Golden Search Optimization Algorithm

This study introduces an effective population-based optimization algorithm, namely the Golden Search Optimization Algorithm (GSO), for numerical function optimization. The new algorithm has a simple but strategy solving complex problems. GSO starts with random possible solutions called objects, which interact each other based on mathematical model to reach global optimum. To provide fine balance between explorative and exploitative behavior of search, proposed method utilizes transfer operator in adaptive step size adjustment scheme. is benchmarked 23 unimodal, multimodal, fixed dimensional functions results are verified by comparative well-known Gravitational (GSA), Sine-Cosine (SCA), Grey Wolf (GWO), Tunicate Swarm (TSA). In addition, nonparametric Wilcoxon’s rank sum test performed measure pair-wise statistical performance valid judgment about algorithm. simulation demonstrate that superior could generate better optimal when compared competitive algorithms.