Bolas Spider Algorithm: A Novel Efficient Nature-Inspired Metaheuristic for Complex Continuous Optimization
Qawaqneh H. Maghaydah S. Alomari S. Bektemyssova G. Montazeri Z. Dehghani M. Malik O.P. Eguchi K.
31 January 2026Intelligent Network and Systems Society
International Journal of Intelligent Engineering and Systems
2026#19Issue 1221 - 239 pp.
A novel metaheuristic optimization algorithm named Bolas Spider Algorithm (BSA), inspired by the hunting behaviour 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.
Algorithm robustness , Benchmark functions , Bolas spider algorithm , Continuous optimization , Exploration and Exploitation , Metaheuristic optimization
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Department of Mathematics, Al Zaytoonah University of Jordan, Amman, 11733, Jordan
Faculty of Information Technology, Abu Dhabi University, United Arab Emirates
Faculty of Science and Information Technology, Jadara University, Irbid, 21110, Jordan
Department of Computer Engineering, International Information Technology University, Almaty, 050000, Kazakhstan
Department of Electrical and Electronics Engineering, Shiraz University of Technology, Shiraz, Iran
Department of Electrical and Software Engineering, University of Calgary, Calgary, T2N 1N4, AB, Canada
Department of Information Electronics, Fukuoka Institute of Technology, Japan
Department of Mathematics
Faculty of Information Technology
Faculty of Science and Information Technology
Department of Computer Engineering
Department of Electrical and Electronics Engineering
Department of Electrical and Software Engineering
Department of Information Electronics
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