Mohammad Shehab* and Ahamad Tajudin Khader Pages 307 - 315 ( 9 )
Background: Cuckoo Search Algorithm (CSA) was introduced by Yang and Deb in 2009. It considers as one of the most successful in various fields compared with the metaheuristic algorithms. However, random selection is used in the original CSA which means there is no high chance for the best solution to select, also, losing the diversity.
Methods: In this paper, the Modified Cuckoo Search Algorithm (MCSA) is proposed to enhance the performance of CSA for unconstrained optimization problems. MCSA is focused on the default selection scheme of CSA (i.e. random selection) which is replaced with tournament selection. So, MCSA will increase the probability of better results and avoid the premature convergence. A set of benchmark functions is used to evaluate the performance of MCSA.
Results: The experimental results showed that the performance of MCSA outperformed standard CSA and the existing literature methods.
Conclusion: The MCSA provides the diversity by using the tournament selection scheme because it gives the opportunity to all solutions to participate in the selection process.
Cuckoo search algorithm, random selection, tournament selection, premature convergence, global optimization problems, MCSA.
Computer Science Department, Aqaba University of Technology, Aqaba 77110, School of Computer Science, Universiti Sains Malaysia, Main Campus, Pulau Pinang 11800