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Symmetry
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Meta-Heuristics for Manufacturing Systems Optimization
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Meta-Heuristics for Manufacturing Systems Optimization

A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Engineering and Materials".

Deadline for manuscript submissions: closed (31 May 2022) | Viewed by 34099

Special Issue Editors

Prof. Dr. Deming Lei

E-Mail Website
Guest Editor
School of Automation, Wuhan University of Technology, Wuhan 430062, China
Interests: manufacturing system optimization and scheduling; vehicle routing problem; multi-objective optimization; intelligent optimization; intelligent control
Special Issues, Collections and Topics in MDPI journals
Dr. Ming Li

E-Mail
Guest Editor
School of Economics and Management, Anhui Polytechnic University, Wuhu, China
Interests: manufacturing system optimization and scheduling; multi-objective optimization; intelligent optimization

Special Issue Information

Dear Colleagues,

Meta-heuristics is a kind of effective tool inspired from the phenomena and behavior of nature and society. There are many meta-heuristics, including genetic algorithms, particle swarm optimization, ant colony optimization, artificial bee colony, estimation of distribution algorithms, differential evolution, shuffled frog-leaping algorithms, teaching-learning-based optimization, imperialist competitive algorithms, etc. The manufacturing industry is an important part of the economy in a number of countries, such as China. Many complicated optimization problems including scheduling and routing extensively exist in manufacturing systems. They may have symmetrical features or constraints, and some of them possess asymmetrical conditions which are difficult to tackle using traditional optimization methods. In the last decade, meta-heuristics have become the main path to solve manufacturing system optimization problems, and many results have been obtained.  

This Special Issue invites contributions addressing novel theories, techniques, and applications for meta-heuristic-based manufacturing system optimization. We intend to garner articles in a variety of topics, such as meta-heuristics for multi-objective optimization, meta-heuristics for constrained optimization, multi-objective production scheduling, production scheduling with uncertainty, energy-efficient scheduling, distributed scheduling, dynamic scheduling, etc. Extensive review papers on the latest research findings are also welcome.

Potential topics include but are not limited to:

  • Meta-heuristics for multi-objective optimization;
  • Meta-heuristics for constrained optimization;
  • Multi-objective production scheduling;
  • Production scheduling with uncertainty;
  • Energy-efficient scheduling;
  • Distributed scheduling;
  • Dynamic scheduling;
  • Assembly line balancing;
  • Vehicle routing problem;
  • Optimization problems in semiconductors, iron, automobile, chemical industry, etc.

Due to the great success of our Special Issue "Meta-Heuristics for Manufacturing Systems Optimization", we decided to set up a second volume. We invite you to contribute to the Special Issue "Symmetry: Meta-Heuristics for Manufacturing Systems Optimization Ⅱ" by https://0-www-mdpi-com.brum.beds.ac.uk/journal/symmetry/special_issues/KJ534I9274.

Prof. Dr. Deming Lei
Dr. Ming Li
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. Symmetry 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 2400 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-heuristic
  • Production scheduling
  • Optimization
  • Manufacturing systems

Published Papers (18 papers)

