Antarctic Ice Worm Algorithm: A Novel Nature-inspired Metaheuristic for Optimization Tasks
Bektemyssova G. Montazeri Z. Dehghani M. Ibraheem I.K. Smerat A. Malik O.P. Al-Salih A.A.M.M. Ahmed M.A. Eguchi K.
30 November 2025Intelligent Network and Systems Society
International Journal of Intelligent Engineering and Systems
2025#18Issue 10318 - 332 pp.
Antarctic Ice Worm Algorithm (AIWA), a novel nature-inspired metaheuristic optimization method inspired by the survival and foraging strategies of Antarctic ice worms, which thrive in extreme polar environments through temperature-dependent activity cycles, collective movement patterns, and adaptive foraging behaviours, is introduced in this paper. AIWA models these natural strategies through a mathematically formalized two-phase search process—exploration and exploitation—executed sequentially for each search agent in every iteration. In the exploration phase, agents perform stochastic, population-influenced movements within the search space, guided by adaptive control parameters that preserve diversity and enhance global search capability. In the exploitation phase, agents converge toward the best-known solutions with an intensity that decreases over time, enabling precise local refinement while avoiding premature stagnation. The algorithm was evaluated using the CEC 2017 benchmark suite of 29 functions, including unimodal, multimodal, hybrid, and composition types, and compared against nine recent metaheuristics: MOA, SFOA, WaOA, ASSA, ALA, GWO, TLBO, BaOA, and AFO. Experimental results show that AIWA achieved rank 1 in 27 out of 29 functions, with the only exceptions being C17-F7 (rank 3) and C17-F22 (rank 2). Statistical analyses and boxplot visualizations confirm AIWA’s robustness, low variance, and consistent convergence across diverse landscapes. These findings position AIWA as a competitive, adaptable, and reliable optimization algorithm, with potential for extension to multi-objective, constrained, and large-scale optimization problems in future research.
Antarctic ice worm , Engineering , Exploitation , Exploration , Metaheuristic , Nature-based , Optimization
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Department of Computer Engineering, International Information Technology University, Almaty, 050000, Kazakhstan
Department of Electrical and Electronics Engineering, Shiraz University of Technology, Shiraz, 7155713876, Iran
Department of Electrical Engineering, College of Engineering, University of Baghdad, Baghdad, 10001, Iraq
Faculty of Educational Sciences, Al-Ahliyya Amman University, Amman, 19328, Jordan
Centre for Research Impact and Outcome, Chitkara University, Punjab, India
Department of Electrical and Software Engineering, University of Calgary, Calgary, T2N 1N4, AB, Canada
Department of Computer Engineering Techniques, Al-Nukhba University College, Baghdad, 10013, Iraq
Department of Medical Instrumentation Techniques Engineering, College of Medical Techniques, Al-Farahidi University, Baghdad, 10001, Iraq
Department of Information Electronics, Fukuoka Institute of Technology, Japan
Department of Computer Engineering
Department of Electrical and Electronics Engineering
Department of Electrical Engineering
Faculty of Educational Sciences
Centre for Research Impact and Outcome
Department of Electrical and Software Engineering
Department of Computer Engineering Techniques
Department of Medical Instrumentation Techniques Engineering
Department of Information Electronics
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