Snake Optimization Algorithm Augmented by Adaptive t-Distribution Mixed Mutation and Its Application in Energy...

Area of Science:

• Computational Intelligence
• Optimization Algorithms
• Metaheuristics

Background:

• Traditional snake optimization suffers from random initialization, slow convergence, and low accuracy.
• Addressing these limitations is crucial for improving optimization performance.

Purpose of the Study:

• To propose an adaptive t-distribution mixed mutation snake optimization strategy.
• To enhance the performance of the snake optimization algorithm by improving initialization, convergence speed, and accuracy.

Main Methods:

• Utilized Tent-based chaotic mapping and quasi-reverse learning for population initialization.
• Introduced an adaptive t-distribution mixed mutation foraging strategy for enhanced exploration.
• Replaced the mating mode with an opposite-sex attraction mechanism for improved global search.

Main Results:

• The enhanced snake optimization algorithm demonstrates accelerated convergence and improved solution accuracy.
• The proposed method shows superior robustness and accuracy compared to the standard snake optimization technique.
• The integrated improvements synergistically enhance the algorithm's overall performance.

Conclusions:

• The adaptive t-distribution mixed mutation snake optimization strategy effectively overcomes the drawbacks of the traditional method.
• The enhanced algorithm achieves a better balance between local and global exploitation capabilities.
• This improved optimization technique offers a more robust and accurate solution for complex problems.