Gaussian bare‐bones gradient‐based optimization: Towards mitigating the performance concerns

Abstract

Gradient‐based optimizer (GBO) is a metaphor‐free mathematic‐based algorithm proposed in recent years. Encouraged by the gradient‐based Newton's method, this algorithm combines with population‐based evolutionary methods. The disadvantage of the traditional GBO algorithm is that the global search ability of the algorithm is too strong, and the local search ability is too weak; accordingly, it is difficult to obtain the global optimal solution efficiently. Therefore, a new improved GBO algorithm (GOMGBO) is developed to mitigate such performance concerns by introducing a Gaussian bare‐bones mechanism, an opposition‐based learning mechanism, and a moth spiral mechanism enhanced GBO algorithm. The proposed GOMGBO has been compared against many famous methods and improved variants on 30 benchmark functions. The experimental results show that GOMGBO has apparent advantages in convergence speed and precision. In addition, this paper analyzes the balance and diversity of the GOMGBO algorithm and compares GOMGBO with other algorithms on several engineering problems. The experimental results show that the GOMGBO algorithm is also better than the competitive algorithm in engineering problems. This study uses the GOMGBO algorithm to optimize kernel extreme learning machine (KELM), and a new GOMGBO‐KELM model is proposed. The model is used to deal with four clinical disease diagnosis problems. Compared with GBO‐KELM, back propagation neural network algorithm, and other models, comparative experiments show that GOMGBO‐KELM has high performance in dealing with practical cases. We invite the community to investigate further our method for solving problems more efficiently with reasonable speed and efficiency. Readers of this study can refer to https://aliasgharheidari.com for any guidance about the proposed GOMGBO method.

Gradient-Based Optimizer (GBO): A Review, Theory, Variants, and Applications

“…In other words, the GBO as many different optimization algorithms cannot maintain its efficacy to solve all problems (i.e., free lunch theorem [137]). Thus, the researchers conducted some modification to fix the weaknesses of GBO, such as enhancing the speed up the convergence [44], avoiding stuck at local optima [45], the Performance [49], and balancing between exploration and exploitation [138]. These new versions can be used to solve various problems.…”

“…In [49], the authors solved the weakness of the GBO (i.e., local search mechanism) by introducing a new improved version of GBO, called GOMGBO which includes oppositionbased learning strategy, Gaussian bare-bones strategy and moth spiral strategy. Thirty benchmark functions have been applied to evaluate the performance of GOMGBO.…”

IRIME: A Novel Approach to Mitigating Exploitation-Exploration Imbalance in Rime Optimization Algorithm for Feature Selection

“…To further validate IRIME's performance, this study compared it with some advanced algorithms using the IEEE CEC 2017 benchmark test suite. These algorithms include EBOwithCMAR [80], LSHADE_cnEpSi [73], ALCPSO [81], CLPSO [82], LSHADE [83], SADE [84], JADE [85], RCBA [86], EPSO [87], CBA [88], and LWOA [89], The specific experimental data is detailed in Table A6 (Appendix). The results in the table highlight that IRIME, alongside some advanced algorithms, achieves performance near the theoretical optimum in functions such as F1, F3, F6, and F9.…”

Parameter identification of photovoltaic models using a sine cosine differential gradient based optimizer

“…A case in point is GBO, which relies on population and gradient information. Despite a new member of MAs, GBO has been applied to many problems [112][113][114] since its introduction.…”