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Research

23 pages, 642 KiB  
Article
Distributed Energy-Efficient Assembly Scheduling Problem with Transportation Capacity
by Deming Lei and Jinlin Li
Symmetry 2022, 14(11), 2225; https://0-doi-org.brum.beds.ac.uk/10.3390/sym14112225 - 22 Oct 2022
Cited by 1 | Viewed by 993
Abstract
The real-life assembly production often has transportation between fabrication and assembly, and the capacity of transportation machine is often considered; however, the previous works are mainly about two-stage distributed assembly scheduling problems. In this study, a distributed energy-efficient assembly scheduling problem (DEASP) with [...] Read more.
The real-life assembly production often has transportation between fabrication and assembly, and the capacity of transportation machine is often considered; however, the previous works are mainly about two-stage distributed assembly scheduling problems. In this study, a distributed energy-efficient assembly scheduling problem (DEASP) with transportation capacity is investigated, in which dedicated parallel machines with symmetry under the given conditions, transportation machines and an assembly machine are used. An adaptive imperialist competitive algorithm (AICA) is proposed to minimize makespan and total energy consumption. A heuristic and an energy-saving rule are used to produce initial solutions. An adaptive assimilation with adaptive global search and an adaptive revolution are implemented, in which neighborhood structures are chosen dynamically, and revolution probability and search times are decided by using the solution quality. The features of the problem are also used effectively. Computational experiments are conducted on a number of instances. The computational results demonstrate that the new strategies of AICA are effective and efficient and AICA can provide promising results for the considered DEASP. Full article
(This article belongs to the Special Issue Meta-Heuristics for Manufacturing Systems Optimization)
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18 pages, 4789 KiB  
Article
Using Adaptive Directed Acyclic Graph for Human In-Hand Motion Identification with Hybrid Surface Electromyography and Kinect
by Yaxu Xue, Yadong Yu, Kaiyang Yin, Haojie Du, Pengfei Li, Kejie Dai and Zhaojie Ju
Symmetry 2022, 14(10), 2093; https://0-doi-org.brum.beds.ac.uk/10.3390/sym14102093 - 08 Oct 2022
Cited by 1 | Viewed by 1039
Abstract
The multi-fingered dexterous robotic hand is increasingly used to achieve more complex and sophisticated human-like manipulation tasks on various occasions. This paper proposes a hybrid Surface Electromyography (SEMG) and Kinect-based human in-hand motion (HIM) capture system architecture for recognizing complex motions of the [...] Read more.
The multi-fingered dexterous robotic hand is increasingly used to achieve more complex and sophisticated human-like manipulation tasks on various occasions. This paper proposes a hybrid Surface Electromyography (SEMG) and Kinect-based human in-hand motion (HIM) capture system architecture for recognizing complex motions of the humans by observing the state information between an object and the human hand, then transferring the manipulation skills into bionic multi-fingered robotic hand realizing dexterous in-hand manipulation. First, an Adaptive Directed Acyclic Graph (ADAG) algorithm for recognizing HIMs is proposed and optimized based on the comparison of multi-class support vector machines; second, ten representative complex in-hand motions are demonstrated by ten subjects, and SEMG and Kinect signals are obtained based on a multi-modal data acquisition platform; then, combined with the proposed algorithm framework, a series of data preprocessing algorithms are realized. There is statistical symmetry in similar types of SEMG signals and images, and asymmetry in different types of SEMG signals and images. A detailed analysis and an in-depth discussion are given from the results of the ADAG recognizing HIMs, motion recognition rates of different perceptrons, motion recognition rates of different subjects, motion recognition rates of different multi-class SVM methods, and motion recognition rates of different machine learning methods. The results of this experiment confirm the feasibility of the proposed method, with a recognition rate of 95.10%. Full article
(This article belongs to the Special Issue Meta-Heuristics for Manufacturing Systems Optimization)
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21 pages, 340 KiB  
Article
An Artificial Bee Colony with Adaptive Competition for the Unrelated Parallel Machine Scheduling Problem with Additional Resources and Maintenance
by Mingbo Li, Huan Xiong and Deming Lei
Symmetry 2022, 14(7), 1380; https://0-doi-org.brum.beds.ac.uk/10.3390/sym14071380 - 05 Jul 2022
Cited by 7 | Viewed by 1304
Abstract
The unrelated parallel machine scheduling problem (UPMSP) is a typical production scheduling problem with certain symmetries on machines. Additional resources and preventive maintenance (PM) extensively exist on parallel machines; however, UPMSP with additional resources and PM has been scarcely investigated. Adaptive competition is [...] Read more.
