Special Issue "Metaheuristic Algorithms in Optimization and Applications 2021"

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 October 2021).

Special Issue Editors

Dr. Yun-Chia Liang
E-Mail Website
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. Hsiang-Ling Chen
E-Mail Website
Guest Editor
Department of Industrial and Systems Engineering, Chung Yuan Christian University, Taoyuan City 320, 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 Tasgetiren
Dr. Quan-Ke Pan
Dr. 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 1400 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 (7 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

Article
Travel Time Reliability-Based Rescue Resource Scheduling for Accidents Concerning Transport of Dangerous Goods by Rail
Algorithms 2021, 14(11), 325; https://0-doi-org.brum.beds.ac.uk/10.3390/a14110325 - 05 Nov 2021
Viewed by 332
Abstract
The characteristics of railway dangerous goods accidents are very complex. The rescue of railway dangerous goods accidents should consider the timeliness of rescue, the uncertainty of traffic environment and the diversity of rescue resources. Thus, the purpose of this paper is to confront [...] Read more.
The characteristics of railway dangerous goods accidents are very complex. The rescue of railway dangerous goods accidents should consider the timeliness of rescue, the uncertainty of traffic environment and the diversity of rescue resources. Thus, the purpose of this paper is to confront the rescue resources scheduling problem of railway dangerous goods accident by considering factors such as rescue capacity, rescue demand and response time. Based on the analysis of travel time and reliability for rescue route, a multi-objective scheduling model of rescue resources based on travel time reliability is constructed in order to minimize the total arrival time of rescue resources and to maximize total reliability. The proposed model is more reliable than the traditional model due to the consideration of travel time reliability of rescue routes. Moreover, a two-stage algorithm is designed to solve this problem. A multi-path algorithm with bound constraints is used to obtain the set of feasible rescue routes in the first stage, and the NSGA-II algorithm is used to determine the scheduling of rescue resources for each rescue center. Finally, the two-stage algorithm is tested on a regional road network, and the results show that the designed two-stage algorithm is valid for solving the rescue resource scheduling problem of dangerous goods accidents and is able to obtain the rescue resource scheduling scheme in a short period of time. Full article
(This article belongs to the Special Issue Metaheuristic Algorithms in Optimization and Applications 2021)
Show Figures

Figure 1

Article
Feature Selection for High-Dimensional Datasets through a Novel Artificial Bee Colony Framework
Algorithms 2021, 14(11), 324; https://0-doi-org.brum.beds.ac.uk/10.3390/a14110324 - 04 Nov 2021
Viewed by 304
Abstract
There are generally many redundant and irrelevant features in high-dimensional datasets, which leads to the decline of classification performance and the extension of execution time. To tackle this problem, feature selection techniques are used to screen out redundant and irrelevant features. The artificial [...] Read more.
There are generally many redundant and irrelevant features in high-dimensional datasets, which leads to the decline of classification performance and the extension of execution time. To tackle this problem, feature selection techniques are used to screen out redundant and irrelevant features. The artificial bee colony (ABC) algorithm is a popular meta-heuristic algorithm with high exploration and low exploitation capacities. To balance between both capacities of the ABC algorithm, a novel ABC framework is proposed in this paper. Specifically, the solutions are first updated by the process of employing bees to retain the original exploration ability, so that the algorithm can explore the solution space extensively. Then, the solutions are modified by the updating mechanism of an algorithm with strong exploitation ability in the onlooker bee phase. Finally, we remove the scout bee phase from the framework, which can not only reduce the exploration ability but also speed up the algorithm. In order to verify our idea, the operators of the grey wolf optimization (GWO) algorithm and whale optimization algorithm (WOA) are introduced into the framework to enhance the exploitation capability of onlooker bees, named BABCGWO and BABCWOA, respectively. It has been found that these two algorithms are superior to four state-of-the-art feature selection algorithms using 12 high-dimensional datasets, in terms of the classification error rate, size of feature subset and execution speed. Full article
(This article belongs to the Special Issue Metaheuristic Algorithms in Optimization and Applications 2021)
Show Figures

