Search

The optimization of continuous space poses a great challenge among the scientific community. When the objective function is nonlinear, the choices of direct search spaces are preferred over the other methods. The use of the hybrid algorithm for these types of optimization is becoming increasingly popular. This study introduced a self-adaptation procedure in a single objective hybrid algorithm and its application for unconstrained and constrained optimization test functions. This single objective hybrid algorithm is based on two popular metaheuristic algorithms, namely the cuckoo search and covariance matrix adaptation evolution strategy. Self-adaptation is a popular way of parameter selection and has a significant place in the computing field. The adaptation is introduced in two significant parameters of this algorithm. Five metaheuristic algorithms, namely cuckoo search, covariance matrix adaptation evolution strategy, particle swarm intelligence, firefly algorithm, and the newly introduced self-adapted single objective hybrid algorithm, were analyzed using unconstrained (unimodal and multimodal) and constrained benchmark test functions. An encouraging performance of this proposed algorithm for unconstrained and constrained test functions was observed. [ABSTRACT FROM AUTHOR]

• 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. 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.). [ABSTRACT FROM AUTHOR]