Open AccessA HYBRID METHOD BASED ON CUCKOO SEARCH ALGORITHM FOR GLOBAL OPTIMIZATION PROBLEMS
Author(s) -
Mohammad Shehab,
Ahamad Tajudin Khader,
Makhlouf Laouchedi
Publication year - 2018
Publication title -
journal of ict
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.217
H-Index - 10
eISSN - 2180-3862
pISSN - 1675-414X
DOI - 10.32890/jict2018.17.3.8261
Subject(s) - cuckoo search , hill climbing , local search (optimization) , guided local search , mathematical optimization , metaheuristic , convergence (economics) , local optimum , computer science , algorithm , global optimization , premature convergence , mathematics , particle swarm optimization , economics , economic growth
Cuckoo search algorithm is considered one of the promising metaheuristic algorithms applied to solve numerous problems in different fields. However, it undergoes the premature convergence problem for high dimensional problems because the algorithm converges rapidly. Therefore, we proposed a robust approach to solve this issue by hybridizing optimization algorithm, which is a combination of Cuckoo search algorithmand Hill climbing called CSAHC discovers many local optimum traps by using local and global searches, although the local search method is trapped at the local minimum point. In other words, CSAHC has the ability to balance between the global exploration of the CSA and the deep exploitation of the HC method. The validation of the performance is determined by applying 13 benchmarks. The results of experimental simulations prove the improvement in the efficiency and the effect of the cooperation strategy and the promising of CSAHC.