Special Issue "Swarm Information Acquisition and Swarm Intelligence in Engineering"

A special issue of Information (ISSN 2078-2489).

Deadline for manuscript submissions: closed (1 June 2015)

Special Issue Editors

Guest Editor
Dr. Baozhen Yao

School of Automotive Engineering, Dalian University of Technology, Dalian 116024, China
E-Mail
Interests: artificial intelligence; urban logistics; public transportation
Guest Editor
Prof. Dr. Yudong Zhang

1 School of information science and technology, Nanjing Normal University, Nanjing, China
2 Columbia University, New York, NY, USA
Website | E-Mail
Interests: magnetic resonance imaging; computer vision; machine learning; pattern recognition; machine vision; artificial neural network; support vector machine; swarm intelligence; global optimization

Special Issue Information

Dear Colleagues,

Swarm intelligence (SI) is an artificial intelligence technique based on the study of the behavior of simple individuals (e.g., ant colonies, bird flocking, animal herding and honey bees) in various decentralized systems. The population, which consists of simple individuals, can usually solve complex tasks in nature by individuals interacting locally with one another and with their environment. Although their behaviors are primarily characterized by autonomy, distributed functioning and self-organizing capacities, local interactions among the individuals often cause a global optimal.

Recently, SI algorithms have attracted much attention from researchers and have also been applied successfully to solve optimization problems in engineering. However, for large and complex problems, SI algorithms consume often much computation time due to stochastic feature of the search approaches. Therefore, there is a potential requirement to develop an efficient algorithm to find solutions under limited time and financial resources in real-world applications.

The aim of this special issue is to highlight the most significant recent developments in the topics of SI and to apply SI algorithms in a real-life scenario. Contributions containing new insights and findings in this field are welcome.

Dr. Baozhen Yao
Prof. Dr. Yudong Zhang
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Information is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 350 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.


Keywords

  • benchmarking and evaluation of new si algorithms
  • Ÿconvergence proof for si algorithms
  • Ÿcomparative theoretical and empirical studies on si algorithms (e.g., ant colony optimization, particle swarm optimization, artificial bee swarm algorithm, bacterial foraging optimization, artificial fish algorithm, …)
  • Ÿsi algorithms for real-world applications

Published Papers (5 papers)