The unrelated parallel machine scheduling problem (UPMSP) is a typical production scheduling problem with certain symmetries on machines. Additional resources and preventive maintenance (PM) extensively exist on parallel machines; however, UPMSP with additional resources and PM has been scarcely investigated. Adaptive competition is also seldom implemented in the artificial bee colony algorithm for production scheduling. In this study, UPMSP with additional resources and PM is investigated, which has certain symmetries with machines. An artificial bee colony with adaptive competition (ABC-AC) is proposed to minimize the makespan. Two employed bee swarms are constructed and evaluated. In the employed bee phase, adaptive competition is used to dynamically decide two cases. The first is the shifting of search resources from the employed bee swarm with a lower evolution quality to another one, and the second is the migration of solutions from the employed bee swarm with a higher evolution quality to another one. An adaptive onlooker bee phase and a new scout phase are given. Extensive experiments are conducted on 300 instances. The computational results demonstrate that the new strategies of ABC-AC are effective, and ABC-AC provides promising results for the considered UPMSP. Full article
(This article belongs to the Special Issue Meta-Heuristics for Manufacturing Systems Optimization)
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19 pages, 4136 KiB  
Article
A Hybrid Framework Model Based on Wavelet Neural Network with Improved Fruit Fly Optimization Algorithm for Traffic Flow Prediction
by Qingyong Zhang, Changwu Li, Conghui Yin, Hang Zhang and Fuwen Su
Symmetry 2022, 14(7), 1333; https://0-doi-org.brum.beds.ac.uk/10.3390/sym14071333 - 28 Jun 2022
Cited by 9 | Viewed by 1325
Abstract
Accurate traffic flow prediction can provide sufficient information for the formation of symmetric traffic flow. To overcome the problem that the basic fruit fly optimization algorithm (FOA) is easy to fall into local optimum and the search method is single, an improved fruit [...] Read more.
Accurate traffic flow prediction can provide sufficient information for the formation of symmetric traffic flow. To overcome the problem that the basic fruit fly optimization algorithm (FOA) is easy to fall into local optimum and the search method is single, an improved fruit fly optimization algorithm (IFOA) based on parallel search strategy and group cooperation strategy is proposed. The multi-swarm mechanism is introduced in the parallel search strategy, in which each subswarm is independent and multiple center positions are determined in the iterative process, thereby avoiding the problems of reduced diversity and premature convergence. To increase communication between fruit fly subswarms, the informative fruit flies selected from subswarms are guided by the randomly generated binary fruit fly to achieve the crossover operation in the group cooperation strategy. Then a hybrid framework model based on wavelet neural network (WNN) with IFOA (IFOA-WNN) for traffic flow prediction is designed, in which IFOA is applied to explore appropriate structure parameters for WNN to achieve better prediction performance. The simulation results verify that the IFOA can provide high-quality structural parameters for WNN, and the hybrid IFOA-WNN prediction model can achieve higher prediction accuracy and stability than the compared methods. Full article
(This article belongs to the Special Issue Meta-Heuristics for Manufacturing Systems Optimization)
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14 pages, 2232 KiB  
Article
3D Localization Method of Partial Discharge in Air-Insulated Substation Based on Improved Particle Swarm Optimization Algorithm
by Pengfei Li, Xinjie Peng, Kaiyang Yin, Yaxu Xue, Rongqing Wang and Zhengsen Ma
Symmetry 2022, 14(6), 1241; https://0-doi-org.brum.beds.ac.uk/10.3390/sym14061241 - 15 Jun 2022
Cited by 5 | Viewed by 1315
Abstract
Partial discharge (PD) localization in an air-insulated substation (AIS) can be used to assess insulation conditions efficiently. Many localization methods have been reported during the past few years. However, the error of the localization results has been large or the localization algorithm too [...] Read more.
Partial discharge (PD) localization in an air-insulated substation (AIS) can be used to assess insulation conditions efficiently. Many localization methods have been reported during the past few years. However, the error of the localization results has been large or the localization algorithm too inefficient. The reason is that the localization equation set is nonlinear and non-symmetrical. In this paper, an improved particle swarm optimization (PSO) algorithm is proposed to improve the localization accuracy in 3D. Firstly, the proposed localization method is based on the symmetrical antenna array and the location error distribution is analyzed. Secondly, the objective function of PSO is constructed using the error distribution. Specifically, the 3D location target is divided into two steps—plane coordinates and height. The two targets are optimized respectively. To verify the method, a test is carried out by a prefabricated fault bushing in the laboratory to compare with the existing methods. According to the results, the localization error is 0.21 m, which can locate the PD source accurately. A complete calculation takes 42.29 s, and the efficiency is increased by 16.13 times under the same accuracy. The comparison results show that the proposed method can greatly improve the efficiency while ensuring accuracy. Full article
