"An Adaptive Differential Evolution Algorithm to Solve Constrained Optimization Problems in Engineering Design"
written by Y.Y. AO, H.Q. CHI,
published by Engineering, Vol.2 No.1, 2010
has been cited by the following article(s):
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[2] Adaptive differential evolution with multi-population-based mutation operators for constrained optimization
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[3] Differential evolution with adaptive trial vector generation strategy and cluster-replacement-based feasibility rule for constrained optimization
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[4] A new Kriging–Bat Algorithm for solving computationally expensive black-box global optimization problems
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[5] Integrating surrogate modeling to improve DIRECT, DE and BA global optimization algorithms for computationally intensive problems
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[6] The influence of inertia weight on the particle swarm optimization algorithm
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[7] Hybridizing gravitational search algorithm with real coded genetic algorithms for structural engineering design problem
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[8] The analysis, identification and measures to remove inconsistencies from differential evolution mutation variants
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[9] Continuous Optimization using Evolutionary Computing: Advancements in Differential Evolution Algorithm for Function Optimization and Data Classification
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[10] Quantum evolutionary computational technique for constrained engineering optimization
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[11] Multi-start Space Reduction (MSSR) surrogate-based global optimization method
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[12] A Fast Differential Evolution for Constrained Optimization Problems in Engineering Design
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[13] Enhanced versions of differential evolution: state-of-the-art survey
International Journal of Computing Science and Mathematics?5.2 (2014), 2014
[14] Enhanced versions of differential evolution: state-of-the-art survey.
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[15] Differential evolution using opposite point for global numerical optimization
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[16] An adaptive normalization based constrained handling methodology with hybrid bi-objective and penalty function approach
Evolutionary Computation (CEC), 2012 IEEE Congress on. IEEE, 2012., 2012
[17] Exponential inertia weight for particle swarm optimization
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[18] A combination of specialized differential evolution variants for constrained optimization
Advances in Artificial Intelligence–IBERAMIA 2012. Springer Berlin Heidelberg, 2012
[19] Constrained engineering design optimization using a hybrid bi-objective evolutionary-classical methodology
Simulated Evolution and Learning, Springer, 2010