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Improved Fox Optimization Algorithm Based on Opposition-Based Learning Mechanism and Control Variable for Solving Engineering Optimization Problems.

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Title: Improved Fox Optimization Algorithm Based on Opposition-Based Learning Mechanism and Control Variable for Solving Engineering Optimization Problems.
Authors: Wang, Dongsheng1 wds0824@qq.com, Gao, Chuang2 13500422153@163.com, Gao, Biao3 gaobiao0910@qq.com
Source: IAENG International Journal of Computer Science. Jun2026, Vol. 53 Issue 6, p2316-2327. 12p.
Subjects: Metaheuristic algorithms, Global optimization, Parameterization, Engineering design, Multidisciplinary design optimization
Abstract: The Fox Optimizer (FOX) is a meta-heuristic algorithm inspired by red foxes' snow-hunting behavior, simulating their "random prey searching" and "precise jumping for hunting" processes. To enhance FOX's global search capability and convergence performance, this paper proposes an improved algorithm (OBL4FOX) integrating the opposition-based learning (OBL) mechanism and optimized control variables. First, the OBL mechanism is introduced to generate opposite solutions, expanding the search space and strengthening global exploration. Second, the core control variable in FOX is improved via four non-linear dynamic update strategies, enhancing adaptability to complex solution spaces. Validated on the CEC2022 test function set, OBL4FOX exhibits superior performance. OBL4FOX is further applied to three types of typical engineering optimization problems: minimizing the manufacturing cost of pressure vessel design, minimizing the structural weight of three-bar truss design, and minimizing the comprehensive cost of welded beam design. The experimental results demonstrate that under the premise of satisfying all engineering constraints, OBL4FOX can obtain better objective function values when solving engineering problems, which verifies its effectiveness and practicality in actual complex engineering optimization scenarios. [ABSTRACT FROM AUTHOR]
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Database: Engineering Source
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Abstract:The Fox Optimizer (FOX) is a meta-heuristic algorithm inspired by red foxes' snow-hunting behavior, simulating their "random prey searching" and "precise jumping for hunting" processes. To enhance FOX's global search capability and convergence performance, this paper proposes an improved algorithm (OBL4FOX) integrating the opposition-based learning (OBL) mechanism and optimized control variables. First, the OBL mechanism is introduced to generate opposite solutions, expanding the search space and strengthening global exploration. Second, the core control variable in FOX is improved via four non-linear dynamic update strategies, enhancing adaptability to complex solution spaces. Validated on the CEC2022 test function set, OBL4FOX exhibits superior performance. OBL4FOX is further applied to three types of typical engineering optimization problems: minimizing the manufacturing cost of pressure vessel design, minimizing the structural weight of three-bar truss design, and minimizing the comprehensive cost of welded beam design. The experimental results demonstrate that under the premise of satisfying all engineering constraints, OBL4FOX can obtain better objective function values when solving engineering problems, which verifies its effectiveness and practicality in actual complex engineering optimization scenarios. [ABSTRACT FROM AUTHOR]
ISSN:1819656X