SSABA: Search Step Adjustment Based Algorithm

Finding the suitable solution to optimization problems is a fundamental challenge in various sciences. Optimization algorithms are one of effective stochastic methods solving problems. In this paper, new algorithm called Search Step Adjustment Based Algorithm (SSABA) presented provide quasi-optimal solutions initial iterations algorithm, step index set highest value for comprehensive search space. Then, with increasing repetitions order focus achieving optimal closer global optimal, reduced reach minimum at end implementation. SSABA mathematically modeled and its performance evaluated on twenty-three different standard objective functions unimodal multimodal types. The results show that proposed has high exploitation power appropriate exploration algorithm. addition, compared eight well-known algorithms, including Particle Swarm (PSO), Genetic (GA), Teaching-Learning (TLBO), Gravitational (GSA), Grey Wolf (GWO), Whale (WOA), Marine Predators (MPA), Tunicate (TSA). simulation better more competitive than performance.