(This article belongs to the Special Issue Meta-Heuristics for Manufacturing Systems Optimization)
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15 pages, 9768 KiB  
Article
Quasi-Reflective Chaotic Mutant Whale Swarm Optimization Fused with Operators of Fish Aggregating Device
by Shoubao Su, Chao He and Liukai Xu
Symmetry 2022, 14(4), 829; https://0-doi-org.brum.beds.ac.uk/10.3390/sym14040829 - 15 Apr 2022
Viewed by 2121
Abstract
To improve the performance of the whale optimization algorithm and further enhance the search accuracy, while increasing the convergence speed, a quasi-reflective chaotic mutant whale swarm optimization, namely QNWOA, is proposed, fused with an operator of Fish Aggregating Devices (FADs) in this paper. [...] Read more.
To improve the performance of the whale optimization algorithm and further enhance the search accuracy, while increasing the convergence speed, a quasi-reflective chaotic mutant whale swarm optimization, namely QNWOA, is proposed, fused with an operator of Fish Aggregating Devices (FADs) in this paper. Firstly, the swarm diversity is increased by using logistic chaotic mapping. Secondly, a quasi-reflective learning mechanism is introduced to improve the convergence speed of the algorithm. Then, the FADs vortex effect and wavelet variation of the marine predator algorithm (MPA) are introduced in the search phase to enhance the stability of the algorithm in the early and late stages and the ability to escape from the local optimum by broking the symmetry of iterative routes. Finally, a combination of linearly decreasing and nonlinear segmentation convergence factors is proposed to balance the local and global search capabilities of the algorithm. Nine benchmark functions are selected for the simulation, and after comparing with other algorithms, the results show that the convergence speed and solution accuracy of the proposed algorithm are promising in this study. Full article
(This article belongs to the Special Issue Meta-Heuristics for Manufacturing Systems Optimization)
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21 pages, 1178 KiB  
Article
Automatic Design of Efficient Heuristics for Two-Stage Hybrid Flow Shop Scheduling
by Lingxuan Liu and Leyuan Shi
Symmetry 2022, 14(4), 632; https://0-doi-org.brum.beds.ac.uk/10.3390/sym14040632 - 22 Mar 2022
Cited by 6 | Viewed by 1429
Abstract
This paper addresses the two-stage hybrid flow shop scheduling problem with a batch processor in the first stage and a discrete processor in the second stage. Incompatible job families and limited buffer size are considered. This hybrid flow shop configuration commonly appears in [...] Read more.
This paper addresses the two-stage hybrid flow shop scheduling problem with a batch processor in the first stage and a discrete processor in the second stage. Incompatible job families and limited buffer size are considered. This hybrid flow shop configuration commonly appears in manufacturing operations and the batch processor is always the bottleneck which breaks the symmetry of processing time. Since making a real-time high-quality schedule is challenging, we focus on the automatic design of efficient heuristics for this two-stage problem based on the genetic programming method. We develop a hyper-heuristic approach to automate the tedious trial-and-error design process of heuristics. The goal is to generate efficient dispatching rules for identifying complete schedules to minimize the total completion time. A genetic programming with cooperative co-evolution approach is proposed to evolve the schedule policy automatically. Numerical results demonstrate that the proposed approach outperforms both the constructive heuristic and meta-heuristic algorithms, and is capable of producing high-quality schedules within seconds. Full article
(This article belongs to the Special Issue Meta-Heuristics for Manufacturing Systems Optimization)
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14 pages, 3382 KiB  
Article
An Evolutionary Numerical Method of Supply Chain Trust Networks with the Degree of Distribution
by Xuelong Zhang, Maojun Zhang, Yuxi Luo and Yanling Yi
Symmetry 2022, 14(3), 587; https://0-doi-org.brum.beds.ac.uk/10.3390/sym14030587 - 16 Mar 2022
Cited by 2 | Viewed by 1549
Abstract
We study the structure of supply chain trust networks (SCTNs) by analyzing the evolution of the networks. An SCTN here comprises enterprises in a fully competitive market connected through the preferential attachment mechanism. A Markov chain analysis is used to understand how various [...] Read more.
We study the structure of supply chain trust networks (SCTNs) by analyzing the evolution of the networks. An SCTN here comprises enterprises in a fully competitive market connected through the preferential attachment mechanism. A Markov chain analysis is used to understand how various factors affect the structure of the SCTNs. The evolution of the SCTNs is also analyzed to identify the asymmetric conditions required for the degree distribution of the SCTNs to obey the power law distribution. The simulation results show that, when the degree of willingness to initiate a trust relationship and the attractiveness index of the supply chain networks meet certain criteria, the underlying network is of a scale-free nature. Full article
(This article belongs to the Special Issue Meta-Heuristics for Manufacturing Systems Optimization)
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18 pages, 742 KiB  
Article
