Adaptive Control of Hybrid PSO-APGA using Neural Network for Constrained Real-Parameter Optimization
نویسندگان
چکیده
This paper describes an evolutionary strategy called PSOGA-NN, which uses Neural Network (NN) for selfadaptive control of hybrid Particle Swarm Optimization and Adaptive Plan system with Genetic Algorithm (PSO-APGA) to solve large scale problems and constrained real-parameter optimization. This approach combines the search ability of all optimization techniques (PSO, GA) for stability of convergence to the optimal solution and incorporates concept from neural network for self-adaptive of control parameters. It is shown to be statistically significantly superior to other Evolutionary Algorithms (EAs) on numerical benchmark problems and constrained real-parameter optimization. Keywords—Adaptive Plan, Neural Network, Parallel Genetic Algorithm, Particle Swarm Optimization, Real-parameter
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