An Improved Mantis Search Algorithm for Solving Optimization Problems.

Area of Science:

• Computational Intelligence
• Optimization Algorithms
• Metaheuristics

Background:

• Traditional mantis search algorithm (MSA) faces challenges with slow convergence and local optima.
• Complex optimization problems require more robust and adaptive algorithms.

Purpose of the Study:

• To introduce an improved mantis search algorithm (IMSA) that overcomes the limitations of the traditional MSA.
• To enhance convergence speed, accuracy, and stability in complex optimization scenarios.

Main Methods:

• Developed an adaptive probability conversion factor for smooth transitions between exploration and exploitation phases.
• Implemented a subspace and full space probability update strategy for dynamic range adjustment.
• Introduced an elite population screening mechanism using Euclidean distance and fitness criteria.
• Designed a base vector adaptive probability selection mechanism for enhanced pertinence.
• Utilized directed random disturbance and non-greedy replacement in sexual cannibalism to maintain population diversity.

Main Results:

• The IMSA demonstrated significant advantages in convergence speed, calculation accuracy, and stability.
• Experimental results on 29 CEC2017 test functions showed superior performance compared to the original MSA and five other leading meta-heuristic algorithms.

Conclusions:

• The proposed IMSA effectively addresses the limitations of the traditional MSA.
• IMSA offers a more robust and efficient solution for complex optimization problems, outperforming existing state-of-the-art algorithms.