Hybrid Intelligent Approaches Research Articles

Performance efficiency of data-based hybrid intelligent approaches to predict crest settlement in rockfill dams

Performance efficiency of data-based hybrid intelligent approaches to predict crest settlement in rockfill dams

A data-driven assessment model for collision responses of offshore platform structure with ship using hybrid intelligent approaches

The accidents of ship collision with offshore platform bring huge risks to the safety of jacket structure. This paper proposed a data-driven assessment model for collision responses of offshore platform structure by integrating several intelligent and data-driven approaches. In the model, artificial neural network combining with dynamic particle swarm optimization algorithm (DPSO-ANN) is addressed to predict the collision responses of offshore platform structure, in which the uniform design method (UDM) is adopted to select training data. Principal component analysis (PCA) is used to eliminate correlation and redundancy of multivariate data of collision responses. Further, based on grey relational analysis (GRA), a GRA-based assessment index is established for the collision responses. In the case study, the prediction results demonstrate that the DPSO-ANN model could approximate the FEA (Finite Element Analysis) well, and the results of GRA-based assessment indicate that the collision velocity and mass of ship are two key factors influencing the collision responses of offshore structure. Also, the assessment results show that it is necessary to pay more attention and vigilance to the situations of high-speed approaching of large tonnage ship and ship berthing at low water level. The proposed model could serve as a beneficial and efficient assessment tool to support safety design of offshore platform structure under collision accidents.

Hybrid and enhanced PSO: Novel first order reliability method-based hybrid intelligent approaches

Computing the sensitivity vector in the traditional first order reliability method may provide inaccurate reliability outcomes for discrete performance functions and inefficient computation burden for high-dimensional problems. In this study, two improved particle swarm optimization algorithms are proposed to enhance the convergence rate with global optimal results during the structural reliability analysis. The abilities for convergence speed and global convergence of the particle swarm optimization algorithm are improved using a novel hybrid method called particle swarm optimization-based harmony search algorithm (PSO–HS), and enhanced particle swarm optimization (EPSO). The proposed methods use a dynamic self-adaptive term to execute the local adjusting process. Using twelve numerical-based engineering problems, the structural reliability frameworks developed based on modified versions of particle swarm optimization algorithms are compared to numerous FORM algorithms and the current metaheuristic methods. Results indicated that the novel proposed methods using the improved PSO algorithms are more robust and efficient than the analytical FORM methods for solving high-dimensional engineering problems. Furthermore, compared to the previous metaheuristic approaches, the suggested methods enabled faster convergence.

Path optimization for multiple humanoid robot using TLBO based ANFIS controller in obscure environment

Humanoid robot NAO is an intelligent reprogrammable agent designed to execute desired work using wireless control system by sensing the working environment at the same time. This article concentrates on the analysis of hybrid intelligent navigation approaches for single and multiple humanoid NAOs. It describes the optimization of a collision-free path in a static and dynamic terrain. The hybridization introduced is designed in two steps. ANFIS (Adaptive Network-based Fuzzy Inference System) controller produces a transitional driving angle (TDA) using a robot location with respect to obstacle distance in all directions. It is fed to Teacher Learner based Optimization (TLBO) approach that produces optimum driving angle (ODA) for the humanoid robot to guide along with its predefined target. It optimizes the path selection by taking Euclidean distance and ODA as the key factor. The hybridized controller is examined in an environment that consists of single and multiple NAOs. Case of inter-collision may occur in the path planning of multiple humanoid NAOs. It is eliminated with the integration of the dining philosopher controller in the base algorithm to prioritized one robot towards the assigned target. Simulation results demonstrate the proposed controller's efficacy and explain that it gives optimized path length and travel time. In addition, a comparative study with previously applied effective algorithms is done.

Hybrid intelligent phishing website prediction using deep neural networks with genetic algorithm‐based feature selection and weighting

In recent years, the web phishing attack has become one of the most serious web security problems, in which the phishers can steal significant financial information about the internet users to carry out financial thefts. Several blacklist-based conventional phishing website detection methods are used to predict the phishing websites. However, numerous phishing websites are not predicted precisely by these blacklist-based conventional methods since many new phishing websites are constantly developed and launched on the Web over time. In this study, hybrid intelligent phishing website prediction using deep neural networks (DNNs) with evolutionary algorithm-based feature selection and weighting methods are suggested to enhance the phishing website prediction. In the proposed hybrid intelligent phishing website prediction approaches, the most influential features and the optimal weights of website features are heuristically identified with the genetic algorithm (GA) to help in increasing the accuracy of phishing website prediction. Accordingly, the website features selected and weighted by the GA are utilised to train DNNs to accurately predict the phishing websites. The experimental results demonstrated that the proposed hybrid intelligent phishing website prediction approaches achieved significantly higher classification accuracy, sensitivity, specificity, and geometric mean in phishing website prediction compared to those proposed in other studies.

