article · Journal of Computational Design and Engineering
The African vultures optimisation algorithm is an established metaheuristic inspired by the foraging and living habits of vultures. While effective across standard tasks, it can struggle with slow convergence and premature stagnation in complex problem spaces. To resolve these limitations, an enhanced variant incorporates a representative vulture selection strategy, a rotating flight strategy, and a selecting accumulation mechanism. The representative selection mechanism balances broad exploration with focused exploitation, while the remaining additions refine overall solution accuracy. Performance evaluations across twenty-three standard benchmark mathematical functions and comparisons with nine advanced alternatives demonstrate improved outcomes and convergence behaviour. Beyond abstract testing, the method effectively solves three real-world engineering design problems and successfully trains multilayer perceptron networks on XOR and cancer classification datasets, showing clear advantages over competing approaches.
Complex computational and engineering challenges require algorithms that can find high-quality solutions quickly without getting stuck in sub-optimal dead ends. By improving search efficiency and stability, this modified nature-inspired algorithm provides a more capable mathematical tool for resolving intricate design constraints and training machine learning models across scientific and industrial disciplines.
The method is positioned at an applied research stage, having been tested on engineering design problems and machine learning classification tasks, specifically using XOR and cancer datasets. Prospective users include engineering design teams and software developers seeking robust optimisation solvers. Commercial adoption would require embedding the algorithm into production-grade engineering design software or specialised data analysis pipelines to validate performance on larger industry-scale datasets.
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Abstract The African vultures optimization algorithm (AVOA) is a recently proposed metaheuristic inspired by the African vultures’ behaviors. Though the basic AVOA performs very well for most optimization problems, it still suffers from the shortcomings of slow convergence rate and local optimal stagnation when solving complex optimization tasks. Therefore, this study introduces a modified version named enhanced AVOA (EAVOA). The proposed EAVOA uses three different techniques namely representative vulture selection strategy, rotating flight strategy, and selecting accumulation mechanism, respectively, which are developed based on the basic AVOA. The representative vulture selection strategy strikes a good balance between global and local searches. The rotating flight strategy and selecting accumulation mechanism are utilized to improve the quality of the solution. The performance of EAVOA is validated on 23 classical benchmark functions with various types and dimensions and compared to those of nine other state-of-the-art methods according to numerical results and convergence curves. In addition, three real-world engineering design optimization problems are adopted to evaluate the practical applicability of EAVOA. Furthermore, EAVOA has been applied to classify multi-layer perception using XOR and cancer datasets. The experimental results clearly show that the EAVOA has superiority over other methods.
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DOI: 10.1093/jcde/qwac135
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