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Applied Sciences
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Heuristic and Evolutionary Algorithms for Engineering Optimization
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Heuristic and Evolutionary Algorithms for Engineering Optimization

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 31 October 2024 | Viewed by 2835

Special Issue Editors

Dr. Junwen Ding

E-Mail Website
Guest Editor
School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China
Interests: heuristic optimization methods; manufacturing system scheduling and optimization; complex system modeling
Prof. Dr. Yang Wang

E-Mail Website
Guest Editor
School of Management, Northwestern Polytechnical University, Xi'an 710072, China
Interests: combinatorial optimization; heuristic algorithms
Prof. Dr. Liji Shen

E-Mail Website
Guest Editor
Supply Chain Management Group, WHU – Otto Beisheim School of Management, 56179 Vallendar, Germany
Interests: metaheuristic methodologies; combinatorial optimization problems; the interface between operational research and artificial intelligence

Special Issue Information

Dear Colleagues,

In the ever-evolving landscape of engineering, the pursuit of optimization lies at the heart of innovation and progress. Applied Sciences is pleased to announce a Special Issue dedicated to exploring the intricate realm of “Heuristic and Evolutionary Algorithms for Engineering Optimization”. As industries strive for efficiency, sustainability, and excellence, the utilization of heuristic and evolutionary algorithms has emerged as a pivotal tool in tackling the complexities of optimization across a myriad of engineering domains. This Special Issue aims to delve deep into the innovative methodologies, applications, and advancements in the field, shedding light on the transformative potential of heuristic and evolutionary algorithms in shaping the future of engineering. Delving into the development and analysis of heuristic and evolutionary algorithms, this Special Issue welcomes contributions that offer novel insights and advancements in algorithmic design. Hybrid and parallel heuristic optimization techniques, which combine the strengths of different algorithms or utilize parallel computing architectures, are of particular interest; multi-objective optimization, a critical aspect of engineering design, is another focal point, with a focus on methodologies that leverage heuristic and evolutionary algorithms to navigate the complexities of optimizing conflicting objectives.

Dr. Junwen Ding
Prof. Dr. Yang Wang
Prof. Dr. Liji Shen
Guest Editors

Manuscript Submission Information

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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

  • engineering optimization problems
  • complex system modelling and optimization
  • control and manufacturing applications
  • multi/many-objective optimization
  • evolutionary computation
  • swarm intelligence
  • meta-heuristics
  • genetic algorithm
  • particle swarm optimization
  • ant colony optimization
  • knowledge processing

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Published Papers (4 papers)

