Bolas Spider Algorithm: A Novel Efficient Nature-Inspired Metaheuristic for Complex Continuous Optimization

Abstract

A novel metaheuristic optimization algorithm named Bolas Spider Algorithm (BSA), inspired by the hunting behavior of bolas spiders is presented in this paper. The proposed algorithm combines pheromone-guided exploration with targeted prey-capture exploitation to achieve a dynamic balance between diversification and intensification, enabling effective navigation of complex continuous optimization landscapes. The algorithm’s design emphasizes adaptability, robustness, and high solution quality, while avoiding premature convergence and maintaining population diversity. Performance of the algorithm was rigorously evaluated on a comprehensive benchmark suite comprising 29 continuous functions, including unimodal, multimodal, hybrid, and composition problems. Comparative experiments involved nine recently developed metaheuristic algorithms, and multiple statistical measures—mean, best, worst, standard deviation, median, and rank—were computed over 30 independent runs for each function. Additionally, the Wilcoxon signed-rank test has been employed to validate the statistical significance of the results. Empirical findings indicate that the proposed algorithm consistently achieves superior performance, obtaining the first rank in 24 out of 29 benchmark functions, including all composition functions and several complex multimodal and hybrid problems. Qualitative analysis using boxplot visualizations further confirms the algorithm’s stability and robustness, demonstrating narrow distributions, low variability, and minimal outliers across independent runs. The observed advantages are attributed to the algorithm’s dual search mechanism, which efficiently combines global exploration with local exploitation, ensuring both convergence accuracy and repeatability. Overall, the results establish the proposed algorithm as a highly effective, reliable, and statistically validated optimization method. Its biologically inspired mechanisms and parameter-efficient design make it suitable for a wide range of continuous optimization problems, with potential applications in engineering, industrial, and real-world decision-making scenarios.

Keywords; Algorithm robustness, Benchmark functions, Bola's spider algorithm, Continuous optimization, Exploration and Exploitation, Metaheuristic optimization