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The smart platform of generating, collecting, managing and processing dynamic data from different sources in the Internet of Vehicles (IoV) pave the way for a large-scale dataset to be accumulated. The dataset can contain records running into hundreds of thousands and even millions of relevant, irrelevant and redundant features. Therefore, feature selection to select only the significant features for developing vehicle collision detection alarm systems for deployment in the IoV edge is critical. However, previous studies on vehicle collision detection in the IoV have not conducted rigorous feature selection. Limited studies have mainly applied Pearson correlation coefficient (PCC) to select subset features influencing vehicle collision in the domain of IoV. However, PCC can cause relevant features to be discarded if the correlation of the non-linear association is too small, thereby providing incorrect feature ranking, which, in turn, increases the chances of developing a model that will give a poor outcome. To close this gap, this paper proposed a multi-objective, filter-based hybrid non-dominated sorted genetic algorithm III with a gain ratio and bi-directional wrapper for the selection of subset features influencing vehicle collision in the IoV. The proposed approach selected the minimal most significant subset features for developing a vehicle collision detection classifier with maximum accuracy for deployment in the IoV. A comparative study proves that the proposed approach performs better than the compared algorithms across hybrid-, wrapper- and filter-based feature selection methods within the family of the NSGA. Further, a comparative analysis with other evolutionary algorithms proves the superiority of the proposal. This study can help researchers in the future by avoiding the use of large-scale computing resources in acquiring data to develop vehicle collision alert systems in the IoV. This can be achieved since only the subset features discovered in this study are collected, as opposed to collecting large features, thus saving time and resources in the subsequent vehicle collision detection data collection in the IoV. [ABSTRACT FROM AUTHOR]

The Levenberg-Marquardt (LM) gradient descent algorithm is used extensively for the training of Artificial Neural Networks (ANN) in the literature, despite its limitations, such as susceptibility to the local minima that undermine its robustness. In this paper, a bio-inspired algorithm referring to the Bat algorithm was proposed for training the ANN, to deviate from the limitations of the LM. The proposed Bat algorithm-based LM (BALM) was simulated on 10 benchmark datasets. For evaluation of the proposed BALM, comparative simulation experiments were conducted. The experimental results indicated that the BALM was found to deviate from the limitations of the LM to advance the accuracy and convergence speed of the ANN. Also, the BALM performs better than the back-propagation algorithm, artificial bee colony trained back-propagation ANN. and artificial bee colony trained LM ANN. The results of this research provide an alternative ANN training algorithm that can be used by researchers and industries to solve complex real-world problems across numerous domains of applications. [ABSTRACT FROM AUTHOR]

When crude oil prices began to escalate in the 1970s, conventional methods were the predominant methods used in forecasting oil pricing. These methods can no longer be used to tackle the nonlinear, chaotic, non-stationary, volatile, and complex nature of crude oil prices, because of the methods’ linearity. To address the methodological limitations, computational intelligence techniques and more recently, hybrid intelligent systems have been deployed. In this paper, we present an extensive review of the existing research that has been conducted on applications of computational intelligence algorithms to crude oil price forecasting. Analysis and synthesis of published research in this domain, limitations and strengths of existing studies are provided. This paper finds that conventional methods are still relevant in the domain of crude oil price forecasting and the integration of wavelet analysis and computational intelligence techniques is attracting unprecedented interest from scholars in the domain of crude oil price forecasting. We intend for researchers to use this review as a starting point for further advancement, as well as an exploration of other techniques that have received little or no attention from researchers. Energy demand and supply projection can effectively be tackled with accurate forecasting of crude oil price, which can create stability in the oil market. [ABSTRACT FROM AUTHOR]

The efficiency of a metaheuristic algorithm for global optimization is based on its ability to search and find the global optimum. However, a good search often requires to be balanced between exploration and exploitation of the search space. In this paper, a new metaheuristic algorithm called Ringed Seal Search (RSS) is introduced. It is inspired by the natural behavior of the seal pup. This algorithm mimics the seal pup movement behavior and its ability to search and choose the best lair to escape predators. The scenario starts once the seal mother gives birth to a new pup in a birthing lair that is constructed for this purpose. The seal pup strategy consists of searching and selecting the best lair by performing a random walk to find a new lair. Affected by the sensitive nature of seals against external noise emitted by predators, the random walk of the seal pup takes two different search states, normal state and urgent state. In the normal state, the pup performs an intensive search between closely adjacent lairs; this movement is modeled via a Brownian walk. In an urgent state, the pup leaves the proximity area and performs an extensive search to find a new lair from sparse targets; this movement is modeled via a Levy walk. The switch between these two states is realized by the random noise emitted by predators. The algorithm keeps switching between normal and urgent states until the global optimum is reached. Tests and validations were performed using fifteen benchmark test functions to compare the performance of RSS with other baseline algorithms. The results show that RSS is more efficient than Genetic Algorithm, Particles Swarm Optimization and Cuckoo Search in terms of convergence rate to the global optimum. The RSS shows an improvement in terms of balance between exploration (extensive) and exploitation (intensive) of the search space. The RSS can efficiently mimic seal pups behavior to find best lair and provide a new algorithm to be used in global optimization problems. [ABSTRACT FROM AUTHOR]

The Flower Pollination Algorithm (FPA) is a novel bio-inspired optimization algorithm that mimics the real life processes of the flower pollination. In this paper, we review the applications of the Single Flower Pollination Algorithm (SFPA), Multi-objective Flower Pollination Algorithm an extension of the SFPA and the Hybrid of FPA with other bio-inspired algorithms. The review has shown that there is still a room for the extension of the FPA to Binary FPA. The review presented in this paper can inspire researchers in the bio-inspired algorithms research community to further improve the effectiveness of the PFA as well as to apply the algorithm in other domains for solving real life, complex and nonlinear optimization problems in engineering and industry. Further research and open questions were highlighted in the paper. [ABSTRACT FROM AUTHOR]

This paper proposes an alternative approach based on a genetic algorithm and neural network (GA–NN) for the prediction of the West Texas Intermediate (WTI) crude oil price. Comparative simulation results suggested that the proposed GA–NN approach is better than the baseline algorithms in terms of prediction accuracy and computational efficiency. Mann–Whitney test results indicated that the WTI crude oil price predicted by the proposed GA–NN and the observed price are statistically equal. Further comparison of the proposed GA–NN with previous studies indicated performance improvement over existing results. The proposed model can be useful in the formulation of policies related to international crude oil price estimations, development plans and industrial production. [ABSTRACT FROM AUTHOR]