ISUD (Individuals with Substance Use Disorder): A Novel Metaheuristic Algorithm for Solving Optimization Problems

In the field of optimization, metaheuristic algorithms have garnered significant interest. These algorithms, which draw inspiration from natural selection, evolution, and problem-solving strategies, offer an alternative approach to solving complex optimization problems. Unlike conventional software engineering methods, metaheuristics do not rely on derivative calculations in the search space. Instead, they explore solutions by iteratively refining and adapting their search process. The no-free-lunch (NFL) theorem proves that an optimization scheme cannot perform well in dealing with all optimization challenges. Over the last two decades, a plethora of metaheuristic algorithms has emerged, each with its unique characteristics and limitations. In this paper, we propose a novel meta-heuristic algorithm called ISUD (Individuals with Substance Use Disorder) to solving optimization problems by examining the clinical behaviors of individuals compelled to use drugs. We evaluate the effectiveness of ISUD by comparing it with several well-known heuristic algorithms across 44 benchmark functions of varying dimensions. Our results demonstrate that ISUD outperforms these existing methods, providing superior solutions for optimization problems.