A Modified Multi-Strategy Dhole Optimization Algorithm and Its Engineering Applications

Area of Science:

• Computational Intelligence
• Optimization Algorithms
• Engineering Applications

Background:

• The Dhole Optimization Algorithm (DOA) faces limitations in exploration range, population diversity, and convergence speed.
• Addressing these limitations is crucial for improving the efficacy of metaheuristic optimization techniques.

Purpose of the Study:

• To propose a Modified Dhole Optimization Algorithm (MDOA) that overcomes the inherent drawbacks of the original DOA.
• To evaluate the performance of MDOA on benchmark functions and real-world engineering optimization problems.

Main Methods:

• MDOA integrates a Beta distribution-based opposition learning strategy, a DE/rand-to-best/1 differential mutation mechanism, and nonlinear parameter control.
• The algorithm was tested on 41 CEC2017 and CEC2022 benchmark functions and applied to five engineering design problems.

Main Results:

• MDOA significantly outperformed 11 state-of-the-art algorithms in convergence speed, accuracy, and robustness on benchmark functions.
• In engineering applications, the MDOA-BP model achieved superior results, notably improving prediction accuracy for moisture content in Dendrobium huoshanense.

Conclusions:

• The Modified Dhole Optimization Algorithm (MDOA) is a highly effective and robust optimizer for complex, constrained, and high-dimensional optimization tasks.
• MDOA shows significant potential for advancing engineering design and predictive modeling applications.