy GAs. Finally, the result is satisfactory when the algorithm is used for the parameters estimation of reaction dynamics model. The optimal value obtained by FAGA is better than Powell and other two improved genetic algorithm.A new fuzzy-based adaptive Particle Swarm Optimization algorithm (FPSO), which uses Fuzzy Logic Controller to control inertia weight, and adds random velocity to increase the probability of global optimization when the algorithm falls into the local convergence, is proposed. Experimental results show that FPSO effectively overcomes slow and premature in unconstrained optimization problem, and has better ability to find the global optimum than the standard PSO. This paper also proposes a self adaptive penalty function for solving constrained optimization problems by applying FPSO. And, the proposed penalty function has the great ability for the optimized problem, which not just has more equality constraints but also more equality constraints. Experimental results demonstrate that FPSO integrated with the proposed penalty function has better performance than other three kinds of penalty function for solving the constrained problem. Finally, FPSO integrated with the proposed penalty function was used to optimize the water system, and the satisfactory result was obtained.