Harris hawks optimization algorithm based on elite fractional mutation for data clustering

H3O-LGBM: hybrid Harris hawk optimization based light gradient boosting machine model for real-time trading

The gross domestic product (GDP) of a country is mainly dependent on its trade and external sector which improves the country's income. According to FY2021-2022, India's nominal GDP is estimated to be 3.12 trillion US dollars. Overall exports and imports have a year-over-year increase of 49.6% and 68% respectively. Machine learning techniques have the potential to improve India's Current gross value by up to 15% by the year 2035. The integration of data, Technology, and talent helps to create intelligent models that enhance artificial intelligence growth. This paper presents an optimized light gradient boosting machine (Light GBM) model using the hybrid Harris hawk optimization (H3O) algorithm for trade forecasting. The overfitting problem in the conventional Harris Hawk Optimization is overcome using the exclusive feature bundling (EFB) and the gradient-based one-side sampling (GOSS) methodologies. The H3O optimization algorithm offers fast convergence by optimizing different lightGBM parameters such as a number of training iterations, maximum depth, minimal data in the leaf, etc. To improve the performance, one step further, the residual errors of the optimized lightGBM model are corrected using the Markov Chain model. The main aim of the optimized lightGBM model is to extract the crucial input values of certain variables such as imports and exports of goods and services, service trade, and merchandise trade and predict the price movement decision. The proposed model identifies the interrelationship with the external market and future market growth along with analyzing the variation in market conditions. The prediction decision is mainly to hold, but, or sell the stocks. When evaluated using the precious metal price forecast and stock market datasets, the proposed methodology shows that the hybrid approach can enhance the prediction performance. The results show that the input parameters were efficient in predicting the economic growth regarding the Intermarket trading system (ITS) and services with higher accuracy.

Dynamic Chaotic Opposition-Based Learning-Driven Hybrid Aquila Optimizer and Artificial Rabbits Optimization Algorithm: Framework and Applications

Aquila Optimizer (AO) and Artificial Rabbits Optimization (ARO) are two recently developed meta-heuristic optimization algorithms. Although AO has powerful exploration capability, it still suffers from poor solution accuracy and premature convergence when addressing some complex cases due to the insufficient exploitation phase. In contrast, ARO possesses very competitive exploitation potential, but its exploration ability needs to be more satisfactory. To ameliorate the above-mentioned limitations in a single algorithm and achieve better overall optimization performance, this paper proposes a novel chaotic opposition-based learning-driven hybrid AO and ARO algorithm called CHAOARO. Firstly, the global exploration phase of AO is combined with the local exploitation phase of ARO to maintain the respective valuable search capabilities. Then, an adaptive switching mechanism (ASM) is designed to better balance the exploration and exploitation procedures. Finally, we introduce the chaotic opposition-based learning (COBL) strategy to avoid the algorithm fall into the local optima. To comprehensively verify the effectiveness and superiority of the proposed work, CHAOARO is compared with the original AO, ARO, and several state-of-the-art algorithms on 23 classical benchmark functions and the IEEE CEC2019 test suite. Systematic comparisons demonstrate that CHAOARO can significantly outperform other competitor methods in terms of solution accuracy, convergence speed, and robustness. Furthermore, the promising prospect of CHAOARO in real-world applications is highlighted by resolving five industrial engineering design problems and photovoltaic (PV) model parameter identification problem.

A Normal Distributed Dwarf Mongoose Optimization Algorithm for Global Optimization and Data Clustering Applications

As data volumes have increased and difficulty in tackling vast and complicated problems has emerged, the need for innovative and intelligent solutions to handle these difficulties has become essential. Data clustering is a data mining approach that clusters a huge amount of data into a number of clusters; in other words, it finds symmetric and asymmetric objects. In this study, we developed a novel strategy that uses intelligent optimization algorithms to tackle a group of issues requiring sophisticated methods to solve. Three primary components are employed in the suggested technique, named GNDDMOA: Dwarf Mongoose Optimization Algorithm (DMOA), Generalized Normal Distribution (GNF), and Opposition-based Learning Strategy (OBL). These parts are used to organize the executions of the proposed method during the optimization process based on a unique transition mechanism to address the critical limitations of the original methods. Twenty-three test functions and eight data clustering tasks were utilized to evaluate the performance of the suggested method. The suggested method’s findings were compared to other well-known approaches. In all of the benchmark functions examined, the suggested GNDDMOA approach produced the best results. It performed very well in data clustering applications showing promising performance.