EBBO: A Biomimetically Enhanced Optimization Algorithm with Multi-Stage Cooperation for Complex Engineering...

Area of Science:

• Computational Intelligence
• Optimization Algorithms
• Engineering Applications

Background:

• The original Beaver Behavior Optimizer (BBO) has limitations in solving complex optimization problems.
• There is a need for advanced optimization techniques that balance exploration and exploitation effectively.

Purpose of the Study:

• To introduce the Enhanced Beaver Behavior Optimizer (EBBO) to address the limitations of the original BBO algorithm.
• To evaluate EBBO's performance on complex, high-dimensional, and multimodal optimization problems.

Main Methods:

• EBBO integrates a three-phase cooperative framework with adaptive mutation.
• Dynamic opposition-based learning and a risk-aware decision strategy inspired by simulated annealing are incorporated.
• Performance is evaluated using the CEC 2017 and CEC 2020 benchmark suites and classical engineering design problems.

Main Results:

• EBBO significantly outperforms nine widely used algorithms, including the original BBO, in convergence accuracy, stability, and robustness.
• Demonstrated average objective value reductions of 15-50% and standard deviation reductions of 30-70% compared to BBO.
• Achieved best or near-optimal solutions for engineering design problems like step-cone pulley, pressure vessel, three-bar truss optimization, and 3D UAV path planning, satisfying all constraints.

Conclusions:

• EBBO offers a reliable and efficient approach for solving complex constrained optimization challenges.
• The algorithm effectively balances exploration and exploitation for superior performance in benchmark and real-world engineering contexts.
• EBBO represents a significant advancement in optimization algorithm development.