A Hybrid Imperialist Competitive Algorithm for the Distributed Unrelated Parallel Machines Scheduling Problem
by Youlian Zheng, Yue Yuan, Qiaoxian Zheng and Deming Lei
Symmetry 2022, 14(2), 204; https://0-doi-org.brum.beds.ac.uk/10.3390/sym14020204 - 21 Jan 2022
Cited by 6 | Viewed by 1988
Abstract
In this paper, the distributed unrelated parallel machines scheduling problem (DUPMSP) is studied and a hybrid imperialist competitive algorithm (HICA) is proposed to minimize total tardiness. All empires were categorized into three types: the strongest empire, the weakest empire, and other empires; the [...] Read more.
In this paper, the distributed unrelated parallel machines scheduling problem (DUPMSP) is studied and a hybrid imperialist competitive algorithm (HICA) is proposed to minimize total tardiness. All empires were categorized into three types: the strongest empire, the weakest empire, and other empires; the diversified assimilation was implemented by using different search operator in the different types of empires, and a novel imperialist competition was implemented among all empires except the strongest one. The knowledge-based local search was embedded. Extensive experiments were conducted to compare the HICA with other algorithms from the literature. The computational results demonstrated that new strategies were effective and the HICA is a promising approach to solving the DUPMSP. Full article
(This article belongs to the Special Issue Meta-Heuristics for Manufacturing Systems Optimization)
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23 pages, 32906 KiB  
Article
A Modification of the Imperialist Competitive Algorithm with Hybrid Methods for Multi-Objective Optimization Problems
by Jianfu Luo, Jinsheng Zhou, Xi Jiang and Haodong Lv
Symmetry 2022, 14(1), 173; https://0-doi-org.brum.beds.ac.uk/10.3390/sym14010173 - 16 Jan 2022
Cited by 2 | Viewed by 1572
Abstract
This paper proposes a modification of the imperialist competitive algorithm to solve multi-objective optimization problems with hybrid methods (MOHMICA) based on a modification of the imperialist competitive algorithm with hybrid methods (HMICA). The rationale for this is that there is an obvious disadvantage [...] Read more.
This paper proposes a modification of the imperialist competitive algorithm to solve multi-objective optimization problems with hybrid methods (MOHMICA) based on a modification of the imperialist competitive algorithm with hybrid methods (HMICA). The rationale for this is that there is an obvious disadvantage of HMICA in that it can only solve single-objective optimization problems but cannot solve multi-objective optimization problems. In order to adapt to the characteristics of multi-objective optimization problems, this paper improves the establishment of the initial empires and colony allocation mechanism and empire competition in HMICA, and introduces an external archiving strategy. A total of 12 benchmark functions are calculated, including 10 bi-objective and 2 tri-objective benchmarks. Four metrics are used to verify the quality of MOHMICA. Then, a new comprehensive evaluation method is proposed, called “radar map method”, which could comprehensively evaluate the convergence and distribution performance of multi-objective optimization algorithm. It can be seen from the four coordinate axes of the radar maps that this is a symmetrical evaluation method. For this evaluation method, the larger the radar map area is, the better the calculation result of the algorithm. Using this new evaluation method, the algorithm proposed in this paper is compared with seven other high-quality algorithms. The radar map area of MOHMICA is at least 14.06% larger than that of other algorithms. Therefore, it is proven that MOHMICA has advantages as a whole. Full article
(This article belongs to the Special Issue Meta-Heuristics for Manufacturing Systems Optimization)
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29 pages, 3495 KiB  
Article
An Ensemble Framework of Evolutionary Algorithm for Constrained Multi-Objective Optimization
by Junhua Ku, Fei Ming and Wenyin Gong
Symmetry 2022, 14(1), 116; https://0-doi-org.brum.beds.ac.uk/10.3390/sym14010116 - 09 Jan 2022
Viewed by 1322
Abstract
In the real-world, symmetry or asymmetry widely exists in various problems. Some of them can be formulated as constrained multi-objective optimization problems (CMOPs). During the past few years, handling CMOPs by evolutionary algorithms has become more popular. Lots of constrained multi-objective optimization evolutionary [...] Read more.
In the real-world, symmetry or asymmetry widely exists in various problems. Some of them can be formulated as constrained multi-objective optimization problems (CMOPs). During the past few years, handling CMOPs by evolutionary algorithms has become more popular. Lots of constrained multi-objective optimization evolutionary algorithms (CMOEAs) have been proposed. Whereas different CMOEAs may be more suitable for different CMOPs, it is difficult to choose the best one for a CMOP at hand. In this paper, we propose an ensemble framework of CMOEAs that aims to achieve better versatility on handling diverse CMOPs. In the proposed framework, the hypervolume indicator is used to evaluate the performance of CMOEAs, and a decreasing mechanism is devised to delete the poorly performed CMOEAs and to gradually determine the most suitable CMOEA. A new CMOEA, namely ECMOEA, is developed based on the framework and three state-of-the-art CMOEAs. Experimental results on five benchmarks with totally 52 instances demonstrate the effectiveness of our approach. In addition, the superiority of ECMOEA is verified through comparisons to seven state-of-the-art CMOEAs. Moreover, the effectiveness of ECMOEA on the real-world problems is also evaluated for eight instances. Full article
