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Improved Cuckoo Search algorithm for numerical function optimization
Author(s) -
Jianjun Liu,
Min Zeng,
Yifan Ge,
Changzhi Wu,
Xiangyu Wang
Publication year - 2018
Publication title -
journal of industrial and management optimization
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.325
H-Index - 32
eISSN - 1553-166X
pISSN - 1547-5816
DOI - 10.3934/jimo.2018142
Subject(s) - cuckoo search , computer science , metaheuristic , convergence (economics) , algorithm , mathematical optimization , cuckoo , benchmark (surveying) , artificial intelligence , particle swarm optimization , mathematics , zoology , geodesy , geography , economics , biology , economic growth
Cuckoo Search (CS) is a recently proposed metaheuristic algorithm to solve optimization problems. For improving its performance both on the efficiency of searching and the speed of convergence, we proposed an improved Cuckoo Search algorithm based on the teaching-learning strategy (TLCS). For a better balance between intensification and diversification, both a dynamic weight factor and an out-of-bound project strategies are also introduced into TLCS. The results of numerical experiment demonstrate that our improved TLCS performs better than the basic CS and other two improved CS methods appearing in literatures.

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