Applied and Computational Mathematics

Special Issue

Some Novel Algorithms for Global Optimization and Relevant Subjects

  • Submission Deadline: 1 June 2017
  • Status: Submission Closed
  • Lead Guest Editor: Loc Nguyen
About This Special Issue
We always try our best to create best results but how we can do so? Mathematical optimization is a good answer for above question if our problems can be modeled by mathematical model. The common model is analytic function and it is very easy for us to know that optimization becomes finding out extreme points of such function. The issue focuses on global optimization which means that how to find out the global peak over the whole function. It is very interesting problem because there are two realistic cases as follows:

1. We want to get the best solution and there is no one better than this solution.
2. Given a good solution, we want to get another better solution.

However, global optimization is also complicated because it is relevant to other mathematical subject such as solution existence and approximation. The issue also mentions these subjects. Your attention please, the issue focuses on algorithms and applied methods to solve problem of global optimization. Thus, theoretical aspects relevant to functional analysis are mentioned very little.

Aims and Scope:

1. Global optimization (main subject)
2. Finding out solution of equations (optional)
3. Approximation (optional)
Lead Guest Editor

  • Loc Nguyen

    International Engineering and Technology Institute (IETI), Long Xuyen, Vietnam

Guest Editors

  • Wen Zhang

    Department of Genomics, Icahn School of Medicine at Mount Sinai, New York, United States

  • Aydin Azizi

    Department of engineering, German University of Technology, Muscat, Oman

  • Hamid Yilmaz

    Department of Industrial Engineering, Bayburt University, Bayburt, Turkey

  • Murat Dener

    Department of Computer Engineering, Gazi University, Ankara, Turkey

  • Mustafa Dursun

    Department of Electrical and Electronics Engineering, Duzce University, Duzce, Turkey

  • Guangbao Guo

    Department of Statistics, Shandong University of Technology, Zibo, China

  • Qingyuan Li

    Key Laboratory of Geo-Informatics, Chinese Academy of Surveying and Mapping, Beijing, China

  • Jianqiang Gao

    College of Computer and Information, Hohai University, Nanjing, China

  • Seyed Hamidreza Aghay Kaboli

    Power Energy Dedicated Advanced Centre (UMPEDAC), University Malaya (UM), Kuala Lumpur, Malaysia

  • Ozan Artun

    Department of Physics, Bulent Ecevit University, Zonguldak, Turkey

  • Bin Guo

    College of Computer and Information Engineering, Xinjiang Agricultral University, Urumqi, China

  • Mohd Zuki Salleh

    Program of Science (Mathematics), Faculty of Industrial Science & Technology, Universiti Malaysia Pahang, Kuantan, Malaysia

  • Feng Xue

    National Key Laboratory of Science and Technology on Test Physics and Numerical Mathematics, Beijing, China

  • Kiss Imre ORCiD

    Department of Engineering and Management, Politehnica University of Timișoara, Timisoara, Romania

  • Izzat Qaralleh

    Department of Mathematics, Tafila Technical University, Tafila, Jordan

  • Mohamed Arezki MELLAL

    Faculty of Engineering Sciences (FSI), M'Hamed Bougara University, Boumerdes, Algeria

  • Loc Nguyen

    Sunflower Soft Company, Ho Chi Minh city , Vietnam

  • Partha Sarathi Chakraborty

    Department of Computer Science and Engineering, SRM University (Sri Ramaswamy Memorial University), Modinagar, India

  • Bal Krishna Saraswat

    Department of Computer Science and Engineering, SRM University (Sri Ramaswamy Memorial University), NCR Campus, Ghaziabad, India

  • Harish Garg

    School of Mathematics, Thapar Institute of Engineering and Technology, Patiala, India

  • Mahmoud K. Okasha

    Department of Statistics, Al Azhar University of Gaza, Gaza, Palestine

  • Dr. Deepshikha Bhargava

    Amity Institute of Information Technology, Amity University Rajasthan, Jaipur, India

  • Ayushi Jaiswal

    Department of Electronics & Comm Engg, Jabalpur, India

  • Khalid Aboodh

    Department of Mathematics, Omdurman Islamic University, Khartoum, Sudan

  • Akshara Makrariya

    Department of Applied Mathematics, Sagar Institute of Research and Technology Excellence, Bhopal, India

  • Kirtiwant Ghadle

    Department of Mathematics, Dr. Babasaheb Ambaeedkar Marathwada University, Aurangabad, India

  • Sihem Ben Zakour

    Department of Statistic, Tunis University, Tunis, Tunisia

  • Vijayasekhar Jaliparthi

    Department of Engineering Mathematics, GITAM University, Hyderabad, India

  • Ansari Saleh Ahmar

    Statistics Department, Universitas Negeri Makassar, Makassar, Indonesia

  • Kavitha Rajamani

    Department of Computer Application, Bangalore, India

  • Tanveer Tarray

    Department Of Computer Science and Engineering, Islamic University Of Science and Technology, Srinagar, India

  • Jalal Laassiri

    Informatics Department, Faculty of Sciences, Ibntofail University, Kenitra, Morocco

  • Mahendra Kumar

    Department of Electronics Engineering, Rajasthan Technical University, Kota, India

  • Jalal laassiri

    Informatics Department, Faculty of Sciences, Ibntofail University, Kenitra, Morocco