(This article belongs to the Special Issue Meta-Heuristics for Manufacturing Systems Optimization)
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22 pages, 2104 KiB  
Article
A Novel Multi-Population Artificial Bee Colony Algorithm for Energy-Efficient Hybrid Flow Shop Scheduling Problem
by Yandi Zuo, Zhun Fan, Tierui Zou and Pan Wang
Symmetry 2021, 13(12), 2421; https://0-doi-org.brum.beds.ac.uk/10.3390/sym13122421 - 14 Dec 2021
Cited by 18 | Viewed by 2318
Abstract
Considering green scheduling and sustainable manufacturing, the energy-efficient hybrid flow shop scheduling problem (EHFSP) with a variable speed constraint is investigated, and a novel multi-population artificial bee colony algorithm (MPABC) is developed to minimize makespan, total tardiness and total energy consumption (TEC), simultaneously. [...] Read more.
Considering green scheduling and sustainable manufacturing, the energy-efficient hybrid flow shop scheduling problem (EHFSP) with a variable speed constraint is investigated, and a novel multi-population artificial bee colony algorithm (MPABC) is developed to minimize makespan, total tardiness and total energy consumption (TEC), simultaneously. It is necessary for manufacturers to fully understand the notion of symmetry in balancing economic and environmental indicators. To improve the search efficiency, the population was randomly categorized into a number of subpopulations, then several groups were constructed based on the quality of subpopulations. A different search strategy was executed in each group to maintain the population diversity. The historical optimization data were also used to enhance the quality of solutions. Finally, extensive experiments were conducted. The results demonstrate that MPABC can achieve an outstanding performance on three metrics DIR, c and nd for the considered EHFSP. Full article
(This article belongs to the Special Issue Meta-Heuristics for Manufacturing Systems Optimization)
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23 pages, 3821 KiB  
Article
Multi-UAV Cooperative Task Assignment Based on Half Random Q-Learning
by Pengxing Zhu and Xi Fang
Symmetry 2021, 13(12), 2417; https://0-doi-org.brum.beds.ac.uk/10.3390/sym13122417 - 14 Dec 2021
Cited by 13 | Viewed by 2483
Abstract
Unmanned aerial vehicle (UAV) clusters usually face problems such as complex environments, heterogeneous combat subjects, and realistic interference factors in the course of mission assignment. In order to reduce resource consumption and improve the task execution rate, it is very important to develop [...] Read more.
Unmanned aerial vehicle (UAV) clusters usually face problems such as complex environments, heterogeneous combat subjects, and realistic interference factors in the course of mission assignment. In order to reduce resource consumption and improve the task execution rate, it is very important to develop a reasonable allocation plan for the tasks. Therefore, this paper constructs a heterogeneous UAV multitask assignment model based on several realistic constraints and proposes an improved half-random Q-learning (HR Q-learning) algorithm. The algorithm is based on the Q-learning algorithm under reinforcement learning, and by changing the way the Q-learning algorithm selects the next action in the process of random exploration, the probability of obtaining an invalid action in the random case is reduced, and the exploration efficiency is improved, thus increasing the possibility of obtaining a better assignment scheme, this also ensures symmetry and synergy in the distribution process of the drones. Simulation experiments show that compared with Q-learning algorithm and other heuristic algorithms, HR Q-learning algorithm can improve the performance of task execution, including the ability to improve the rationality of task assignment, increasing the value of gains by 12.12%, this is equivalent to an average of one drone per mission saved, and higher success rate of task execution. This improvement provides a meaningful attempt for UAV task assignment. Full article
(This article belongs to the Special Issue Meta-Heuristics for Manufacturing Systems Optimization)
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30 pages, 3082 KiB  
Article
MTV-MFO: Multi-Trial Vector-Based Moth-Flame Optimization Algorithm
by Mohammad H. Nadimi-Shahraki, Shokooh Taghian, Seyedali Mirjalili, Ahmed A. Ewees, Laith Abualigah and Mohamed Abd Elaziz
Symmetry 2021, 13(12), 2388; https://0-doi-org.brum.beds.ac.uk/10.3390/sym13122388 - 10 Dec 2021
Cited by 34 | Viewed by 3055
Abstract
The moth-flame optimization (MFO) algorithm is an effective nature-inspired algorithm based on the chemical effect of light on moths as an animal with bilateral symmetry. Although it is widely used to solve different optimization problems, its movement strategy affects the convergence and the [...] Read more.
