Swarm intelligence
Journal of Global Optimization
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Empirical Study of Hybrid Particle Swarm Optimizers with the Simplex Method Operator
ISDA '05 Proceedings of the 5th International Conference on Intelligent Systems Design and Applications
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
Low Dimensional Simplex Evolution--A Hybrid Heuristic for Global Optimization
SNPD '07 Proceedings of the Eighth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing - Volume 02
Advances in Differential Evolution
Advances in Differential Evolution
Search biases in constrained evolutionary optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
A simple multimembered evolution strategy to solve constrained optimization problems
IEEE Transactions on Evolutionary Computation
An Adaptive Tradeoff Model for Constrained Evolutionary Optimization
IEEE Transactions on Evolutionary Computation
Constrained optimization based on modified differential evolution algorithm
Information Sciences: an International Journal
Improving differential evolution algorithm by synergizing different improvement mechanisms
ACM Transactions on Autonomous and Adaptive Systems (TAAS)
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In this paper, we propose a new selection criterion for candidate solutions to a constrained optimization problem. Such a selection mechanism is incorporated into a differential evolution (DE) algorithm. This DE approach is then hybridized with an operator based on the Nelder-Mead method, whose aim is to speed up convergence towards good solutions. The proposed approach is called "Hybrid of Differential Evolution and the Simplex Method for Constrained Optimization Problems" (HDESMCO), and is validated using a well-know benchmark for constrained evolutionary optimization. The results indicate that our proposed approach produces solutions whose quality is competitive with respect to those generated by three evolutionary algorithms from the state-of-the-art (improved stochastic ranking, diversity-DE and Generalized Differential Evolution), but requiring a lower number of objective function evaluations.