Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
3.7
2.3
Journals
Algorithms
Special Issues
Metaheuristic Algorithms in Optimization and Applications (volume 2)
share announcement

Metaheuristic Algorithms in Optimization and Applications (volume 2)

A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Combinatorial Optimization, Graph, and Network Algorithms".

Deadline for manuscript submissions: closed (31 May 2019) | Viewed by 25412

Special Issue Editors

Prof. Dr. Yun-Chia Liang

E-Mail Website1 Website2
Guest Editor
Industrial Engineering and Management Department, Yuan Ze University, Taoyuan City 32003, Taiwan
Interests: combinatorial optimization; meta-heuristic; neural network; production scheduling; supply chain management
Special Issues, Collections and Topics in MDPI journals
Dr. Mehmet Fatih Tasgetiren

E-Mail Website
Guest Editor
Industrial Engineering Department, Yasar University, 35100 Yasar, Turkey
Interests: heuristic optimization; scheduling; real parameter optimization
Special Issues, Collections and Topics in MDPI journals
Dr. Quan-Ke Pan

E-Mail Website
Guest Editor
State Key Lab of Digital Manufacturing Equipment & Technology, Huazhong University of Science & Technology, Wuhan 430074, China
Interests: intelligent optimization theory; algorithms and applications modeling; optimization and scheduling for production manufacturing systems
Special Issues, Collections and Topics in MDPI journals
Dr. Angela Hsiang-Ling Chen

E-Mail Website
Guest Editor
Business Administration Department, Nanya Institute of Technology, Taiwan
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 submissions that pass pre-check are 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 1600 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 (5 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

27 pages, 10891 KiB  
Article
OPTIMUS: Self-Adaptive Differential Evolution with Ensemble of Mutation Strategies for Grasshopper Algorithmic Modeling
by Cemre Cubukcuoglu, Berk Ekici, Mehmet Fatih Tasgetiren and Sevil Sariyildiz
Algorithms 2019, 12(7), 141; https://doi.org/10.3390/a12070141 - 12 Jul 2019
Cited by 30 | Viewed by 6848
Abstract
Most of the architectural design problems are basically real-parameter optimization problems. So, any type of evolutionary and swarm algorithms can be used in this field. However, there is a little attention on using optimization methods within the computer aided design (CAD) programs. In [...] Read more.
Most of the architectural design problems are basically real-parameter optimization problems. So, any type of evolutionary and swarm algorithms can be used in this field. However, there is a little attention on using optimization methods within the computer aided design (CAD) programs. In this paper, we present Optimus, which is a new optimization tool for grasshopper algorithmic modeling in Rhinoceros CAD software. Optimus implements self-adaptive differential evolution algorithm with ensemble of mutation strategies (jEDE). We made an experiment using standard test problems in the literature and some of the test problems proposed in IEEE CEC 2005. We reported minimum, maximum, average, standard deviations and number of function evaluations of five replications for each function. Experimental results on the benchmark suite showed that Optimus (jEDE) outperforms other optimization tools, namely Galapagos (genetic algorithm), SilverEye (particle swarm optimization), and Opossum (RbfOpt) by finding better results for 19 out of 20 problems. For only one function, Galapagos presented slightly better result than Optimus. Ultimately, we presented an architectural design problem and compared the tools for testing Optimus in the design domain. We reported minimum, maximum, average and number of function evaluations of one replication for each tool. Galapagos and Silvereye presented infeasible results, whereas Optimus and Opossum found feasible solutions. However, Optimus discovered a much better fitness result than Opossum. As a conclusion, we discuss advantages and limitations of Optimus in comparison to other tools. The target audience of this paper is frequent users of parametric design modelling e.g., architects, engineers, designers. The main contribution of this paper is summarized as follows. Optimus showed that near-optimal solutions of architectural design problems can be improved by testing different types of algorithms with respect to no-free lunch theorem. Moreover, Optimus facilitates implementing different type of algorithms due to its modular system. Full article
(This article belongs to the Special Issue Metaheuristic Algorithms in Optimization and Applications (volume 2))
►▼ Show Figures

