Gaussian combined arms algorithm: a novel meta-heuristic approach for solving engineering problems

This study presents the Gaussian Combined Arms (GCA) algorithm, a novel metaheuristic approach inspired by military strategies, designed to address high-dimensional and complex optimization challenges in engineering. The algorithm employs a dual-agent strategy by dividing search agents into two coordinated groups: ground forces for intensively refining solutions within promising regions and air forces for extensively exploring the search space to avoid local optima. By integrating Gaussian distribution principles, the GCA algorithm dynamically balances exploration and exploitation, ensuring adaptability and efficiency across diverse optimization landscapes. Experimental evaluations are first applied on three sets of test functions, including the standard benchmark set, CEC2017 and CEC2019, followed by several real-world engineering problems, such as Economic Load Dispatch and structural design optimization. The results demonstrate that GCA achieves superior accuracy, robustness, and convergence rates compared to conventional metaheuristic algorithms. These findings underscore the potential of GCA as a reliable tool for solving intricate engineering optimization problems.