Random-guided optimizer: a metaheuristic that shifts random search to guided search through iteration

• Main Authors: Kusuma, Purba Daru, Hasibuan, Faisal Candrasyah
• Other Authors: Telkom University
• Format: Article in Journal/Newspaper
• Language: English
• Published: Institute of Advanced Engineering and Science 2024
• Subjects: Random-guided optimizer: a metaheuristic that shifts random search to guided search through iteration; Metaheuristic; Northern goshawk optimization; Optimization; Random motion; Slime mold algorithm; Stochastic process; Swarm intelligence; Northern Goshawk
• Online Access: https://beei.org/index.php/EEI/article/view/6507; https://doi.org/10.11591/eei.v13i4.6507

This study offers a new swarm-based metaheuristic: random-guided optimizer (RGO). RGO has novel mechanics in shifting the random motion into a guided motion strategy during the iteration. In RGO, the iteration is divided into three equal size phases. In the first phase, the unit walks randomly inside the search space to tackle the local optimal problem earlier. In the second phase, each unit uses a unit selected randomly among the population as a reference in conducting the guided motion. In the third phase, each unit conducts guided motion toward or surpasses the best unit. Through simulation, RGO successfully finds the acceptable solution for 23 benchmark functions. Moreover, RGO successfully finds the global optimal solution for four functions: Branin, Goldstein-Price, Six Hump Camel, and Schwefel 2.22. RGO also outperforms slime mold algorithm (SMA), pelican optimization algorithm (POA), golden search optimizer (GSO), and northern goshawk optimizer (NGO) in solving 12, 20, 12, and 1 function consecutively. In the future, improvement can be made by transforming RGO into solid multiple-phase strategy without losing its identity as a metaheuristic with multiple strategy in every iteration.