Hybridizing the Cuckoo Search Algorithm with Different Mutation Operators for Numerical Optimization Problems
Abed-alguni, B., Paul, D. “Hybridizing The Cuckoo Search Algorithm with Different Mutation operators for Numerical Optimization Problems”, Journal of Intelligent Systems, Vol. 29 (Issue 1), pp. 1043-1062, 2020.
Abstract
The Cuckoo search (CS) algorithm is an efficient evolutionary algorithm inspired by the nesting and parasitic reproduction behaviors of some cuckoo species. Mutation is an operator used in evolutionary algorithms to maintain the diversity of the population from one generation to the next. The original CS algorithm uses the Lévy flight method, which is a special mutation operator, for efficient exploration of the search space. The major goal of the current paper is to experimentally evaluate the performance of the CS algorithm after replacing the Lévy flight method in the original CS algorithm with seven different mutation methods. The proposed variations of CS were evaluated using 14 standard benchmark functions in terms of the accuracy and reliability of the obtained results over multiple simulations. The experimental results suggest that the CS with polynomial mutation provides more accurate results and is more reliable than the other CS variations.
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