Figure 1

Article
Metaheuristics for a Flow Shop Scheduling Problem with Urgent Jobs and Limited Waiting Times
Algorithms 2021, 14(11), 323; https://0-doi-org.brum.beds.ac.uk/10.3390/a14110323 - 03 Nov 2021
Viewed by 312
Abstract
This study considers a scheduling problem for a flow shop with urgent jobs and limited waiting times. The urgent jobs and limited waiting times are major considerations for scheduling in semiconductor manufacturing systems. The objective function is to minimize a weighted sum of [...] Read more.
This study considers a scheduling problem for a flow shop with urgent jobs and limited waiting times. The urgent jobs and limited waiting times are major considerations for scheduling in semiconductor manufacturing systems. The objective function is to minimize a weighted sum of total tardiness of urgent jobs and the makespan of normal jobs. This problem is formulated in mixed integer programming (MIP). By using a commercial optimization solver, the MIP can be used to find an optimal solution. However, because this problem is proved to be NP-hard, solving to optimality requires a significantly long computation time for a practical size problem. Therefore, this study adopts metaheuristic algorithms to obtain a good solution quickly. To complete this, two metaheuristic algorithms (an iterated greedy algorithm and a simulated annealing algorithm) are proposed, and a series of computational experiments were performed to examine the effectiveness and efficiency of the proposed algorithms. Full article
(This article belongs to the Special Issue Metaheuristic Algorithms in Optimization and Applications 2021)
Show Figures

Figure 1

Article
Metaheuristics for the Minimum Time Cut Path Problem with Different Cutting and Sliding Speeds
Algorithms 2021, 14(11), 305; https://0-doi-org.brum.beds.ac.uk/10.3390/a14110305 - 23 Oct 2021
Viewed by 384
Abstract
The problem of efficiently cutting smaller two-dimensional pieces from a larger surface is recurrent in several manufacturing settings. This problem belongs to the domain of cutting and packing (C&P) problems. This study approached a category of C&P problems called the minimum time cut [...] Read more.
The problem of efficiently cutting smaller two-dimensional pieces from a larger surface is recurrent in several manufacturing settings. This problem belongs to the domain of cutting and packing (C&P) problems. This study approached a category of C&P problems called the minimum time cut path (MTCP) problem, which aims to identify a sequence of cutting and sliding movements for the head device to minimize manufacturing time. Both cutting and slide speeds (just moving the head) vary according to equipment, despite their relevance in real-world scenarios. This study applied the MTCP problem on the practical scope and presents two metaheuristics for tackling more significant instances that resemble real-world requirements. The experiments presented in this study utilized parameter values from typical laser cutting machines to assess the feasibility of the proposed methods compared to existing commercial software. The results show that metaheuristic-based solutions are competitive when addressing practical problems, achieving increased performance regarding the processing time for 94% of the instances. Full article
(This article belongs to the Special Issue Metaheuristic Algorithms in Optimization and Applications 2021)
Show Figures

Figure 1

Article
Enhanced Hyper-Cube Framework Ant Colony Optimization for Combinatorial Optimization Problems
Algorithms 2021, 14(10), 286; https://0-doi-org.brum.beds.ac.uk/10.3390/a14100286 - 29 Sep 2021
Viewed by 380
Abstract
Solving of combinatorial optimization problems is a common practice in real-life engineering applications. Trusses, cranes, and composite laminated structures are some good examples that fall under this category of optimization problems. Those examples have a common feature of discrete design domain that turn [...] Read more.
Solving of combinatorial optimization problems is a common practice in real-life engineering applications. Trusses, cranes, and composite laminated structures are some good examples that fall under this category of optimization problems. Those examples have a common feature of discrete design domain that turn them into a set of NP-hard optimization problems. Determining the right optimization algorithm for such problems is a precious point that tends to impact the overall cost of the design process. Furthermore, reinforcing the performance of a prospective optimization algorithm reduces the design cost. In the current study, a comprehensive assessment criterion has been developed to assess the performance of meta-heuristic (MH) solutions in the domain of structural design. Thereafter, the proposed criterion was employed to compare five different variants of Ant Colony Optimization (ACO). It was done by using a well-known structural optimization problem of laminate Stacking Sequence Design (SSD). The initial results of the comparison study reveal that the Hyper-Cube Framework (HCF) ACO variant outperforms the others. Consequently, an investigation of further improvement led to introducing an enhanced version of HCFACO (or EHCFACO). Eventually, the performance assessment of the EHCFACO variant showed that the average practical reliability became more than twice that of the standard ACO, and the normalized price decreased more to hold at 28.92 instead of 51.17. Full article
(This article belongs to the Special Issue Metaheuristic Algorithms in Optimization and Applications 2021)
Show Figures

