Enhanced Red-billed Blue Magpie Optimizer for engineering optimization problems.

Area of Science:

• Computational intelligence
• Optimization algorithms
• Metaheuristic computing

Background:

• Complex optimization problems often challenge traditional methods due to high dimensionality, nonlinearity, and multimodal landscapes.
• Existing metaheuristic algorithms require enhancements for improved search effectiveness and adaptability.

Purpose of the Study:

• To introduce the Enhanced Red-billed Blue Magpie Optimizer (ERBMO), an advanced metaheuristic algorithm.
• To improve population diversity, mitigate premature convergence, and balance exploration-exploitation in optimization.

Main Methods:

• ERBMO integrates diversity-adaptive weight updating, periodic pattern search, and evolutionary probabilistic combinatorial mutation.
• Evaluations conducted on CEC2017 and CEC2022 benchmark suites.
• Comparative analysis against state-of-the-art and classical optimization algorithms.
• Application to real-world engineering design problems: speed reducer, pressure vessel, step-cone pulley, and hydrostatic thrust bearing.

Main Results:

• ERBMO achieved the highest Friedman rankings on the CEC2017 benchmark suite across 30D, 50D, and 100D.
• ERBMO secured top overall rankings on the CEC2022 benchmark suite for 10D and 20D, outperforming nine state-of-the-art algorithms.
• Ablation studies confirmed the effectiveness of each proposed enhancement strategy.
• ERBMO consistently ranked first on real-world engineering problems, delivering optimal or near-optimal solutions with high robustness.

Conclusions:

• ERBMO demonstrates superior performance and robustness compared to existing algorithms on both benchmark and practical optimization tasks.
• The proposed enhancement mechanisms effectively address limitations of traditional metaheuristics.
• ERBMO offers a reliable framework for engineering optimization and metaheuristic algorithm design.