An enhanced dung beetle optimizer with multiple strategies for robot path planning.

Area of Science:

• Computational Intelligence
• Optimization Algorithms
• Swarm Intelligence

Background:

• Original dung beetle optimization algorithm suffers from low population diversity, limited global exploration, and susceptibility to local optima.
• Existing algorithms struggle with convergence accuracy and robustness in complex problem spaces.

Purpose of the Study:

• To propose a novel, hybrid multi-strategy improved dung beetle optimization algorithm (SSTDBO).
• To enhance population diversity, global exploration, and convergence accuracy.
• To validate the algorithm's effectiveness on benchmark functions and real-world engineering problems.

Main Methods:

• Initialization using cubic chaotic mapping to improve population diversity and search range.
• Incorporation of a cooperative search algorithm to strengthen inter-individual and group communication during foraging.
• Integration of T-distribution mutation and differential evolutionary variation for enhanced population diversity and local optima avoidance.
• Comparative analysis against multiple established optimization algorithms (GODBO, QHDBO, DBO, KOA, NOA, WOA, HHO) using 29 CEC2017 benchmark functions.

Main Results:

• The proposed SSTDBO algorithm exhibits significantly enhanced robustness and optimization capabilities compared to existing methods.
• Superior performance demonstrated across 29 benchmark test functions from CEC2017.
• Effective application to real-world robot path planning problems, confirming practical utility.

Conclusions:

• The hybrid multi-strategy approach substantially improves the dung beetle optimization algorithm's performance.
• SSTDBO offers a superior and robust solution for complex optimization tasks, including engineering applications like robot path planning.