Improved Gradient-Based Optimizer for solving real-world engineering problems

Abstract

Gradient-based optimizer (GBO) is one of the most promising metaheuristic algorithms, where it proved its efficiency in various fields. GBO combine two major search mechanisms population-based and gradient-based Newton. Thus, it has a strong ability in global search. However, it suffers from dealing with local search problems. In this paper, a new version introduces which integrates the feature of Simulating annealing method (SA) with the GBO (GBOSA) to enhance the local search technique. The proposed GBOSA has been compared with various popular algorithms and improved variants on a set of real-world engineering problems. The experiment results show that GBOSA outperformed the other algorithms in the literature.