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Research

Open AccessArticle Travel Mode Detection Based on Neural Networks and Particle Swarm Optimization
Information 2015, 6(3), 522-535; doi:10.3390/info6030522
Received: 2 June 2015 / Revised: 18 August 2015 / Accepted: 18 August 2015 / Published: 21 August 2015
Cited by 1 | PDF Full-text (750 KB) | HTML Full-text | XML Full-text
Abstract
The collection of massive Global Positioning System (GPS) data from travel surveys has increased exponentially worldwide since the 1990s. A number of methods, which range from rule-based to advanced classification approaches, have been applied to detect travel modes from GPS positioning data collected
[...] Read more.
The collection of massive Global Positioning System (GPS) data from travel surveys has increased exponentially worldwide since the 1990s. A number of methods, which range from rule-based to advanced classification approaches, have been applied to detect travel modes from GPS positioning data collected in travel surveys based on GPS-enabled smartphones or dedicated GPS devices. Among these approaches, neural networks (NNs) are widely adopted because they can extract subtle information from training data that cannot be directly obtained by human or other analysis techniques. However, traditional NNs, which are generally trained by back-propagation algorithms, are likely to be trapped in local optimum. Therefore, particle swarm optimization (PSO) is introduced to train the NNs. The resulting PSO-NNs are employed to distinguish among four travel modes (walk, bike, bus, and car) with GPS positioning data collected through a smartphone-based travel survey. As a result, 95.81% of samples are correctly flagged for the training set, while 94.44% are correctly identified for the test set. Results from this study indicate that smartphone-based travel surveys provide an opportunity to supplement traditional travel surveys. Full article
(This article belongs to the Special Issue Swarm Information Acquisition and Swarm Intelligence in Engineering)
Open AccessArticle Optimization of China Crude Oil Transportation Network with Genetic Ant Colony Algorithm
Information 2015, 6(3), 467-480; doi:10.3390/info6030467
Received: 12 June 2015 / Revised: 30 June 2015 / Accepted: 3 August 2015 / Published: 12 August 2015
PDF Full-text (814 KB) | HTML Full-text | XML Full-text
Abstract
Taking into consideration both shipping and pipeline transport, this paper first analysed the risk factors for different modes of crude oil import transportation. Then, based on the minimum of both transportation cost and overall risk, a multi-objective programming model was established to optimize
[...] Read more.
Taking into consideration both shipping and pipeline transport, this paper first analysed the risk factors for different modes of crude oil import transportation. Then, based on the minimum of both transportation cost and overall risk, a multi-objective programming model was established to optimize the transportation network of crude oil import, and the genetic algorithm and ant colony algorithm were employed to solve the problem. The optimized result shows that VLCC (Very Large Crude Carrier) is superior in long distance sea transportation, whereas pipeline transport is more secure than sea transport. Finally, this paper provides related safeguard suggestions on crude oil import transportation. Full article
(This article belongs to the Special Issue Swarm Information Acquisition and Swarm Intelligence in Engineering)
Open AccessArticle Improved Genetic Algorithm Optimization for Forward Vehicle Detection Problems
Information 2015, 6(3), 339-360; doi:10.3390/info6030339
Received: 30 May 2015 / Revised: 26 June 2015 / Accepted: 6 July 2015 / Published: 10 July 2015
Cited by 1 | PDF Full-text (4470 KB) | HTML Full-text | XML Full-text
Abstract
Automated forward vehicle detection is an integral component of many advanced driver-assistance systems. The method based on multi-visual information fusion, with its exclusive advantages, has become one of the important topics in this research field. During the whole detection process, there are two
[...] Read more.
Automated forward vehicle detection is an integral component of many advanced driver-assistance systems. The method based on multi-visual information fusion, with its exclusive advantages, has become one of the important topics in this research field. During the whole detection process, there are two key points that should to be resolved. One is to find the robust features for identification and the other is to apply an efficient algorithm for training the model designed with multi-information. This paper presents an adaptive SVM (Support Vector Machine) model to detect vehicle with range estimation using an on-board camera. Due to the extrinsic factors such as shadows and illumination, we pay more attention to enhancing the system with several robust features extracted from a real driving environment. Then, with the introduction of an improved genetic algorithm, the features are fused efficiently by the proposed SVM model. In order to apply the model in the forward collision warning system, longitudinal distance information is provided simultaneously. The proposed method is successfully implemented on a test car and evaluation experimental results show reliability in terms of both the detection rate and potential effectiveness in a real-driving environment. Full article
(This article belongs to the Special Issue Swarm Information Acquisition and Swarm Intelligence in Engineering)
Open AccessArticle An Approach to an Intersection Traffic Delay Study Based on Shift-Share Analysis
Information 2015, 6(2), 246-257; doi:10.3390/info6020246
Received: 10 April 2015 / Revised: 13 May 2015 / Accepted: 2 June 2015 / Published: 8 June 2015
PDF Full-text (763 KB) | HTML Full-text | XML Full-text
Abstract
Intersection traffic delay research has traditionally placed greater emphasis on the study of through and left-turning vehicles than right-turning ones, which often renders existing methods or models inapplicable to intersections with heavy pedestrian and non-motorized traffic. In the meantime, there is also a
[...] Read more.
Intersection traffic delay research has traditionally placed greater emphasis on the study of through and left-turning vehicles than right-turning ones, which often renders existing methods or models inapplicable to intersections with heavy pedestrian and non-motorized traffic. In the meantime, there is also a need for understanding the relations between different types of delay and how they each contribute to the total delay of the entire intersection. In order to address these issues, this paper first examines models that focus on through and left-turn traffic delays, taking into account the presence of heavy mixed traffic flows that are prevalent in developing countries, then establishes a model for calculating right-turn traffic delay and, last, proposes an approach to analyzing how much each of the three types of traffic delay contributes to the total delay of the intersection, based on the application of shift-share analysis (SSA), which has been applied extensively in the field of economics. Full article
(This article belongs to the Special Issue Swarm Information Acquisition and Swarm Intelligence in Engineering)
Open AccessArticle Identifying Travel Mode with GPS Data Using Support Vector Machines and Genetic Algorithm
Information 2015, 6(2), 212-227; doi:10.3390/info6020212
Received: 15 April 2015 / Revised: 21 May 2015 / Accepted: 27 May 2015 / Published: 4 June 2015
PDF Full-text (838 KB) | HTML Full-text | XML Full-text
Abstract
Travel mode identification is one of the essential steps in travel information detection with Global Positioning System (GPS) survey data. This paper presents a Support Vector Classification (SVC) model for travel mode identification with GPS data. Genetic algorithm (GA) is employed for optimizing
[...] Read more.
Travel mode identification is one of the essential steps in travel information detection with Global Positioning System (GPS) survey data. This paper presents a Support Vector Classification (SVC) model for travel mode identification with GPS data. Genetic algorithm (GA) is employed for optimizing the parameters in the model. The travel modes of walking, bicycle, subway, bus, and car are recognized in this model. The results indicate that the developed model shows a high level of accuracy for mode identification. The estimation results also present GA’s contribution to the optimization of the model. The findings can be used to identify travel mode based on GPS survey data, which will significantly enhance the efficiency and accuracy of travel survey and data processing. By providing crucial trip information, the results also contribute to the modeling and analyzing of travel behavior and are readily applicable to a wide range of transportation practices. Full article
(This article belongs to the Special Issue Swarm Information Acquisition and Swarm Intelligence in Engineering)

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