Success-Based Optimization Algorithm (SBOA): Development and enhancement of a metaheuristic optimizer

Abstract

This paper presents the development of the Success-Based Optimization Algorithm (SBOA), a novel metaheuristic inspired by success attribution theory, designed to address complex, high-dimensional optimization problems. SBOA balances exploration and exploitation by utilizing high-performing solutions and average-performing candidates to guide the search process, dynamically adjusting based on solution quality. The algorithm is evaluated against seven well-established optimization methods using CEC 2017 benchmark functions in 10, 30, and 50 dimensions. It is applied to a real-world engineering problem involving the optimal design of shell-and-tube heat exchangers (STHEs). The results demonstrate that SBOA consistently surpasses most competing algorithms, especially in higher-dimensional cases, achieving lower objective values and faster convergence. Statistical analyses, including the Wilcoxon signed-rank test, confirm the significant advantages of SBOA in benchmark performance and cost-effectiveness in practical engineering applications. These findings position SBOA as a highly adaptable and efficient optimization tool for addressing complex tasks.