A novel cuckoo search technique for solving discrete optimization problems

Abstract

During the past decade, swarm intelligence (SI) techniques have received considerable recognition among researchers to solve continuous optimization problems. However, only few significant works have been reported in the literature to solve discrete optimization problems using SI techniques. Therefore, this paper proposes an improved SI technique, namely, discrete cuckoo search. As an application, the proposed technique is employed to solve a transposition cipher, and then the efficiency of the proposed technique is compared to the existing genetic algorithms. The obtained results indicate that the performance of the proposed technique is superior to genetic algorithms (as compared to genetic algorithm, cuckoo search is roughly 1.5 times faster and recovers 12% more number of key elements). Hence, the proposed technique can be utilized to solve various discrete optimization problems, e.g., for optimal placement of phaser measurement units in a power system, traveling salesman problem, graph coloring problem etc. © 2018, The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden.