A Hybrid Chaotic Zebra Optimization Algorithm for Cost-Effective Healthcare Team Formation

Abstract

This paper presents a hybrid approach to enhancing the zebra optimization algorithm (ZOA) by integrating the chaotic map for cost-effective healthcare team formation. Healthcare team formation is one of the complex optimization problems that is essential in resource allocation, cost efficiency, and skill diversity. Traditional methods struggle to find optimal solutions, which makes the metaheuristic algorithm a valuable approach to solving complex challenges. Metaheuristic algorithms are inspired by natural and evolutionary processes and have been widely implemented in optimization problems due to their ability to explore large solution spaces and bring optimal solutions. Among these, ZOA has shown the ability to solve optimization problems where it is inspired by zebra natural behaviors, which face some limitations on diversity, exploration, and resource allocation, particularly in finding the best team formation by random skill set. The standard ZOA’s randomization of data lacks strategic diversity, which leads to inefficient solutions and slower convergence. To overcome these limitations, the chaotic tent-map will be integrated with ZOA to improve the algorithm’s exploration and heterogeneity or solution capabilities. The enhanced ZOA performance will be compared with the original ZOA and other metaheuristic algorithms. The performance of the improved algorithm is endorsed using real data information from expert doctors in Malaysia, displaying improved outcomes in terms of both cost efficiency and team formation size.