Salp improved Northern Goshawk optimization algorithm and its application to robot path planning

• Published in: Cluster computing Vol. 28; no. 12; p. 765
• Main Authors: Wu, Changjun, Li, Qingzhen, Wang, Qiaohua, Zhang, Huanlong, Song, Xiaohui
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.11.2025; Springer Nature B.V
• Subjects: Algorithms; Collaboration; Computer Communication Networks; Computer Science; Convergence; Fault diagnosis; Foraging behavior; Operating Systems; Optimization; Optimization algorithms; Path planning; Population distribution; Processor Architectures; Robots; Searching; Strategy; Leader strategy; Crazy operator; Northern Goshawk optimization; Salp Swarm algorithm; Good points
• ISSN: 1386-7857; 1573-7543
• DOI: 10.1007/s10586-025-05500-z

The traditional Northern Goshawk optimization algorithm is prone to premature convergence and falling into local optima during the optimization process. In response to this problem, this paper integrates the leader strategy of the Salp Swarm Algorithm into the exploration stage of the Northern Goshawk optimization algorithm and proposes a Salp improved Northern Goshawk optimization algorithm (SANGO). Firstly, initialize the population using a set of good points to make the population distribution more uniform and improve the quality of the initial solution. Secondly, the leader strategy of the salp swarm algorithm is introduced to improve the exploration stage of the Northern Goshawk optimization algorithm, balancing the global and local search capabilities of the algorithm. The Levy flight strategy is added to perturb the current local optimal solution, increasing the search range to jump out of the local optimal solution, and improving the search efficiency and solving ability of the algorithm. Finally, in order to solve the premature convergence caused by the loss of population diversity in the later stage of iteration, the crazy operator is used to improve the development stage and enhance the convergence accuracy of the algorithm. Compare SANGO with DBO, GWO, POA, HHO, NGO, INGO, GODBO, DFPSO, ASFSSA, GMPBSA, MGLMRFO and HHNGO on 12 basic functions, CEC-2017, and CEC-2021 test sets. At the same time, the SANGO algorithm was applied to robot path planning, simulation experiments were conducted in different grid environments, as well as design applications in two engineering, to verify the effectiveness and practicality of the proposed algorithm.