C-SOMGOAM: A Self-Organizing Migrating Grasshopper Mutation Algorithm for solving constrained optimization - Singh, Dipti; Chand, Neha (Brazilian Journal of Biometrics, 2026)
This paper introduces C-SOMGOAM, a self-organizing migrating grasshopper optimization algorithm designed for constrained optimization (CO). The algorithm integrates features from the Grasshopper Optimization Algorithm (GOA), the Self-Organizing Migrating Algorithm (SOMA), and a non-uniform mutation operator. A key contribution of this work is the incorporation of a penalty-free constraint-handling mechanism into GOA to effectively address CO problems. Unlike some traditional methods, the proposed GOA variant explicitly operates on a population of solutions. Starting with a randomly selected population, the algorithm iteratively modifies these solutions to improve the approximation of the global optimum. C-SOMGOAM is not only simple to implement but also capable of generating feasible and high-quality solutions. To assess its performance, the algorithm is evaluated on ten constrained benchmark problems and three engineering design problems from the literature. The effectiveness of C-SOMGOAM is demonstrated through comprehensive result analysis and comparative evaluation against existing variants.