Journal
SWARM AND EVOLUTIONARY COMPUTATION
Volume 29, Issue -, Pages 47-72Publisher
ELSEVIER
DOI: 10.1016/j.swevo.2016.03.001
Keywords
Cuckoo search; Global optimization; Self-adaptation; Hybridization
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Adaptation and hybridization typically improve the performances of original algorithm. This paper proposes a novel hybrid self-adaptive cuckoo search algorithm, which extends the original cuckoo search by adding three features, i.e., a balancing of the exploration search strategies within the cuckoo search algorithm, a self-adaptation of cuckoo search control parameters and a linear population reduction. The algorithm was tested on 30 benchmark functions from the CEC-2014 test suite, giving promising results comparable to the algorithms, like the original differential evolution (DE) and original cuckoo search (CS), some powerful variants of modified cuckoo search (i.e., MOCS, CS-VSF) and self-adaptive differential evolution (i.e., jDE, SaDE), while overcoming the results of a winner of the CEC-2014 competition L-Shade remains a great challenge for the future. (C) 2016 Elsevier B.V. All rights reserved.
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