A Hybrid SSA and SMA with Mutation Opposition-Based Learning for Constrained Engineering Problems.

Area of Science:

• Computational Intelligence
• Optimization Algorithms
• Engineering Applications

Background:

• Salp Swarm Algorithm (SSA) faces challenges with local minima and population density.
• Slime Mould Algorithm (SMA) offers global exploration but suffers from slow convergence.
• Constrained engineering problems require robust and efficient optimization techniques.

Purpose of the Study:

• To propose a novel hybrid optimization algorithm, HSMSSA, by integrating SSA and SMA.
• To enhance global optimization performance and convergence speed for complex problems.
• To address premature convergence and balance exploration-exploitation phases in optimization.

Main Methods:

• Integrating SMA into SSA's leader position updating equations.
• Incorporating Levy flight to improve exploration capabilities.
• Developing a mutation opposition-based learning strategy for enhanced performance.

Main Results:

• HSMSSA was tested on 23 benchmark functions (unimodal and multimodal).
• The algorithm was evaluated on five classical constrained engineering problems.
• Simulation results indicate HSMSSA outperforms SMA, SSA, and other comparative algorithms.

Conclusions:

• HSMSSA exhibits competitive performance and practical effectiveness for real-world constrained engineering problems.
• The proposed hybrid approach successfully leverages the strengths of both SSA and SMA.
• Future research directions include applications in feature selection and image segmentation.