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This paper proposes a new particle swarm optimization method that use chaotic maps for parameter adaptation. To enhance the performance of particle swarm optimization, which is an evolutionary computation technique through individual improvement plus population cooperation and competition, a modified particle swarm optimization algorithm is proposed by incorporating chaos(CPSO). Firstly, diversity...
This paper proposes an improved particle swarm-based-simulated annealing method by combine simulate annealing algorithm and swarm particle optimization. An improved annealing schedule is introduced to enhance the performance of particle swarm optimization. The cooling rate is higher at the beginning than at the end of the search process. In this way, the algorithm can explore for solutions in more...
Metaheuristic optimization algorithms have become popular choice for solving complex and intricate problems which are difficult to solve by traditional methods. Particle swarm optimization has shown an effective performance for solving variant benchmark and real-world optimization problems. However, it suffers from premature convergence because of quick losing of diversity. In order to enhance its...
Adaptive mutation is introduced into improved particle swarm optimization to increase the performance of particle swarm optimization algorithms. The mutation probability is adjusted according to the variance of the population's fitness. Nonlinear decreasing strategy is used to adjust the inerita weight and enhance searching ability that can abandon the local optimal solution and find the global one...
Particles can remember some information in an optimization process. They learn by themselves and from other particles, so the next generation can inherit much information from their parents and finally find optimal solutions. But particles are also faced with two problems of stagnating in a local but not global optimum. Genetic algorithms have strong global search ability. Genetic algorithms are combined...
The existing problems of Hopfield neural networks sovling travelling salesman problem are analysed and improved energy function is proposed in this paper. Probablity model is introduced into improved HNNs. Probablity model records the gene information of the best individuals,which can make genetic algorithm search simultaneously in depth and width. An improved genetic hopfield neural networks based...
Although the grey forecasting model has been successfully employed in various fields and demonstraed promising results, its performance still could be improved. In order to improve the fitting capability of grey model, an improved grey neural network model with GA optimization is proposed in this paper. To avoid the premature convergence and inbreeding, an improved GA (IGA) is proposed in this paper...
Probabilistic Neural Network (PNN) was applied to prediction mainly. Over the traditional neural network, less time was cost by PNN in network architecture determining and training. But the smoothing parameter used in the estimation results was a user-defined constant. How to determine this parameter's value is a crucial problem in PNN. Combined with adaptive genetic algorithm (GA), a novel PNN was...
Conventional genetic algorithm is prone to many problems, such as premature convergence, poor performance of partial search, inefficient in the final stage, difficulty in keeping balance between population diversity and selective pressure. In order to resolve these problems, the amount of information from parents was measured with correlation coefficient. Then an alternation strategy based on hereditary...
In connection with the problem that disturbance of voltage and load change will lead to the performance deterioration of the conventional PID control in a DC speed regulation system, a two closed-loop fuzzy PID control method is presented. Through fuzzy logic reference, this method dynamically tunes the three parameters of PID controller adaptively according to the speed error e and its variety rate...
When GA is used to optimize neural networks, two problems need to be solved. One is the inbreeding and gene coding. Another is the balance between selection pressure and population diversity. One-to-one correspondence between the gene coding and functional equivalence class decreases the coding redundancy through normalizing coding of network. Adaptive crossover and mutation probability is proposed...
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