Hybrid SCA-BOA: a balanced exploration–exploitation metaheuristic for large-scale optimization and cloud computing applications

Abstract Complex nonlinear problems are typically solved by optimization methods, and metaheuristic algorithms have attracted significant attention because of their ability to solve complex optimization problems. Most of the available approaches, however, have weaknesses, including premature convergence, a lack of balance between exploration and exploitation and slow convergence within high-dimensional spaces. To overcome these shortcomings, in this paper, the hybridization of the sine cosine algorithm (SCA) and butterfly optimization algorithm (BOA) and the hybrid sine cosine-butterfly optimization algorithm (SCA-BOA), which have the global exploration power of the sine cosine algorithm and the high local exploitation power of the butterfly optimization algorithm, are suggested. The suggested solution works in two phases. In the first step, the sine cosine mechanism improves the diversity in the population and conducts a global search with sinusoidal operators. At a later stage, the butterfly optimization strategy increases the search near promising regions, enhancing local refinement. This hybrid design permits a more balanced trade-off between exploration and exploitation as well as improved convergence stability. The proposed algorithm was evaluated using classical benchmark functions and the IEEE CEC 2022 benchmark suite under identical experimental settings. Ten standard benchmark functions were tested using the algorithm and compared to nine existing metaheuristic algorithms under the same experimental conditions. The findings show that the SCA-BOA has better performance in terms of fitness values, convergence speed, and stability of the solution. The experimental results demonstrate that the proposed hybrid SCA-BOA achieved the best overall average ranking score (2.3) among the compared optimization algorithms and produced competitive mean fitness and standard deviation values across diverse benchmark landscapes. Furthermore, the proposed algorithm was validated using the IEEE CEC 2022 benchmark suite, where convergence analysis demonstrated improved convergence stability and adaptive search behavior on unimodal, multimodal, hybrid, and composition benchmark functions. In addition, population diversity analysis quantitatively confirmed the balanced exploration–exploitation capability of the proposed hybrid algorithm and its effectiveness in avoiding premature convergence during optimization. These results demonstrate the effectiveness of the proposed algorithm and its ability to address complex continuous optimization problems in the field.