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Yu Q, Zheng Y, Zhang P, Zeng L, Han R, Shi Y, Li D. Genetic programming-based predictive model for the Cr removal effect of in-situ electrokinetic remediation in contaminated soil. JOURNAL OF HAZARDOUS MATERIALS 2023; 460:132430. [PMID: 37659239 DOI: 10.1016/j.jhazmat.2023.132430] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/14/2023] [Revised: 08/14/2023] [Accepted: 08/27/2023] [Indexed: 09/04/2023]
Abstract
Soil electrokinetic remediation is an emerging and efficient in-situ remediation technology for reducing environmental risks. Promoting the dissolution and migration of Cr in soil under the electric field is crucial to decrease soil toxicity and ecological influences. However, it is difficult to establish strong relationships between soil treatment and impact factors and to quantify their contributions. Machine learning can help establish pollutant migration models, but it is challenging to derive predictive formulas to improve remediation efficiency, describe the predictive model construction process, and reflect the importance of the predictors for better regulation. Therefore, this paper established a predictive model for the electrokinetic remediation of Cr-contaminated soil using genetic programming (GP) after determining the characteristic parameters which influenced the remediation effect, described the model's adaptive optimization process through the algorithm's function, and identified the sensitivity factors affecting the Cr removal effect. Results showed that the Cr(VI) and total Cr concentrations predicted by GP were in satisfactory agreement with the experimental values, 92% of the training data and 90% of the validation data achieved errors within 1%, and could fully reflect the target ions' content variation in different soil layers. By substituting the above prediction formulas into Sobol sensitivity analysis, it was determined that conductivity, pH, current, and moisture content dramatically affected the Cr content variation in distinguished regions. For overall contaminated area, the system current and soil pH were the most sensitive factors for Cr(VI) and total Cr contents. Remediation efforts throughout the contaminated area should focus on the role of current versus soil pH. GP and sensitivity analysis can provide decision support and operational guidance for in-situ soil electrokinetic treatment by establishing a remediation effect prediction model, expediting the development and innovation of electrokinetic technology.
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Affiliation(s)
- Qiu Yu
- State Key Laboratory of Coal Mine Disaster Dynamics and Control, Chongqing University, Chongqing 400044, China; College of Resources and Safety Engineering, Chongqing University, Chongqing 400044, China
| | - Yi Zheng
- State Key Laboratory of Coal Mine Disaster Dynamics and Control, Chongqing University, Chongqing 400044, China; College of Resources and Safety Engineering, Chongqing University, Chongqing 400044, China
| | - Pengpeng Zhang
- State Key Laboratory of Coal Mine Disaster Dynamics and Control, Chongqing University, Chongqing 400044, China; College of Resources and Safety Engineering, Chongqing University, Chongqing 400044, China
| | - Linghao Zeng
- State Key Laboratory of Coal Mine Disaster Dynamics and Control, Chongqing University, Chongqing 400044, China; College of Resources and Safety Engineering, Chongqing University, Chongqing 400044, China
| | - Renhui Han
- State Key Laboratory of Coal Mine Disaster Dynamics and Control, Chongqing University, Chongqing 400044, China; College of Resources and Safety Engineering, Chongqing University, Chongqing 400044, China
| | - Yaoming Shi
- State Key Laboratory of Coal Mine Disaster Dynamics and Control, Chongqing University, Chongqing 400044, China; College of Resources and Safety Engineering, Chongqing University, Chongqing 400044, China
| | - Dongwei Li
- State Key Laboratory of Coal Mine Disaster Dynamics and Control, Chongqing University, Chongqing 400044, China; College of Resources and Safety Engineering, Chongqing University, Chongqing 400044, China.
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Gao G, Mei Y, Xin B, Jia YH, Browne WN. Automated Coordination Strategy Design Using Genetic Programming for Dynamic Multipoint Dynamic Aggregation. IEEE TRANSACTIONS ON CYBERNETICS 2022; 52:13521-13535. [PMID: 34077383 DOI: 10.1109/tcyb.2021.3080044] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
The multipoint dynamic aggregation (MPDA) problem of the multirobot system is of great significance for its real-world applications such as bush fire elimination. The problem is to design the optimal plan for a set of heterogeneous robots to complete some geographically distributed tasks collaboratively. In this article, we consider the dynamic version of the problem, where new tasks keep appearing after the robots are dispatched from the depot. The dynamic MPDA problem is a complicated optimization problem due to several characteristics, such as the collaboration of robots, the accumulative task demand, the relationships among robots and tasks, and the unpredictable task arrivals. In this article, a new model of the problem considering these characteristics is proposed. To solve the problem, we develop a new genetic programming hyperheuristic (GPHH) method to evolve reactive coordination strategies (RCSs), which can guide the robots to make decisions in real time. The proposed GPHH method contains a newly designed effective RCS heuristic template to generate the execution plan for the robots according to a GP tree. A new terminal set of features related to both robots and tasks and a cluster filter that assigns the robots to urgent tasks are designed. The experimental results show that the proposed GPHH significantly outperformed the state-of-the-art methods. Through further analysis, useful insights such as how to distribute and coordinate robots to execute different types of tasks are discovered.
