An effective solution to numerical and multi-disciplinary design optimization problems using chaotic slime mold...

Area of Science:

• Computational Intelligence
• Optimization Algorithms
• Meta-heuristics

Background:

• Meta-heuristic algorithms, like the slime mold algorithm (SMA), are prone to getting trapped in local minima.
• Enhancing the exploitation phase is crucial for improving algorithm performance.

Purpose of the Study:

• To introduce a novel chaotic slime mold algorithm (CSMA) by integrating a sinusoidal chaotic function with the basic SMA.
• To improve the exploitation capabilities and solution accuracy of the SMA.

Main Methods:

• A sinusoidal chaotic function was combined with the standard slime mold algorithm (SMA).
• The proposed Chaotic Slime Mold Algorithm (CSMA) was applied to 23 standard test functions and 10 multidisciplinary design problems.
• CSMA's performance was benchmarked against various established optimization algorithms (PSO, DE, SSA, MVO, GWO, MFO, SCA, CS, TSA, GA, HS, ACO, MMA, etc.).

Main Results:

• The chaotic strategy significantly improved the performance of the slime mold algorithm (SMA) in terms of solution accuracy.
• CSMA demonstrated superior convergence and outperformed existing algorithms on most benchmark functions and engineering design problems.
• Statistical analysis confirmed the effectiveness of the chaotic enhancement.

Conclusions:

• The integration of chaotic functions, specifically a sinusoidal chaotic function, effectively enhances the exploitation phase of the slime mold algorithm (SMA).
• The developed Chaotic Slime Mold Algorithm (CSMA) offers improved accuracy and convergence, outperforming traditional optimizers on complex problems.
• CSMA presents a promising advancement in meta-heuristic optimization techniques for diverse applications.