Improved Cuckoo Search algorithmic variants for constrained nonlinear optimization. (November 2020)

• Record Type: Journal Article
• Authors: Tsipianitis, Alexandros; Tsompanakis, Yiannis
• Is Part Of: Advances in engineering software. Volume 149(2020)
• Publication Date: 2020-11
• Subjects: Structural optimization -- Nonlinear constraints -- Dynamic penalty -- Swarm intelligence -- Evolutionary optimizers -- Cuckoo Search -- Benchmark problems; Computer-aided engineering -- Periodicals; Engineering -- Computer programs -- Periodicals; Engineering -- Software -- Periodicals; Periodicals; 620.0028553
• DOI: 10.1016/j.advengsoft.2020.102865
• Languages: English
• ISSNs: 0965-9978

Abstract

Highlights: Four novel variants of the Cuckoo-Search (CS) algorithm are developed. Parameter adaptation, dynamic penalties and hybridization with BSA are used. Enhanced CS variants outperform original CS and other metaheuristic algorithms. CS variants become more efficient as problem size and/or complexity are increased. Using dynamic penalties yields better final designs and faster convergence. Abstract: Although Cuckoo Search (CS) is a quite new nature-inspired metaheuristic optimization algorithm, it has been extensively used in engineering applications, since it has been proven very efficient in solving complex nonlinear problems. In this paper, efficient modifications have been made to the original CS algorithm to enhance its efficiency and robustness. More specifically, constant parameters of the algorithm, such as the probability of the alien egg being discovered by the host bird and the step size of Levy flights have been dynamically tuned. In addition, static and dynamic penalty functions are introduced within the optimization formulation. Finally, a hybrid optimization approach is developed to combine the advantages of CS with those of Bird Swarm Algorithm (BSA). Benchmark problems, widely used in relevant studies, have been solved and the obtained solutions are compared with those previously reported using the standard CS algorithm and other popular evolutionary optimization techniques (i.e., Genetic Algorithms, Particle Swarm Optimization, etc.).