Jalak Bali Optimization Algorithm: a novel metaheuristic algorithm for Solving Engineering Optimization Problems

Abstrak

Despite the rapid development of metaheuristic algorithms, achieving a robust balance between exploration and exploitation while maintaining stability across diverse optimization landscapes remains a significant challenge. Existing approaches often suffer from premature convergence and inconsistent performance, particularly on high-dimensional and multimodal problems. To address these limitations, this paper proposes a novel nature-inspired metaheuristic, termed the Jalak Bali Optimization Algorithm (JBOA), which is conceptually derived from the exploration, predation, and regeneration behaviors of the endemic Indonesian Jalak Bali bird. The main novelty of JBOA lies in its integrated search mechanism that combines adaptive exploration strategies with regeneration-based exploitation to enhance convergence reliability and prevent stagnation. The proposed algorithm is mathematically formulated and evaluated on 23 benchmark functions from the CEC 2017 suite, and compared against eight state-of-the-art metaheuristic algorithms using comprehensive numerical experiments and statistical analyses. The results demonstrate that JBOA achieves superior stability and competitive accuracy, particularly on unimodal and moderately complex multimodal functions, while maintaining consistent convergence behavior. These findings highlight JBOA as a robust and efficient optimization framework with strong potential for solving complex engineering optimization problems.

Kata kunci

• Jalak Bali Optimization Algorithm
• Metaheuristic
• Swarm Intelligence
• global optimization
• engineering optimization