An efficient chaotic mutative moth-flame-inspired optimizer for global optimization tasks

• Published in: Expert systems with applications Vol. 129; pp. 135 - 155
• Main Authors: Xu, Yueting, Chen, Huiling, Heidari, Ali Asghar, Luo, Jie, Zhang, Qian, Zhao, Xuehua, Li, Chengye
• Format: Journal Article
• Language: English
• Published: New York Elsevier Ltd 01.09.2019; Elsevier BV
• Subjects: Algorithms; Artificial neural networks; Butterflies & moths; Chaotic local search; Convergence; Gaussian mutation; Global optimization; Heuristic methods; Hybrid systems; Kernel extreme learning machine; Kernels; Moth-flame optimization algorithm; Mutation; Parameter optimization; Performance prediction; Task complexity; Parameter optimization; Chaotic local search; Moth-flame optimization algorithm; Gaussian mutation; Kernel extreme learning machine
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2019.03.043

•This paper proposes a boosted MFO for global search and kernel extreme learning machines.•Two strategies have been introduced into MFO for a more stable balance.•The extensive results on benchmark problems and real datasets have been performed.•A hybrid kernel extreme learning machine model is established for financial stress prediction. [Display omitted] Moth-flame optimization algorithm (MFO) is a new nature-inspired meta-heuristic based on the navigation routine of moths in the environment known as transverse orientation. For some complex optimization tasks, especially high dimensional and multimodal problems, the conventional MFO may face problems in the convergence trends or be trapped into the local and deceptive optima. Therefore, in this study, two strategies have been introduced into the conventional MFO to get a more stable sense of balance between the exploration and exploitation propensities. First, Gaussian mutation is employed to increase the population diversity of MFO. Then, a chaotic local search is applied to the flame updating process of MFO for better exploiting the locality of the solutions. The proposed CLSGMFO approach was compared against a wide range of well-known classical metaheuristic algorithms (MAs) and various advanced MAs using 23 classical benchmark functions. It was shown that the designed CLSGMFO can outperform most of the popular MAs in terms of solution quality and convergence speed. Moreover, based on CLSGMFO, a hybrid kernel extreme learning machine model, which is called CLSGMFO-KELM, is established to deal with financial stress prediction scenarios. To investigate the effectiveness of the CLSGMFO-KELM model, the proposed hybrid system was tested on two widely used financial datasets and compared against a broad array of popular classifiers. The results demonstrate that the proposed learning scheme can offer a superior kernel extreme learning machine model with excellent predictive performance. Accordingly, the proposed CLSGMFO can serve as an effective and efficient computer-aided tool for financial prediction.