Arctic Puffin Optimization Algorithm Integrating Opposition-Based Learning and Differential Evolution with...

Area of Science:

• Computational Intelligence
• Swarm Intelligence Algorithms
• Optimization Techniques

Background:

• Swarm intelligence algorithms, including the Arctic Puffin Optimization (APO), often face challenges like slow convergence and premature local optima.
• Balancing exploration and exploitation remains a critical issue in developing effective optimization algorithms.

Purpose of the Study:

• To introduce an improved Arctic Puffin Optimization (IAPO) algorithm designed to overcome the limitations of the original APO.
• To enhance convergence speed, accuracy, and the ability to escape local optima in optimization tasks.

Main Methods:

• Incorporation of a mirror opposition-based learning mechanism to broaden the search scope and improve solution-finding efficiency.
• Integration of a dynamic differential evolution strategy with adaptive parameters to enhance local optima escape and precision.
• Comparative experimental analysis against eight other optimization algorithms on benchmark functions (CEC2019, CEC2022) and engineering problems.

Main Results:

• The IAPO algorithm achieved superior accuracy, faster convergence, and enhanced robustness compared to existing algorithms.
• IAPO secured first-place average rankings across various benchmark test suites, including CEC2019 and CEC2022.
• The algorithm obtained optimal solutions for three engineering optimization design problems, demonstrating practical effectiveness.

Conclusions:

• The proposed IAPO algorithm effectively addresses the limitations of traditional swarm intelligence methods.
• IAPO demonstrates significant improvements in convergence speed, accuracy, and robustness for complex optimization tasks.
• The algorithm's performance on benchmark and engineering problems validates its effectiveness and practical applicability.