The re-optimization strategy of multi-layer hybrid building’s cooling and heating load soft sensing technology research based on temperature interval and hierarchical modeling techniques

To avoid expanding existing fossil power plants, building the energy performance management system is an effect method to cope with a high growth demand of electricity in China's urbanization process. The accuracy thermal load prediction is the first step to build an energy performance management system which has important significance for energy saving, decreasing air pollution and high-efficiency operation of HVAC system.This paper discusses the use of hybrid intelligent approaches and re-optimization strategy, a multi-layer hybrid model (APNN) has been proposed by hybridizing an auto-regressive model with exogenous inputs (ARX) and a particle swarm optimization neural network (PSO-NN) to make a good use of the comprehensive information of the meteorological data and historical data. For the purposes of improving prediction precision generalization ability of the multi-layer hybrid model, temperature interval and hierarchical modeling techniques were used. According to the re-optimization strategy, there are two improvements of the previous proposed APNN model, which are based on temperature interval and hierarchical modeling by solar radiation intensity. Compared with the basic prediction models, validation results show that accuracy of the optimized models are greatly improved. What’s more, the optimization of multi-layer hybrid building’s cooling and heating load soft sensing technology enhance learning and generalization capability of the basic APNN model.

A hybrid intelligence-aided approach to affect-sensitive e-learning

E-Learning has revolutionized the delivery of learning through the support of rapid advances in Internet technology. Compared with face-to-face traditional classroom education, e-learning lacks interpersonal and emotional interaction between students and teachers. In other words, although a vital factor in learning that influences a human's ability to solve problems, affect has been largely ignored in existing e-learning systems. In this study, we propose a hybrid intelligence-aided approach to affect-sensitive e-learning. A system has been developed that incorporates affect recognition and intervention to improve the learner's learning experience and help the learner become better engaged in the learning process. The system recognizes the learner's affective states using multimodal information via hybrid intelligent approaches, e.g., head pose, eye gaze tracking, facial expression recognition, physiological signal processing and learning progress tracking. The multimodal information gathered is fused based on the proposed affect learning model. The system provides online interventions and adapts the online learning material to the learner's current learning state based on pedagogical strategies. Experimental results show that interest and confusion are the most frequently occurring states when a learner interacts with a second language learning system and those states are highly related to learning levels (easy versus difficult) and outcomes. Interventions are effective when a learner is disengaged or bored and have been shown to help learners become more engaged in learning.

A survey on evolutionary algorithm based hybrid intelligence in bioinformatics.

With the rapid advance in genomics, proteomics, metabolomics, and other types of omics technologies during the past decades, a tremendous amount of data related to molecular biology has been produced. It is becoming a big challenge for the bioinformatists to analyze and interpret these data with conventional intelligent techniques, for example, support vector machines. Recently, the hybrid intelligent methods, which integrate several standard intelligent approaches, are becoming more and more popular due to their robustness and efficiency. Specifically, the hybrid intelligent approaches based on evolutionary algorithms (EAs) are widely used in various fields due to the efficiency and robustness of EAs. In this review, we give an introduction about the applications of hybrid intelligent methods, in particular those based on evolutionary algorithm, in bioinformatics. In particular, we focus on their applications to three common problems that arise in bioinformatics, that is, feature selection, parameter estimation, and reconstruction of biological networks.

On the potential contributions of hybrid intelligent approaches to Multicomponent Robotic System development

The area of cognitive or intelligent robotics is moving from the single monolithic robot control and behavior problem to that of controlling robots with multiple components or multiple robots operating together, and even collaborating, in dynamic and unstructured environments. This paper introduces the topic and provides a general overview of the current state of the field of Multicomponent Robotic Systems focusing on providing some insights into where Hybrid Intelligent Systems could provide key contributions to its advancement. Thus, the aim is to identify prospective research areas and to try to delimit the field from the point of view of the following essential problem: how to coordinate multiple robotic elements in order to perform useful tasks.