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Research

22 pages, 8713 KiB  
Article
A Path Planning Method Based on Hybrid Sand Cat Swarm Optimization Algorithm of Green Multimodal Transportation
by Zhe Sun, Qiming Yang, Junyi Liu, Xu Zhang and Zhixin Sun
Appl. Sci. 2024, 14(17), 8024; https://doi.org/10.3390/app14178024 - 8 Sep 2024
Viewed by 645
Abstract
Aiming at the difficulty of measuring various costs and time-consuming elements in multimodal transport, this paper constructs a green vehicle comprehensive multimodal transport model which incorporates transportation, transit, quality damage, fuel consumption, and carbon emission costs and proposes a hybrid embedded time window [...] Read more.
Aiming at the difficulty of measuring various costs and time-consuming elements in multimodal transport, this paper constructs a green vehicle comprehensive multimodal transport model which incorporates transportation, transit, quality damage, fuel consumption, and carbon emission costs and proposes a hybrid embedded time window to calculate the time penalty cost in order to reflect the actual transport characteristics. Furthermore, in order to better solve the model, a hybrid sand cat swarm optimization (HSCSO) algorithm is proposed by introducing Logistic–Tent chaotic mapping and an adaptive lens opposition-based learning strategy to enhance the global search capability, and inspired by the swarm intelligence scheme, a momentum–bellicose strategy and an equilibrium crossover pool are introduced to improve the search efficiency and convergence ability. Through testing nine benchmark functions, the HSCSO algorithm exhibits superior convergence accuracy and speed in dealing with complex multi-dimensional problems. Based on the excellent global performance, the HSCSO algorithm was utilized for multimodal vehicle transportation in East China, and a path with a lower comprehensive cost was successfully planned, which proved the effectiveness of the HSCSO algorithm in green intermodal transport path planning. Full article
(This article belongs to the Special Issue Heuristic and Evolutionary Algorithms for Engineering Optimization)
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21 pages, 3431 KiB  
Article
Inspiring Designers’ Innovative Thinking: An Evolutionary Design Method for Product Forms
by Shifeng Liu, Jianning Su, Shutao Zhang, Kai Qiu and Shijie Wang
Appl. Sci. 2024, 14(17), 7818; https://doi.org/10.3390/app14177818 - 3 Sep 2024
Viewed by 591
Abstract
The product form serves as a crucial information carrier for expressing design concepts and encompasses significant valuable references. During the product iteration process, changes in design subjects, such as designers and decision-makers, result in substantial variability and uncertainty in the direction of product [...] Read more.
The product form serves as a crucial information carrier for expressing design concepts and encompasses significant valuable references. During the product iteration process, changes in design subjects, such as designers and decision-makers, result in substantial variability and uncertainty in the direction of product form evolution. To address these issues, an evolutionary design method for product forms based on the gray Markov model and an evolutionary algorithm is proposed in this study. Firstly, quadratic curvature entropy is utilized to quantify historical form features of product evolution. Subsequently, the original data on product form feature evolution are fitted and predicted using the gray Markov model, thereby obtaining the predicted value of the latest generation of product form features, which is determined to be 0.14586. Finally, this study uses this predicted value to construct a fitness function in the framework of an evolutionary algorithm, which in turn identifies next-generation product forms that can stimulate designers’ creative thinking. The method’s application is illustrated using the side outer contour of the Audi A4 automobile as an example. The research findings demonstrate that combining the gray Markov model with an evolutionary algorithm can effectively simulate designers’ understanding of previous generations’ design concepts and achieve stable inheritance of these design concepts during product iteration. This approach mitigates the risk of abrupt changes in design concepts caused by designers and decision-makers due to personal cognitive biases, thereby enhancing product development efficiency. Full article
(This article belongs to the Special Issue Heuristic and Evolutionary Algorithms for Engineering Optimization)
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16 pages, 2333 KiB  
Article
A Four-Label-Based Algorithm for Solving Stable Extension Enumeration in Abstract Argumentation Frameworks
by Mao Luo, Ningning He, Xinyun Wu, Caiquan Xiong and Wanghao Xu
Appl. Sci. 2024, 14(17), 7656; https://doi.org/10.3390/app14177656 - 29 Aug 2024
Viewed by 451
Abstract
In abstract argumentation frameworks, the computation of stable extensions is an important semantic task for evaluating the acceptability of arguments. The current approaches for the computation of stable extensions are typically conducted through methodologies that are either label-based or extension-based. Label-based algorithms operate [...] Read more.
In abstract argumentation frameworks, the computation of stable extensions is an important semantic task for evaluating the acceptability of arguments. The current approaches for the computation of stable extensions are typically conducted through methodologies that are either label-based or extension-based. Label-based algorithms operate by assigning labels to each argument, thus reducing the attack relations between arguments to constraint relations among the labels. This paper analyzes the existing two-label and three-label enumeration algorithms for stable extensions through case studies. It is found that both the two-label and three-label algorithms are not precise enough in defining types of arguments. To address these issues, this paper proposes a four-label enumeration algorithm for stable extensions. This method introduces amust_in label to pre-mark certain in-type arguments, thereby achieving a finer classification of in-type arguments. This enhances the labelings’ propagation ability and reduces the algorithm’s search space. Our proposed four-label algorithm was tested on authoritative benchmark sets of abstract argumentation framework problems: ICCMA 2019, ICCMA 2021, and ICCMA 2023. Experimental results show that the four-label algorithm significantly improves solving efficiency compared to existing two-label and three-label algorithms. Additionally, ablation experiments confirm that both the four-label transition strategy and preprocessing strategy enhance the algorithm’s performance. Full article
(This article belongs to the Special Issue Heuristic and Evolutionary Algorithms for Engineering Optimization)
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27 pages, 648 KiB  
Article
Dual-Neighborhood Tabu Search for Computing Stable Extensions in Abstract Argumentation Frameworks
by Yuanzhi Ke, Xiaogang Hu, Junjie Sun, Xinyun Wu, Caiquan Xiong and Mao Luo
Appl. Sci. 2024, 14(15), 6428; https://doi.org/10.3390/app14156428 - 23 Jul 2024
Viewed by 706
Abstract
Abstract argumentation has become one of the important fields of artificial intelligence. This paper proposes a dual-neighborhood tabu search (DNTS) method specifically designed to find a single stable extension in abstract argumentation frameworks. The proposed algorithm implements an improved dual-neighborhood strategy incorporating a [...] Read more.
Abstract argumentation has become one of the important fields of artificial intelligence. This paper proposes a dual-neighborhood tabu search (DNTS) method specifically designed to find a single stable extension in abstract argumentation frameworks. The proposed algorithm implements an improved dual-neighborhood strategy incorporating a fast neighborhood evaluation method. In addition, by introducing techniques such as tabu and perturbation, this algorithm is able to jump out of the local optimum, which significantly improves the performance of the algorithm. In order to evaluate the effectiveness of the method, the performance of the algorithm on more than 300 randomly generated benchmark datasets was studied and compared with the algorithm in the literature. In the experiment, DNTS outperforms the other method regarding time consumption in more than 50 instances and surpasses the other meta-heuristic method in the number of solved cases. Further analysis shows that the initialization method, the tabu strategy, and the perturbation technique help guarantee the efficiency of the proposed DNTS. Full article
(This article belongs to the Special Issue Heuristic and Evolutionary Algorithms for Engineering Optimization)
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