  • Peter Juma Ochieng

    Department of Computer Science, Bogor Agricultural University, Bogor, Indonesia

  • Deepak Kumar

    Department of Electrical, Gautam Buddha University, grater noida, India

  • Santosh Kumar Suman

    Department of Electrical Engineering, Rajkiya Engineering College, Kannauj, India

  • Dickson Kinyua

    Department of Mathematics, Moi University, Eldoret, Kenya

  • Behnam Razzaghmaneshi

    Department of Mathematics, Islamic Azad University of Talesh, Talesh, Iran

  • Elham Sharifi Rasouli

    Department of Mathematics, Birjand university, Tabriz, Iran

  • Hasan Barzegar

    Department of Mathematics, Tafresh University, Tafresh, Iran

  • Reza Behmanesh

    Department of Industrial Engineering, Science and Research Branch, Islamic Azad University, Isfahan, Iran

Published Articles

  • Global Optimization with Descending Region Algorithm

    Loc Nguyen

    Issue: Volume 6, Issue 4-1, July 2017
    Pages: 72-82
    Received: 8 April 2017
    Accepted: 10 April 2017
    Published: 9 June 2017
    DOI: 10.11648/j.acm.s.2017060401.17
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    Abstract: Global optimization is necessary in some cases when we want to achieve the best solution or we require a new solution which is better the old one. However global optimization is a hazard problem. Gradient descent method is a well-known technique to find out local optimizer whereas approximation solution approach aims to simplify how to solve the gl... Show More
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  • Mobile Online Computer-Adaptive Tests (CAT) for Gathering Patient Feedback in Pediatric Consultations

    Tsair-Wei Chien , Wen-Pin Lai , Ju-Hao Hsieh

    Issue: Volume 6, Issue 4-1, July 2017
    Pages: 64-71
    Received: 19 December 2016
    Accepted: 9 January 2017
    Published: 6 February 2017
    DOI: 10.11648/j.acm.s.2017060401.16
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    Abstract: Background: Few studies have used online patient feedback from smartphones for computer adaptive testing (CAT). Objective: We developed a mobile online CAT survey procedure and evaluated whether it was more precise and efficient than traditional non-adaptive testing (NAT) when gathering patient feedback about their perceptions of interaction with a... Show More
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  • Using Structure Holes for Determining Key Factors: An Illustration of Reporting Eradication of Amoebiasis

    Tsair-Wei Chien , Shih-Bin Su

    Issue: Volume 6, Issue 4-1, July 2017
    Pages: 55-63
    Received: 20 December 2016
    Accepted: 9 January 2017
    Published: 24 January 2017
    DOI: 10.11648/j.acm.s.2017060401.15
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    Abstract: Background: Many researches aim to determine key factors affecting their concerns of interest using traditional statistical techniques, such as logistical or linear regressions. Social network analysis (SNA) is a newly novel way determining key roles through the use of network and graph theories recently. An example of commonly visualized through S... Show More
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  • Comparison of Singular Perturbations Approximation Method and Meta-Heuristic-Based Techniques for Order Reduction of Linear Discrete Systems

    Anouar Bouazza

    Issue: Volume 6, Issue 4-1, July 2017
    Pages: 48-54
    Received: 16 August 2016
    Accepted: 12 September 2016
    Published: 8 December 2016
    DOI: 10.11648/j.acm.s.2017060401.14
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    Abstract: This paper presents a survey of Singular Perturbations Approximation (SPA) method and meta-heuristic techniques for order reduction of linear systems in discrete case. A comparison of intelligent techniques to determine the reduced order model of higher order linear systems is presented. Two approaches are considered: Particle Swarm Optimization (P... Show More
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  • Tutorial on Support Vector Machine

    Loc Nguyen

    Issue: Volume 6, Issue 4-1, July 2017
    Pages: 1-15
    Received: 7 September 2015
    Accepted: 8 September 2015
    Published: 17 June 2016
    DOI: 10.11648/j.acm.s.2017060401.11
    Downloads:
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    Abstract: Support vector machine is a powerful machine learning method in data classification. Using it for applied researches is easy but comprehending it for further development requires a lot of efforts. This report is a tutorial on support vector machine with full of mathematical proofs and example, which help researchers to understand it by the fastest ... Show More
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  • Tutorial on Hidden Markov Model

    Loc Nguyen

    Issue: Volume 6, Issue 4-1, July 2017
    Pages: 16-38
    Received: 11 September 2015
    Accepted: 13 September 2015
    Published: 17 June 2016
    DOI: 10.11648/j.acm.s.2017060401.12
    Downloads:
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    Abstract: Hidden Markov model (HMM) is a powerful mathematical tool for prediction and recognition. Many computer software products implement HMM and hide its complexity, which assist scientists to use HMM for applied researches. However comprehending HMM in order to take advantages of its strong points requires a lot of efforts. This report is a tutorial on... Show More
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  • Longest-path Algorithm to Solve Uncovering Problem of Hidden Markov Model

    Loc Nguyen

    Issue: Volume 6, Issue 4-1, July 2017
    Pages: 39-47
    Received: 12 March 2016
    Accepted: 14 March 2016
    Published: 17 June 2016
    DOI: 10.11648/j.acm.s.2017060401.13
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    Abstract: Uncovering problem is one of three main problems of hidden Markov model (HMM), which aims to find out optimal state sequence that is most likely to produce a given observation sequence. Although Viterbi is the best algorithm to solve uncovering problem, I introduce a new viewpoint of how to solve HMM uncovering problem. The proposed algorithm is ca... Show More
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