Special Issue "Metaheuristic Algorithms in Optimization and Applications (volume 2)"

A special issue of Algorithms (ISSN 1999-4893).

Deadline for manuscript submissions: 31 May 2019

Special Issue Editors

Guest Editor
Dr. Yun-Chia Liang

Industrial Engineering and Management Department, Yuan Ze University, Taiwan
Website | E-Mail
Interests: combinatorial optimization; meta-heuristic; neural network; production scheduling; supply chain management
Guest Editor
Dr. Mehmet Fatih Tasgetiren

Industrial Engineering Department, Yasar University, Turkey
Website | E-Mail
Interests: heuristic optimization; scheduling; real parameter optimization
Guest Editor
Dr. Quan-Ke Pan

State Key Lab of Digital Manufacturing Equipment & Technology, Huazhong University of Science & Technology, China
Website | E-Mail
Interests: intelligent optimization theory, algorithms and applications modeling, optimization and scheduling for production manufacturing systems
Guest Editor
Dr. Angela Hsiang-Ling Chen

Business Administration Department, Nanya Institute of Technology, Taiwan
Website | E-Mail
Interests: project management; logistics and supply chain management; decision analysis

Special Issue Information

Dear Colleagues,

Metaheuristic algorithms have attracted a great deal of attention in artificial intelligence, engineering design, data mining, planning and scheduling, logistics and supply chains, etc. This Special Issue focuses on the recent developments of metaheuristic algorithms and their diverse applications, as well as theoretical studies. Both combinatorial and continuous optimization problems are welcome.

We invite authors to contribute original research articles as well as review articles on recent advances in these active research areas. Topics of interest include, but are not limited to:

  • Swarm intelligence such as Artificial Bee Colony, Ant Colony Optimization, Particle Swarm Optimization, and Virus Optimization Algorithm, etc.
  • Nature-inspired metaheuristic algorithms such as Evolutionary Algorithm, Genetic Algorithm, etc.
  • Neighborhood search algorithms such as Iterated Local Search, Simulated Annealing, Tabu Search, Variable Neighborhood Search, etc.
  • New metaheuristic frameworks/approaches/operators
  • Parallelization of metaheuristics
  • Hybridized algorithms
  • Empirical and theoretical research of metaheuristics
  • High-impact applications of metaheuristics
  • Challenging problems such as multi-objective, stochastic, or dynamic problems
  • Automatic configuration of metaheuristics

Dr. Yun-Chia Liang
Dr. Mehmet Fatih Tasgetiren
Dr. Quan-Ke Pan
Dr. Angela Hsiang-Ling Chen
Guest Editors

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. Algorithms is an international peer-reviewed open access monthly 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 1000 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

  • Meta-heuristics
  • Optimization
  • Evolutionary Algorithm
  • Swarm Intelligence

Published Papers (2 papers)

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Research

Open AccessArticle A Two-Phase Approach for Single Container Loading with Weakly Heterogeneous Boxes
Algorithms 2019, 12(4), 67; https://doi.org/10.3390/a12040067
Received: 30 January 2019 / Revised: 21 March 2019 / Accepted: 25 March 2019 / Published: 30 March 2019
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Abstract
We propose in this paper a two-phase approach that decomposes the process of solving the three-dimensional single Container Loading Problem (CLP) into subsequent tasks: (i) the generation of blocks of boxes and (ii) the loading of blocks into the container. The first phase [...] Read more.
We propose in this paper a two-phase approach that decomposes the process of solving the three-dimensional single Container Loading Problem (CLP) into subsequent tasks: (i) the generation of blocks of boxes and (ii) the loading of blocks into the container. The first phase is deterministic, and it is performed by means of constructive algorithms from the literature. The second phase is non-deterministic, and it is performed with the use of Generate-and-Solve (GS), a problem-independent hybrid optimization framework based on problem instance reduction that combines a metaheuristic with an exact solver. Computational experiments performed on benchmark instances indicate that our approach presents competitive results compared to those found by state-of-the-art algorithms, particularly for problem instances consisting of a few types of boxes. In fact, we present new best solutions for classical instances from groups BR1 and BR2. Full article
(This article belongs to the Special Issue Metaheuristic Algorithms in Optimization and Applications (volume 2))
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Open AccessArticle Shadowed Type-2 Fuzzy Systems for Dynamic Parameter Adaptation in Harmony Search and Differential Evolution Algorithms
Algorithms 2019, 12(1), 17; https://doi.org/10.3390/a12010017
Received: 22 November 2018 / Revised: 11 December 2018 / Accepted: 3 January 2019 / Published: 9 January 2019
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Abstract
Nowadays, dynamic parameter adaptation has been shown to provide a significant improvement in several metaheuristic optimization methods, and one of the main ways to realize this dynamic adaptation is the implementation of Fuzzy Inference Systems. The main reason for this is because Fuzzy [...] Read more.
Nowadays, dynamic parameter adaptation has been shown to provide a significant improvement in several metaheuristic optimization methods, and one of the main ways to realize this dynamic adaptation is the implementation of Fuzzy Inference Systems. The main reason for this is because Fuzzy Inference Systems can be designed based on human knowledge, and this can provide an intelligent dynamic adaptation of parameters in metaheuristics. In addition, with the coming forth of Type-2 Fuzzy Logic, the capability of uncertainty handling offers an attractive improvement for dynamic parameter adaptation in metaheuristic methods, and, in fact, the use of Interval Type-2 Fuzzy Inference Systems (IT2 FIS) has been shown to provide better results with respect to Type-1 Fuzzy Inference Systems (T1 FIS) in recent works. Based on the performance improvement exhibited by IT2 FIS, the present paper aims to implement the Shadowed Type-2 Fuzzy Inference System (ST2 FIS) for further improvements in dynamic parameter adaptation in Harmony Search and Differential Evolution optimization methods. The ST2 FIS is an approximation of General Type-2 Fuzzy Inference Systems (GT2 FIS), and is based on the principles of Shadowed Fuzzy Sets. The main reason for using ST2 FIS and not GT2 FIS is because the computational cost of GT2 FIS represents a time limitation in this application. The paper presents a comparison of the conventional methods with static parameters and the dynamic parameter adaptation based on ST2 FIS, and the approaches are compared in solving mathematical functions and in controller optimization. Full article
(This article belongs to the Special Issue Metaheuristic Algorithms in Optimization and Applications (volume 2))
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