An Adaptive Optimization Strategy Applied to the Artificial Bee Colony Algorithm
DOI:
https://doi.org/10.23055/ijietap.2026.33.5.11703Keywords:
adaptive algorithm, Computational Intelligence, Optimization, model predictive controlAbstract
Balancing exploration and exploitation remains a central challenge in metaheuristic optimization, particularly for engineering applications requiring both global search efficiency and reliable convergence. This paper introduces a simple and adaptive strategy that dynamically adjusts the behavior of an optimization algorithm based on real‑time performance feedback. The method identifies key search operators and assigns each an explorative and exploitative variant, grouped into two optimization modes. An online mechanism switches between these modes according to the successful mutation rate, enabling the optimizer to respond to the evolving landscape of the search process. The strategy is integrated into the artificial bee colony algorithm and evaluated on 18 numerical benchmarks, a CEC2021 test suite, and a nonlinear model predictive control problem. Results demonstrate improved robustness, competitive accuracy, and faster convergence by reducing the number of evaluations by more than 4% against the established GABC, highlighting the method’s suitability for complex industrial optimization
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