Empirical model-building and response surface
Empirical model-building and response surface
Response surfaces: designs and analyses
Response surfaces: designs and analyses
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Enhanced simulated annealing for globally minimizing functions of many-continuous variables
ACM Transactions on Mathematical Software (TOMS)
Swarm intelligence
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Response Surface Methodology: Process and Product in Optimization Using Designed Experiments
Response Surface Methodology: Process and Product in Optimization Using Designed Experiments
Hybrid methods using genetic algorithms for global optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A hybrid approach to modeling metabolic systems using a geneticalgorithm and simplex method
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A restarted and modified simplex search for unconstrained optimization
Computers and Operations Research
Digital redesign of uncertain interval systems via a hybrid particle swarm optimiser
International Journal of Innovative Computing and Applications
Boid particle swarm optimisation
International Journal of Innovative Computing and Applications
Simulation optimization based on Taylor Kriging and evolutionary algorithm
Applied Soft Computing
Gaussian variable neighborhood search for continuous optimization
Computers and Operations Research
Generalized dynamical fuzzy model for identification and prediction
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
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This paper integrates Nelder-Mead simplex search method (NM) with genetic algorithm (GA) and particle swarm optimization (PSO), respectively, in an attempt to locate the global optimal solutions for the nonlinear continuous variable functions mainly focusing on response surface methodology (RSM). Both the hybrid NM-GA and NM-PSO algorithms incorporate concepts from the NM, GA or PSO, which are readily to implement in practice and the computation of functional derivatives is not necessary. The hybrid methods were first illustrated through four test functions from the RSM literature and were compared with original NM, GA and PSO algorithms. In each test scheme, the effectiveness, efficiency and robustness of these methods were evaluated via associated performance statistics, and the proposed hybrid approaches prove to be very suitable for solving the optimization problems of RSM-type. The hybrid methods were then tested by ten difficult nonlinear continuous functions and were compared with the best known heuristics in the literature. The results show that both hybrid algorithms were able to reach the global optimum in all runs within a comparably computational expense.