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摘要
As a novel evolutionary computation, cuckoo search (CS) algorithm has attracted much attention and wide applications, owing to its easy implementation. CS as most population-based algorithm is good at identifying promising area of the search space, but less good at fine-tuning the approximation to the minimization. To the best of our knowledge, the hybridization of augmented Lagrangian method, cuckoo search and Solis and Wets local search has not been attempted yet. In this paper, an effective hybrid cuckoo search algorithm based on Solis and Wets local search technique is proposed for constrained global optimization that relies on an augmented Lagrangian function for constraint-handling. Numerical results and comparisons with other state-of-the-art stochastic algorithms using a set of benchmark constrained test functions and engineering design optimization problems are provided.