The moth-flame optimization (MFO) algorithm is an effective nature-inspired algorithm based on the chemical effect of light on moths as an animal with bilateral symmetry. Although it is widely used to solve different optimization problems, its movement strategy affects the convergence and the balance between exploration and exploitation when dealing with complex problems. Since movement strategies significantly affect the performance of algorithms, the use of multi-search strategies can enhance their ability and effectiveness to solve different optimization problems. In this paper, we propose a multi-trial vector-based moth-flame optimization (MTV-MFO) algorithm. In the proposed algorithm, the MFO movement strategy is substituted by the multi-trial vector (MTV) approach to use a combination of different movement strategies, each of which is adjusted to accomplish a particular behavior. The proposed MTV-MFO algorithm uses three different search strategies to enhance the global search ability, maintain the balance between exploration and exploitation, and prevent the original MFO’s premature convergence during the optimization process. Furthermore, the MTV-MFO algorithm uses the knowledge of inferior moths preserved in two archives to prevent premature convergence and avoid local optima. The performance of the MTV-MFO algorithm was evaluated using 29 benchmark problems taken from the CEC 2018 competition on real parameter optimization. The gained results were compared with eight metaheuristic algorithms. The comparison of results shows that the MTV-MFO algorithm is able to provide competitive and superior results to the compared algorithms in terms of accuracy and convergence rate. Moreover, a statistical analysis of the MTV-MFO algorithm and other compared algorithms was conducted, and the effectiveness of our proposed algorithm was also demonstrated experimentally. Full article
(This article belongs to the Special Issue Meta-Heuristics for Manufacturing Systems Optimization)
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22 pages, 2881 KiB  
Article
Collaborative Production Task Decomposition and Allocation among Multiple Manufacturing Enterprises in a Big Data Environment
by Feng Li, Xiya Li, Yun Yang, Yan Xu and Yan Zhang
Symmetry 2021, 13(12), 2268; https://0-doi-org.brum.beds.ac.uk/10.3390/sym13122268 - 28 Nov 2021
Cited by 3 | Viewed by 2332
Abstract
To realize the efficient decomposition and allocation of collaborative production tasks and resources among multiple enterprises, a task decomposition and allocation model for collaborative production among multiple manufacturing enterprises is proposed in a big data environment. The model is designed for the efficient [...] Read more.
To realize the efficient decomposition and allocation of collaborative production tasks and resources among multiple enterprises, a task decomposition and allocation model for collaborative production among multiple manufacturing enterprises is proposed in a big data environment. The model is designed for the efficient and fast processing of production information using big data technology. This study innovatively applies the 5S management method to conduct data preprocessing for a manufacturing service provider and design the operation process of data cleaning and conversion to improve the efficiency of data processing. A collaborative optimization model, based on a hierarchical model with seven levels and considering time, costs, and services, is established for the task of production to achieve a reasonable match between supply and demand. Finally, the correlation coefficients of manufacturing service providers are configured according to weight order, so that the weight order is symmetrical with that of the manufacturer. The model also engages all manufacturing service providers with different production capabilities in collaborative production. The model is proved to be scientific and effective by using a specific example. In cooperative production activities, the production tasks of small and medium-sized enterprises can be effectively allocated. It can also realize efficient cooperative production among multiple manufacturing enterprises. Full article
(This article belongs to the Special Issue Meta-Heuristics for Manufacturing Systems Optimization)
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15 pages, 868 KiB  
Article
A Novel Shuffled Frog-Leaping Algorithm for Unrelated Parallel Machine Scheduling with Deteriorating Maintenance and Setup Time
by Deming Lei and Tian Yi
Symmetry 2021, 13(9), 1574; https://0-doi-org.brum.beds.ac.uk/10.3390/sym13091574 - 26 Aug 2021
Cited by 10 | Viewed by 1487
Abstract
Unrelated parallel machine scheduling problems (UPMSP) with various processing constraints have been considered fully; however, a UPMSP with deteriorating preventive maintenance (PM) and sequence-dependent setup time (SDST) is seldom considered. In this study, a new differentiated shuffled frog-leaping algorithm (DSFLA) is presented to [...] Read more.
Unrelated parallel machine scheduling problems (UPMSP) with various processing constraints have been considered fully; however, a UPMSP with deteriorating preventive maintenance (PM) and sequence-dependent setup time (SDST) is seldom considered. In this study, a new differentiated shuffled frog-leaping algorithm (DSFLA) is presented to solve the problem with makespan minimization. The whole search procedure consists of two phases. In the second phase, quality evaluation is done on each memeplex, then the differentiated search processes are implemented between good memeplexes and other ones, and a new population shuffling is proposed. We conducted a number of experiments. The computational results show that the main strategies of DSFLA were effective and reasonable and DSFLA was very competitive at solving UPMSP with deteriorating PM and SDST. Full article
