Multi-Strategy Improved Northern Goshawk Optimization Algorithm and Application

Abstract

The Northern Goshawk Optimization Algorithm (NGO) is a population-based meta-heuristic algorithm inspired by the hunting behavior of the northern goshawk. Compared with other algorithms, NGO have a certain competitiveness, but there is still an imbalance in development and exploration, and it is easy to fall into the local optimal. This paper proposes a multi-strategy Improved Northern Goshawk optimization algorithm (INGO) to address these shortcomings. INGO uses an improved tent chaos mapping strategy to generate the initial population and improve the quality of the initial solution set. The levy flight strategy is introduced in the hunting stage of the NGO to improve the search range of the solution and avoid the algorithm’s prematurity. In addition, we introduce a nonlinear convergence factor and heart-shaped search strategy to reduce the probability of the algorithm falling into the local optimal and improve the convergence speed of the algorithm. We evaluated INGO’s performance using 23 benchmark functions, CEC2017 test suite functions, and three constrained engineering optimization problems and validated its optimization using Wilcoxon rank sum tests. Experimental results show that the algorithm has higher convergence accuracy and better detection ability. Finally, INGO was applied to optimize the ensemble learning system.