Attrition Mill Optimization Algorithm: A Novel Approach for Solving Engineering Optimization Problems
Amir Mohammad Sharafaddini, Behnam Mohammad Hasani Zade, Najme Mansouri
Supercomputing Journal•2025
Abstract
Optimization algorithms are essential across science and engineering, and because problem scales grow, constraints tighten, and the no-free-lunch theorem still applies, new methods continue to appear to better trade exploration and exploitation while remaining simple and efficient. This paper presents the Attrition Mill Optimization (AMO) algorithm, a physics-inspired metaheuristic modeled after attrition mills. AMO employs a compact three-stage structure—Initialization, Operation, and Rejuvenation Cycle—to maintain a strong exploration–exploitation balance with minimal control parameters. Integrated Opposition-Based Learning (OBL) helps delay premature convergence and preserve diversity in both low- and high-dimensional problems. Evaluations across 19 benchmark functions and three real-world engineering problems demonstrate that AMO achieves competitive or superior performance with shorter runtimes compared to 16 established metaheuristics. Furthermore, the paper introduces a multi-objective extension, MOAMO, featuring external archives, crowding-distance selection, and dynamic reference guidance. On the Zitzler–Deb–Thiele (ZDT) and Deb–Thiele–Laumanns–Zitzler (DTLZ) benchmark suites, MOAMO achieves stable convergence toward the Pareto front with strong coverage.