(This article belongs to the Special Issue Meta-Heuristics for Manufacturing Systems Optimization)
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17 pages, 3730 KiB  
Article
Path Planning of AS/RS Based on Cost Matrix and Improved Greedy Algorithm
by Dongdong Li, Lei Wang, Sai Geng and Benchi Jiang
Symmetry 2021, 13(8), 1483; https://0-doi-org.brum.beds.ac.uk/10.3390/sym13081483 - 12 Aug 2021
Cited by 8 | Viewed by 1810
Abstract
Logistics plays an important role in the field of global economy, and the storage and retrieval of tasks in a warehouse which has symmetry is the most important part of logistics. Generally, the shelves of a warehouse have a certain degree of symmetry [...] Read more.
Logistics plays an important role in the field of global economy, and the storage and retrieval of tasks in a warehouse which has symmetry is the most important part of logistics. Generally, the shelves of a warehouse have a certain degree of symmetry and similarity in their structure. The storage and retrieval efficiency directly affects the efficiency of logistics. The efficiency of the traditional storage and retrieval mode has become increasingly inconsistent with the needs of the industry. In order to solve this problem, this paper proposes a greedy algorithm based on cost matrix to solve the path planning problem of the automatic storage and retrieval system (AS/RS). Firstly, aiming at the path planning mathematical model of AS/RS, this paper proposes the concept of cost matrix, which transforms the traditional storage and retrieval problem into the element combination problem of cost matrix. Then, a more efficient backtracking algorithm is proposed based on the exhaustive method. After analyzing the performance of the backtracking algorithm, combined with some rules, a greedy algorithm which can further improve efficiency is proposed; the convergence of the improved greedy algorithm is also proven. Finally, through simulation, the time consumption of the greedy algorithm is only 0.59% of the exhaustive method, and compared with the traditional genetic algorithm, the time consumption of the greedy algorithm is about 50% of the genetic algorithm, and it can still maintain its advantage in time consumption, which proves that the greedy algorithm based on cost matrix has a certain feasibility and practicability in solving the path planning of the automatic storage and retrieval system. Full article
(This article belongs to the Special Issue Meta-Heuristics for Manufacturing Systems Optimization)
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19 pages, 2507 KiB  
Article
A Method for Designing and Optimizing the Electrical Parameters of Dynamic Tuning Passive Filter
by Yifei Wang, Kaiyang Yin, Huikang Liu and Youxin Yuan
Symmetry 2021, 13(7), 1115; https://0-doi-org.brum.beds.ac.uk/10.3390/sym13071115 - 23 Jun 2021
Cited by 8 | Viewed by 2325
Abstract
Power electronics-based apparatuses absorb non-sinusoidal currents. These are considered non-linear and non-symmetrical loads for the power grid, and they generate a harmonic current. The dynamic tuning passive filter (DTPF) is one of the best solutions for improving power quality and filtering out harmonic [...] Read more.
Power electronics-based apparatuses absorb non-sinusoidal currents. These are considered non-linear and non-symmetrical loads for the power grid, and they generate a harmonic current. The dynamic tuning passive filter (DTPF) is one of the best solutions for improving power quality and filtering out harmonic currents to get a symmetrical current waveform. The electrical parameters of DTPF can influence its absorbing harmonic current, tuning performance, and cost. In this paper, a method for designing and optimizing the electrical parameters of dynamic tuning passive filter is proposed in order to improve the effectiveness of DTPF and the symmetry level of the power source. First, according to the characteristics of the harmonic source, the design technical indicators of DTPF, and its topology, the design procedure for the electrical parameters of DTPF is proposed. Second, based on detailed analysis of the test results, the range of the harmonic current absorption coefficient is determined. Third, the range of the relationship coefficient is determined by analyzing the impact of the filter capacitor’s capacity on the filter performance. Fourth, the calculation method for the electrical parameters of DTPF is devised. Finally, the validity of this method is verified by several engineering cases, and the electrical parameters of the filter capacitor and electromagnetic coupling reactance converter (ECRC) under the lowest total cost are simulated and optimized. Our approach can optimize the electrical parameters of DTPF and improve the harmonic suppression effectiveness, thus leading to a more symmetrical waveform and successfully avoiding power grid problems. The research results of this study not only provide a basis for the design of ECRC, but also lay a foundation for the machining DTPF. Full article
(This article belongs to the Special Issue Meta-Heuristics for Manufacturing Systems Optimization)
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