Figure 1

30 pages, 2798 KiB  
Article
A Variable Block Insertion Heuristic for Solving Permutation Flow Shop Scheduling Problem with Makespan Criterion
by Damla Kizilay, Mehmet Fatih Tasgetiren, Quan-Ke Pan and Liang Gao
Algorithms 2019, 12(5), 100; https://doi.org/10.3390/a12050100 - 09 May 2019
Cited by 22 | Viewed by 4732
Abstract
In this paper, we propose a variable block insertion heuristic (VBIH) algorithm to solve the permutation flow shop scheduling problem (PFSP). The VBIH algorithm removes a block of jobs from the current solution. It applies an insertion local search to the partial solution. [...] Read more.
In this paper, we propose a variable block insertion heuristic (VBIH) algorithm to solve the permutation flow shop scheduling problem (PFSP). The VBIH algorithm removes a block of jobs from the current solution. It applies an insertion local search to the partial solution. Then, it inserts the block into all possible positions in the partial solution sequentially. It chooses the best one amongst those solutions from block insertion moves. Finally, again an insertion local search is applied to the complete solution. If the new solution obtained is better than the current solution, it replaces the current solution with the new one. As long as it improves, it retains the same block size. Otherwise, the block size is incremented by one and a simulated annealing-based acceptance criterion is employed to accept the new solution in order to escape from local minima. This process is repeated until the block size reaches its maximum size. To verify the computational results, mixed integer programming (MIP) and constraint programming (CP) models are developed and solved using very recent small VRF benchmark suite. Optimal solutions are found for 108 out of 240 instances. Extensive computational results on the VRF large benchmark suite show that the proposed algorithm outperforms two variants of the iterated greedy algorithm. 236 out of 240 instances of large VRF benchmark suite are further improved for the first time in this paper. Ultimately, we run Taillard’s benchmark suite and compare the algorithms. In addition to the above, three instances of Taillard’s benchmark suite are also further improved for the first time in this paper since 1993. Full article
(This article belongs to the Special Issue Metaheuristic Algorithms in Optimization and Applications (volume 2))
►▼ Show Figures

Figure 1

21 pages, 3929 KiB  
Article
Multi-Metaheuristic Competitive Model for Optimization of Fuzzy Controllers
by Marylu L. Lagunes, Oscar Castillo, Fevrier Valdez and Jose Soria
Algorithms 2019, 12(5), 90; https://doi.org/10.3390/a12050090 - 28 Apr 2019
Cited by 21 | Viewed by 3830
Abstract
This article describes an optimization methodology based on a model of competitiveness between different metaheuristic methods. The main contribution is a strategy to dynamically find the algorithm that obtains the best result based on the competitiveness of methods to solve a specific problem [...] Read more.
This article describes an optimization methodology based on a model of competitiveness between different metaheuristic methods. The main contribution is a strategy to dynamically find the algorithm that obtains the best result based on the competitiveness of methods to solve a specific problem using different performance metrics depending on the problem. The algorithms used in the preliminary tests are: the firefly algorithm (FA), which is inspired by blinking fireflies; wind-driven optimization (WDO), which is inspired by the movement of the wind in the atmosphere, and in which the positions and velocities of the wind packages are updated; and finally, drone squadron optimization (DSO)—the inspiration for this method is new and interesting—based on artifacts, where drones have a command center that sends information to individual drones and updates their software to optimize the objective function. The proposed model helps discover the best method to solve a specific problem, and also reduces the time that it takes to search for methods before finding the one that obtains the most satisfactory results. The main idea is that with this competitiveness approach, methods are tested at the same time until the best one to solve the problem in question is found. As preliminary tests of the model, the optimization of the benchmark mathematical functions and membership functions of a fuzzy controller of an autonomous mobile robot was used. Full article
(This article belongs to the Special Issue Metaheuristic Algorithms in Optimization and Applications (volume 2))
►▼ Show Figures

Figure 1

15 pages, 919 KiB  
Article
A Two-Phase Approach for Single Container Loading with Weakly Heterogeneous Boxes
by Rommel Dias Saraiva, Napoleão Nepomuceno and Plácido Rogério Pinheiro
Algorithms 2019, 12(4), 67; https://doi.org/10.3390/a12040067 - 30 Mar 2019
Cited by 9 | Viewed by 4498
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))
►▼ Show Figures

Figure 1

22 pages, 5009 KiB  
Article
Shadowed Type-2 Fuzzy Systems for Dynamic Parameter Adaptation in Harmony Search and Differential Evolution Algorithms
by Oscar Castillo, Patricia Melin, Fevrier Valdez, Jose Soria, Emanuel Ontiveros-Robles, Cinthia Peraza and Patricia Ochoa
Algorithms 2019, 12(1), 17; https://doi.org/10.3390/a12010017 - 09 Jan 2019
Cited by 38 | Viewed by 4881
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))
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-5
Back to TopTop