A Multi-Strategy Enhanced Hybrid Ant–Whale Algorithm and Its Applications in Machine Learning

Chenyang Gao, Yahua He and Yuelin Gao ()
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Chenyang Gao: The School of Computer Science and Engineering, Xidian University, Xi’an 710071, China
Yahua He: Ningxia Province Key Laboratory of Intelligent Information and Data Processing, North Minzu University, Yinchuan 750021, China
Yuelin Gao: The School of Computer Science and Engineering, Xidian University, Xi’an 710071, China

Mathematics, 2024, vol. 12, issue 18, 1-38

Abstract: Based on the principles of biomimicry, evolutionary algorithms (EAs) have been widely applied across diverse domains to tackle practical challenges. However, the inherent limitations of these algorithms call for further refinement to strike a delicate balance between global exploration and local exploitation. Thus, this paper introduces a novel multi-strategy enhanced hybrid algorithm called MHWACO, which integrates a Whale Optimization Algorithm (WOA) and Ant Colony Optimization (ACO). Initially, MHWACO employs Gaussian perturbation optimization for individual initialization. Subsequently, individuals selectively undertake either localized exploration based on the refined WOA or global prospecting anchored in the Golden Sine Algorithm (Golden-SA), determined by transition probabilities. Inspired by the collaborative behavior of ant colonies, a Flight Ant (FA) strategy is proposed to guide unoptimized individuals toward potential global optimal solutions. Finally, the Gaussian scatter search (GSS) strategy is activated during low population activity, striking a balance between global exploration and local exploitation capabilities. Moreover, the efficacy of Support Vector Regression (SVR) and random forest (RF) as regression models heavily depends on parameter selection. In response, we have devised the MHWACO-SVM and MHWACO-RF models to refine the selection of parameters, applying them to various real-world problems such as stock prediction, housing estimation, disease forecasting, fire prediction, and air quality monitoring. Experimental comparisons against 9 newly proposed intelligent optimization algorithms and 9 enhanced algorithms across 34 benchmark test functions and the CEC2022 benchmark suite, highlight the notable superiority and efficacy of MSWOA in addressing global optimization problems. Finally, the proposed MHWACO-SVM and MHWACO-RF models outperform other regression models across key metrics such as the Mean Bias Error (MBE), Coefficient of Determination ( R 2 ), Mean Absolute Error (MAE), Explained Variance Score (EVS), and Median Absolute Error (MEAE).

Keywords: ant colony optimization; whale optimization algorithm; flight ant strategy; Gaussian scatter search strategy; prediction problems (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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