Evolutionary computation: toward a new philosophy of machine intelligence
Evolutionary computation: toward a new philosophy of machine intelligence
Comparison between Genetic Algorithms and Particle Swarm Optimization
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
Nonlinear inertia weight variation for dynamic adaptation in particle swarm optimization
Computers and Operations Research
A multi-objective genetic local search algorithm and itsapplication to flowshop scheduling
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Multiobjective programming using uniform design and genetic algorithm
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients
IEEE Transactions on Evolutionary Computation
Parameter identification of chaotic dynamic systems through an improved particle swarm optimization
Expert Systems with Applications: An International Journal
Parameter estimation of bilinear systems based on an adaptive particle swarm optimization
Engineering Applications of Artificial Intelligence
Intelligent identification and control using improved fuzzy particle swarm optimization
Expert Systems with Applications: An International Journal
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A particle swarm optimization method with nonlinear time-varying evolution based on neural network (PSO-NTVENN) is proposed to design large-scale passive harmonic filters (PHF) under abundant harmonic current sources. The goal is to minimize the cost of the filters, the filters loss, and the total harmonic distortion of currents and voltages at each bus, simultaneously. In the PSO-NTVENN method, parameters are determined by using a sequential neural network approximation. Meanwhile, based on the concept of multi-objective optimization, how to define the fitness function of the PSO to include different performance criteria is also discussed. To show the feasibility of the proposed method, illustrative examples of designing optimal passive harmonic filters for a chemical plant are presented.