An Enhanced Whale Optimization Algorithm with outpost and multi-population mechanisms for high-dimensional optimization and medical diagnosis

Abstract

Swarm intelligence optimization algorithms represent a significant branch of nature-inspired computational methods, designed to solve complex optimization problems by simulating the collective behavior of biological systems. Whale optimization algorithm (WOA) is a newly developed meta-heuristic algorithm, which is mainly based on the predation behavior of humpback whales in the ocean. This study proposes an enhanced version of the WOA, named the Outpost-based Multi-population Whale Optimization Algorithm (OMWOA), which integrates two key mechanisms: the outpost mechanism and a multi-population enhanced mechanism. These modifications aim to improve the algorithm’s performance in terms of solution accuracy and convergence rate. The effectiveness of OMWOA is thoroughly evaluated by benchmarking it against state-of-the-art evolutionary algorithms from the IEEE CEC 2017 and IEEE CEC 2022 competitions. Additionally, this study provides a detailed analysis of the influence of the outpost and multi-population mechanisms on OMWOA’s performance, as well as its scalability in problems of varying dimensionalities. To validate its applicability in real-world problems, the proposed algorithm is combined with Kernel Extreme Learning Machine (KELM) for solving medical disease diagnosis tasks. The experimental results demonstrate the superior performance of OMWOA in terms of diagnostic accuracy across five medical datasets, highlighting its potential for real-world applications.