Gaussian bare-bone slime mould algorithm: performance optimization and case studies on truss structures.

Area of Science:

• Computational Intelligence
• Swarm Intelligence
• Optimization Algorithms

Background:

• The slime mould algorithm (SMA) is a novel meta-heuristic inspired by slime mould foraging behavior.
• The original SMA exhibits good performance but suffers from local optima and insufficient exploitation.
• Enhancing exploration and exploitation is crucial for improving meta-heuristic algorithm efficiency.

Purpose of the Study:

• To propose a Gaussian barebone mutation enhanced SMA (GBSMA) to address the shortcomings of the original SMA.
• To improve the convergence speed and solution accuracy of the slime mould algorithm.
• To evaluate the performance of GBSMA on benchmark test functions and real-world optimization problems.

Main Methods:

• Incorporation of a Gaussian function to accelerate convergence and expand the search space.
• Integration of a differential evolution (DE) update strategy with a guiding vector for enhanced global search.
• Introduction of greedy selection to prevent invalid position updates and maintain population diversity.

Main Results:

• GBSMA demonstrated significantly improved convergence speed and solution accuracy compared to the original SMA.
• Experimental results on IEEE CEC2017 test functions validated the superior performance of GBSMA.
• Application to truss structure optimization problems confirmed GBSMA's effectiveness in solving complex engineering tasks.

Conclusions:

• The proposed GBSMA effectively overcomes the limitations of the original SMA, particularly in terms of local optima and exploitation.
• GBSMA offers enhanced exploration and exploitation capabilities, leading to better optimization results.
• This enhanced algorithm shows significant potential for various optimization applications in science and engineering.