A Multi-Strategy Improved Northern Goshawk Optimization Algorithm for Optimizing Engineering Problems.

Area of Science:

• Computational Intelligence
• Optimization Algorithms
• Metaheuristic Computing

Background:

• The Northern Goshawk Optimization (NGO) algorithm is an efficient metaheuristic but suffers from local optima entrapment and slow convergence.
• Addressing these limitations is crucial for improving the practical applicability of optimization techniques.

Purpose of the Study:

• To introduce the Multi-Strategy Improved Northern Goshawk Optimization (MSINGO) algorithm.
• To enhance the exploration, exploitation, and convergence speed of the original NGO algorithm.

Main Methods:

• The MSINGO algorithm integrates cubic mapping, weighted stochastic difference mutation, and weighted sine and cosine optimization strategies into the NGO framework.
• Performance evaluation involved comparative experiments using CEC2017 test functions against five highly cited and six recent metaheuristic algorithms.
• The algorithm's effectiveness was further validated on six real-world engineering problems.

Main Results:

• MSINGO significantly outperformed competitive algorithms in exploitation ability, exploration ability, local optimal avoidance, and scalability on CEC2017 test functions.
• Experimental results indicate a substantial improvement over the original NGO and other benchmark algorithms.
• The algorithm demonstrated practical utility and potential when applied to real-world engineering challenges.

Conclusions:

• The proposed MSINGO algorithm effectively overcomes the drawbacks of the original NGO, particularly in avoiding local optima and accelerating convergence.
• MSINGO exhibits robust performance and superior capabilities compared to existing metaheuristic algorithms.
• The study highlights MSINGO's potential for solving complex optimization problems in various domains.