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A hybrid northern goshawk optimization algorithm based on cluster collaboration
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AI
DOI：10.1007/s10586-024-04571-8.png)
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Abstract
En 中文
To address the problems that the northern goshawk optimization algorithm (NGO) has a slow convergence speed and is highly susceptible to fall into local optimal solutions, this paper proposes a hybrid northern goshawk optimization algorithm based on cluster collaboration (HHNGO), which effectively improves the convergence speed and alleviates the problem of falling into the local optimum. Firstly, piecewise chaotic mapping is used to initialize the population, which makes the initial population more evenly distributed in the search space and improves the quality of the initial solution. Secondly, the prey recognition position update formula in the harris hawk optimization algorithm is introduced to improve the exploration phase. Meanwhile, a nonlinear factor can be added to accelerate the process which reaches the minimum difference between the prey best position and the average position of the eagle group. Thus the iteration number is reduced during the search process, and the convergence speed of the algorithm is improved. Finally, the Cauchy variation strategy is used to perturb the optimal solution of the algorithm. Then, its probability jumping out of the local optimal solution is increased, and the global search capability is enhanced. The experimental comparison is carried out to analyze the 12 standard functions, CEC-2019 and CEC-2021 test functions in HHNGO and PSO, GWO, POA, HHO, NGO, INGO, DFPSO, MGLMRFO, GMPBSA algorithms, and HHNGO is applied in PID parameter rectification. The results prove the feasibility and superiority of the proposed method.
Keywords:
Northern goshawk optimization
Harris hawk optimization
Exploration phase
Aonlinear factor
Cauchy variation
Journal
C
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4.1
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5.0K
Citations:
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