Nature-Inspired Approach: A Novel Rat Optimization Algorithm for Global Optimization.

Area of Science:

• Computational Intelligence
• Optimization Algorithms
• Nature-Inspired Computing

Background:

• Optimization problems are prevalent in engineering and scientific research.
• Existing algorithms may suffer from slow convergence and local optima.
• Nature-inspired algorithms offer novel approaches to complex optimization tasks.

Purpose of the Study:

• To introduce a novel nature-inspired optimization algorithm, the Rat Optimization Algorithm (ROA).
• To simulate the social behaviors of rats for developing an effective optimization technique.
• To enhance the ROA's performance by incorporating the Levy flight strategy.

Main Methods:

• The Rat Optimization Algorithm (ROA) was developed, mimicking rat foraging and hunting behaviors.
• Three core operators simulate prey searching, chasing, fighting, jumping, and hunting.
• The Levy flight strategy was integrated to improve convergence speed and escape local optima.
• The algorithm was evaluated using four real-world engineering problems and 22 benchmark functions.

Main Results:

• The ROA demonstrated robust performance across diverse optimization landscapes.
• Experimental results indicated the ROA's superiority in solving real-world engineering optimization problems.
• The Levy flight strategy effectively mitigated issues of slow convergence and local optima entrapment.
• Comparative analysis showed the ROA outperforming several established optimization techniques.

Conclusions:

• The Rat Optimization Algorithm (ROA) is a promising new nature-inspired technique for optimization.
• The ROA effectively handles complex engineering optimization challenges.
• The integration of Levy flight enhances the algorithm's efficiency and global search capabilities.