Figure 1

Article
Algorithms for Bidding Strategies in Local Energy Markets: Exhaustive Search through Parallel Computing and Metaheuristic Optimization
Algorithms 2021, 14(9), 269; https://0-doi-org.brum.beds.ac.uk/10.3390/a14090269 - 16 Sep 2021
Viewed by 565
Abstract
The integration of different energy resources from traditional power systems presents new challenges for real-time implementation and operation. In the last decade, a way has been sought to optimize the operation of small microgrids (SMGs) that have a great variety of energy sources [...] Read more.
The integration of different energy resources from traditional power systems presents new challenges for real-time implementation and operation. In the last decade, a way has been sought to optimize the operation of small microgrids (SMGs) that have a great variety of energy sources (PV (photovoltaic) prosumers, Genset CHP (combined heat and power), etc.) with uncertainty in energy production that results in different market prices. For this reason, metaheuristic methods have been used to optimize the decision-making process for multiple players in local and external markets. Players in this network include nine agents: three consumers, three prosumers (consumers with PV capabilities), and three CHP generators. This article deploys metaheuristic algorithms with the objective of maximizing power market transactions and clearing price. Since metaheuristic optimization algorithms do not guarantee global optima, an exhaustive search is deployed to find global optima points. The exhaustive search algorithm is implemented using a parallel computing architecture to reach feasible results in a short amount of time. The global optimal result is used as an indicator to evaluate the performance of the different metaheuristic algorithms. The paper presents results, discussion, comparison, and recommendations regarding the proposed set of algorithms and performance tests. Full article
(This article belongs to the Special Issue Metaheuristic Algorithms in Optimization and Applications 2021)
Show Figures

Figure 1

Article
Allocating Students to Industry Placements Using Integer Programming and Ant Colony Optimisation
Algorithms 2021, 14(8), 219; https://0-doi-org.brum.beds.ac.uk/10.3390/a14080219 - 21 Jul 2021
Viewed by 805
Abstract
The increasing demand for work-ready students has heightened the need for universities to provide work integrated learning programs to enhance and reinforce students’ learning experiences. Students benefit most when placements meet their academic requirements and graduate aspirations. Businesses and community partners are more [...] Read more.
The increasing demand for work-ready students has heightened the need for universities to provide work integrated learning programs to enhance and reinforce students’ learning experiences. Students benefit most when placements meet their academic requirements and graduate aspirations. Businesses and community partners are more engaged when they are allocated students that meet their industry requirements. In this paper, both an integer programming model and an ant colony optimisation heuristic are proposed, with the aim of automating the allocation of students to industry placements. The emphasis is on maximising student engagement and industry partner satisfaction. As part of the objectives, these methods incorporate diversity in industry sectors for students undertaking multiple placements, gender equity across placement providers, and the provision for partners to rank student selections. The experimental analysis is in two parts: (a) we investigate how the integer programming model performs against manual allocations and (b) the scalability of the IP model is examined. The results show that the IP model easily outperforms the previous manual allocations. Additionally, an artificial dataset is generated which has similar properties to the original data but also includes greater numbers of students and placements to test the scalability of the algorithms. The results show that integer programming is the best option for problem instances consisting of less than 3000 students. When the problem becomes larger, significantly increasing the time required for an IP solution, ant colony optimisation provides a useful alternative as it is always able to find good feasible solutions within short time-frames. Full article
(This article belongs to the Special Issue Metaheuristic Algorithms in Optimization and Applications 2021)
Show Figures

Figure 1

Back to TopTop