HSCWMA: A New Hybrid SCA-WMA Algorithm for Solving Optimization Problems

Hybrid metaheuristic algorithms have recently become an interesting topic in solving optimization problems. The woodpecker mating algorithm (WMA) and the sine cosine algorithm (SCA) have been integrated in this paper to propose a hybrid metaheuristic algorithm for solving optimization problems called HSCWMA. Despite the high capacity of the WMA algorithm for exploration, this algorithm needs to augment exploitation especially in initial iterations. Also, the sine and cosine relations used in the SCA provide the good exploitation for this algorithm, but SCA suffers the lack of an efficient process for the implementation of effective exploration. In HSCWMA, the modified mathematical search functions of SCA by Levy flight mechanism is applied to update the female woodpeckers in WMA. Moreover, the local search memory is used for all search elements in the proposed hybrid algorithm. The goal of proposing the HSCWMA is to use exploration capability of WMA and Levy flight, utilize exploitation susceptibility of the SCA and the local search memory, for developing exploration and exploitation qualification, and providing the dynamic balance between these two phases. For efficiency evaluation, the proposed algorithm is tested on 28 mathematical benchmark functions. The HSCWMA algorithm has been compared with a series of the most recent and popular metaheuristic algorithms and it outperforms them for solving nonconvex, inseparable, and highly complex optimization problems. The proposed algorithm is also used as a Multi-Layer Perceptron (MLP) neural network trainer to solve the software development effort estimation (SDEE) problem on three real-world datasets. The simulation results proved the superior and promising performance of the HSCWMA algorithm in the majority of evaluations.

Automated summary

The HSCWMA algorithm combines the exploration capabilities of WMA and Levy flight, with the exploitation susceptibility of SCA and local search memory, to achieve a dynamic balance between exploration and exploitation. Through testing on mathematical benchmark functions and real-world datasets, it has shown superior performance in solving complex optimization problems and software development effort estimation tasks.