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

Automated summary

The Golden Search Optimization Algorithm (GSO) is an effective population-based optimization algorithm that uses random solutions and a simple mathematical model to reach global optimum. The algorithm utilizes a transfer operator for adaptive step size adjustment to balance explorative and exploitative behavior in the search.