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Đurasević M, Đumić M. Automated design of heuristics for the container relocation problem using genetic programming. Appl Soft Comput 2022. [DOI: 10.1016/j.asoc.2022.109696] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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Zhu L, Lin J, Li YY, Wang ZJ. A decomposition-based multi-objective genetic programming hyper-heuristic approach for the multi-skill resource constrained project scheduling problem. Knowl Based Syst 2021. [DOI: 10.1016/j.knosys.2021.107099] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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Comparison of schedule generation schemes for designing dispatching rules with genetic programming in the unrelated machines environment. Appl Soft Comput 2020. [DOI: 10.1016/j.asoc.2020.106637] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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Zhang W, Ding J, Wang Y, Zhang S, Zhuang X. Energy-efficient bi-objective manufacturing scheduling with intermediate buffers using a three-stage genetic algorithm. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2020. [DOI: 10.3233/jifs-191072] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Affiliation(s)
- Wenyu Zhang
- School of Information Management and Artificial Intelligence, Zhejiang University of Finance and Economics, Hangzhou, China
| | - Jiepin Ding
- School of Information Management and Artificial Intelligence, Zhejiang University of Finance and Economics, Hangzhou, China
| | - Yan Wang
- School of Information Management and Artificial Intelligence, Zhejiang University of Finance and Economics, Hangzhou, China
| | - Shuai Zhang
- School of Information Management and Artificial Intelligence, Zhejiang University of Finance and Economics, Hangzhou, China
| | - Xiaoyu Zhuang
- School of Information Management and Artificial Intelligence, Zhejiang University of Finance and Economics, Hangzhou, China
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Xu B, Mei Y, Wang Y, Ji Z, Zhang M. Genetic Programming with Delayed Routing for Multiobjective Dynamic Flexible Job Shop Scheduling. EVOLUTIONARY COMPUTATION 2020; 29:75-105. [PMID: 32375006 DOI: 10.1162/evco_a_00273] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Dynamic Flexible Job Shop Scheduling (DFJSS) is an important and challenging problem, and can have multiple conflicting objectives. Genetic Programming Hyper-Heuristic (GPHH) is a promising approach to fast respond to the dynamic and unpredictable events in DFJSS. A GPHH algorithm evolves dispatching rules (DRs) that are used to make decisions during the scheduling process (i.e., the so-called heuristic template). In DFJSS, there are two kinds of scheduling decisions: the routing decision that allocates each operation to a machine to process it, and the sequencing decision that selects the next job to be processed by each idle machine. The traditional heuristic template makes both routing and sequencing decisions in a non-delay manner, which may have limitations in handling the dynamic environment. In this article, we propose a novel heuristic template that delays the routing decisions rather than making them immediately. This way, all the decisions can be made under the latest and most accurate information. We propose three different delayed routing strategies, and automatically evolve the rules in the heuristic template by GPHH. We evaluate the newly proposed GPHH with Delayed Routing (GPHH-DR) on a multiobjective DFJSS that optimises the energy efficiency and mean tardiness. The experimental results show that GPHH-DR significantly outperformed the state-of-the-art GPHH methods. We further demonstrated the efficacy of the proposed heuristic template with delayed routing, which suggests the importance of delaying the routing decisions.
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Affiliation(s)
- Binzi Xu
- School of Electrical Engineering, Anhui Polytechnic University, Wuhu, 241000, PR China School of IoT and Engineering, Jiangnan University, Wuxi, 214122, PR China School of Engineering and Computer Science, Victoria University of Wellington, Wellington 6140, New Zealand
| | - Yi Mei
- School of Engineering and Computer Science, Victoria University of Wellington, Wellington 6140, New Zealand
| | - Yan Wang
- School of IoT and Engineering, Jiangnan University, Wuxi, 214122, PR China
| | - Zhicheng Ji
- School of IoT and Engineering, Jiangnan University, Wuxi, 214122, PR China
| | - Mengjie Zhang
- School of Engineering and Computer Science, Victoria University of Wellington, Wellington 6140, New Zealand
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