Enhanced Multi-Strategy Improved Animated Oat Optimization Algorithm and Its Engineering Application

Area of Science:

• Computational Intelligence
• Optimization Algorithms
• Metaheuristics

Background:

• Traditional Animated Oat Optimization Algorithm (AOO) suffers from poor initial population distribution and inadequate balance between global exploration and local exploitation.
• Limitations hinder performance in complex optimization tasks.

Purpose of the Study:

• Propose an Enhanced Animated Oat Optimization Algorithm (EAOO) to overcome AOO limitations.
• Improve population initialization, dynamic search balance, and convergence speed.
• Validate EAOO performance on benchmark functions and engineering problems.

Main Methods:

• Introduced Sinusoidal chaotic map for enhanced initial population diversity and spatial distribution.
• Integrated a nonlinear disturbance factor for adaptive global exploration-local exploitation balance.
• Implemented an adaptive t-distribution mutation operator with dynamic selection for improved convergence and premature convergence avoidance.

Main Results:

• EAOO demonstrated superior optimization accuracy, faster convergence speed, and enhanced robustness compared to traditional algorithms on CEC2017 and CEC2020 benchmark suites.
• Statistical tests confirmed significant performance improvements.
• EAOO achieved better structural design parameters and reduced manufacturing costs in welded beam and pressure vessel design problems.

Conclusions:

• EAOO effectively addresses limitations of the traditional AOO algorithm.
• The proposed multi-strategy enhancements lead to superior performance in complex optimization tasks.
• EAOO shows significant practical value for high-dimensional, nonlinear, constrained engineering optimization problems.