Best-worst northern goshawk optimizer: a new stochastic optimization method

• Published in: International Journal of Electrical and Computer Engineering (IJECE)
• Main Authors: Purba, Daru Kusuma, Faisal, Candrasyah Hasibuan
• Format: Article in Journal/Newspaper
• Language: unknown
• Published: Zenodo 2023
• Subjects: Agent system; Local search; Metaheuristic; Northern goshawk optimization; Particle swarm optimization; Stochastic optimization; Swarm intelligence; Northern Goshawk
• Online Access: https://doi.org/10.11591/ijece.v13i6.pp7016-7026

This study introduces a new metaheuristic method: the best-worst northern goshawk optimizer (BW-NGO). This algorithm is an enhanced version of the northern goshawk optimizer (NGO). Every BW-NGO iteration consists of four phases. First, each agent advances toward the best agent and away from the worst agent. Second, each agent moves relatively to the agent selected at random. Third, each agent conducts a local search. Fourth, each agent traces the space at random. The first three phases are mandatory, while the fourth phase is optional. Simulation is performed to assess the performance of BW-NGO. In this simulation, BW-NGO is confronted with four algorithms: particle swarm optimization (PSO), pelican optimization algorithm (POA), golden search optimizer (GSO), and northern goshawk optimizer (NGO). The result exhibits that BW-NGO discovers an acceptable solution for the 23 benchmark functions. BW-NGO is better than PSO, POA, GSO, and NGO in consecutively optimizing 22, 20, 15, and 11 functions. BW-NGO can discover the global optimal solution for three functions.