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Purpose: This study aims to present a unique hybrid metaheuristic approach to solving the nonlinear analysis of hall currents and electric double layer (EDL) effects in multiphase wavy flow by merging the firefly algorithm (FA) and the water cycle algorithm (WCA). Design/methodology/approach: Nonlinear Hall currents and EDL effects in multiphase wavy flow are originally described by partial differential equations, which are then translated into an ordinary differential equation model. The hybrid FA-WCA technique is used to take on the optimization challenge and find the best possible design weights for artificial neural networks. The fitness function is efficiently optimized by this hybrid approach, allowing the optimal design weights to be determined. Findings: The proposed strategy is shown to be effective by taking into account multiple variables to arrive at a single answer. The numerical results obtained from the proposed method exhibit good agreement with the reference solution within finite intervals, showcasing the accuracy of the approach used in this study. Furthermore, a comparison is made between the presented results and the reference numerical solutions of the Hall Currents and electroosmotic effects in multiphase wavy flow problem. Originality/value: This comparative analysis includes various performance indices, providing a statistical assessment of the precision, efficiency and reliability of the proposed approach. Moreover, to the best of the authors' knowledge, this is a new work which has not been explored in existing literature and will add new directions to the field of fluid flows to predict most accurate results. [ABSTRACT FROM AUTHOR]

Purpose Predicting customers’ purchase behaviors is a challenging task. The literature has introduced the individual-level and the segment-based predictive modeling approaches for this purpose. Each method has its own advantages and drawbacks, and performs in certain cases. The purpose of this paper is to propose a hybrid approach which predicts customers’ individual purchase behaviors and reduces the limitations of these two methods by combining the advantages of them.Design/methodology/approach The proposed hybrid approach is established based on individual-level and segment-based approaches and utilizes the historical transactional data and predictive algorithms to generate predictions. The effectiveness of the proposed approach is experimentally evaluated in the domain of supermarket shopping by using real-world data and using five popular machine learning classification algorithms including logistic regression, decision trees, support vector machines, neural networks and random forests.Findings A comparison of results shows that the proposed hybrid approach substantially outperforms the individual-level and the segment-based approaches in terms of prediction coverage while maintaining roughly comparable prediction accuracy to the individual-level method. Moreover, the experimental results demonstrate that logistic regression performs better than the other classifiers in predicting customer purchase behavior.Practical implications The study concludes that the proposed approach would be beneficial for enterprises in terms of designing customized services and one-to-one marketing strategies.Originality/value This study is the first attempt to adopt a hybrid approach combining individual-level and segment-based approaches to predict customers’ individual purchase behaviors. [ABSTRACT FROM AUTHOR]

Highlights: [•] This paper proposes a novel hybrid approach for wind speed prediction. [•] SAM is applied to simulate and predict the seasonal component in wind speed series. [•] ESM and RBFN mainly capture the linear and nonlinear patterns of the trend component in wind speed series. [•] The wind speed datasets in the Hexi Corridor of China are used to demonstrate the proposed approach. [•] It is concluded that the proposed approach can improve the prediction accuracy. [Copyright &y& Elsevier]

Abstract: In recent years, the Particle Swarm Optimization has rapidly gained increasing popularity and many variants and hybrid approaches have been proposed to improve it. In this paper, motivated by the behavior and the spatial characteristics of the social and cognitive experience of each particle in the swarm, we develop a hybrid framework that combines the Particle Swarm Optimization and the Differential Evolution algorithm. Particle Swarm Optimization has the tendency to distribute the best personal positions of the swarm particles near to the vicinity of problem’s optima. In an attempt to efficiently guide the evolution and enhance the convergence, we evolve the personal experience or memory of the particles with the Differential Evolution algorithm, without destroying the search capabilities of the algorithm. The proposed framework can be applied to any Particle Swarm Optimization algorithm with minimal effort. To evaluate the performance and highlight the different aspects of the proposed framework, we initially incorporate six classic Differential Evolution mutation strategies in the canonical Particle Swarm Optimization, while afterwards we employ five state-of-the-art Particle Swarm Optimization variants and four popular Differential Evolution algorithms. Extensive experimental results on 25 high dimensional multimodal benchmark functions along with the corresponding statistical analysis, suggest that the hybrid variants are very promising and significantly improve the original algorithms in the majority of the studied cases. [Copyright &y& Elsevier]

The coordinated path following problem, steering a fleet of unicycles along a path while achieving a desired inter-vehicle formation, is investigated. The path is a paved road in the world or a marked curve on the floor of work area, for which each vehicle can measure the distance to the path and the heading error as well as the curvature of the path segment in its sensing range. Moreover, the vehicles can measure the arc distances of their neighbors that lie in their sensing range. A hybrid control approach is proposed to solve the problem. Depending on the posture with respect to the partly observed path, each vehicle either interacts with one of the others or works at the single-agent level. It is shown that the path following error of each vehicle is eventually reduced to zero and vehicles of every weakly connected component asymptotically converge to formations with equal arc distances. [ABSTRACT FROM AUTHOR]