MSAO-EDA: A Modified Snow Ablation Optimizer by Hybridizing with Estimation of Distribution Algorithm.

Area of Science:

• Optimization Algorithms
• Computational Intelligence
• Metaheuristics

Background:

• Metaheuristic algorithms are crucial for solving complex optimization problems.
• The snow ablation algorithm (SAO) is a physics-based metaheuristic but suffers from over-exploitation and local optima.
• Balancing global and local search remains a challenge in existing algorithms.

Purpose of the Study:

• To enhance the performance of the snow ablation algorithm (SAO).
• To address SAO's limitations in exploration-exploitation balance and local optima avoidance.
• To propose a novel hybrid algorithm, MSAO-EDA, for improved numerical optimization.

Main Methods:

• Developed a modified snow ablation algorithm hybrid distribution estimation algorithm (MSAO-EDA).
• Implemented a collaborative search framework integrating SAO and EDA.
• Introduced an offset EDA approach to replace SAO's exploration strategy.
• Utilized a greedy strategy to accelerate agent convergence.

Main Results:

• MSAO-EDA demonstrated excellent efficiency in numerical optimization tasks.
• The proposed algorithm showed competitive performance against various advanced metaheuristic algorithms.
• Experimental results on CEC 2017 and CEC 2022 test suites validate MSAO-EDA's effectiveness.

Conclusions:

• MSAO-EDA effectively overcomes the limitations of the original SAO.
• The hybrid approach significantly improves exploration and exploitation capabilities.
• MSAO-EDA represents a highly competitive and efficient variant for complex optimization problems.