Convergence Properties of the Nelder--Mead Simplex Method in Low Dimensions
SIAM Journal on Optimization
Journal of Global Optimization
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
Opposition-Based Differential Evolution
IEEE Transactions on Evolutionary Computation
Accelerating Differential Evolution Using an Adaptive Local Search
IEEE Transactions on Evolutionary Computation
ICIC'11 Proceedings of the 7th international conference on Advanced Intelligent Computing
Expert Systems with Applications: An International Journal
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In this paper, an effective hybrid NM-DE algorithm is proposed for global optimization by merging the searching mechanisms of Nelder-Mead (NM) simplex method and differential evolution (DE). First a reasonable framework is proposed to hybridize the NM simplex-based geometric search and the DE-based evolutionary search. Second, the NM simplex search is modified to further improve the quality of solutions obtained by DE. By interactively using these two searching approaches with different mechanisms, the searching behavior can be enriched and the exploration and exploitation abilities can be well balanced. Based on a set of benchmark functions, numerical simulation and statistical comparison are carried out. The comparative results show that the proposed hybrid algorithm outperforms some existing algorithms including hybrid DE and hybrid NM algorithms in terms of solution quality, convergence rate and robustness.