1. the complete title of one (or more) paper(s) published in the open literature describing the work that the author claims describes a human-competitive result; Heuristic Navigation Model based on Genetic Programming for Multi-UAV Power Inspection Problem with Charging Stations 2. the name, complete physical mailing address, e-mail address, and phone number of EACH author of EACH paper(s); Xiang-Ling Chen, xiao_chentongxue@163.com +86 17810271125 School of Engineering and Computer Science, South China University of Technology, Guangzhou Higher Education Mega Centre, Panyu District, Guangzhou, 510006 Xiao-Cheng Liao, dejavu.rabbyt@gmail.com +64 27 888 3638 School of Engineering and Computer Science, Room CO358, Cotton Building, Victoria University of Wellington, Gate 7, Kelburn Parade, Wellington, New Zealand Wei-neng Chen, cschenwn@scut.edu.cn +86 13763363263 School of Engineering and Computer Science, South China University of Technology, Guangzhou Higher Education Mega Centre, Panyu District, Guangzhou, 510006 3. the name of the corresponding author (i.e., the author to whom notices will be sent concerning the competition); Wei-neng Chen 4. the abstract of the paper(s); Efficient power inspection is crucial for maintaining a stable power system. During an inspection, unmanned aerial vehicles (UAVs) usually need to be recharged due to the wide geographical range of inspection and the limited battery capacity of UAVs. This limitation makes the problem more challenging that requires not only optimizing the task execution order, but also taking the charging of UAVs into consideration. In order to address this complex problem, this work first formulates the UAV power inspection planning problem with charging stations. After that, we propose a new heuristic navigation model, in which UAVs can follow a heuristic rule to decide where to go next based on both its own information and task-related information. To obtain the heuristic rule, we design a set of features to describe the status of the UAVs and task completion. Then a genetic programming (GP) algorithm is introduced to evolve and get the heuristic rule. Finally, by applying heuristic navigation rule, the UAV navigation model can automatically prioritize task and charging order, and generate UAV flight routes that satisfy all constraints. The experiment results show that our method significantly outperforms the state-of-the-art algorithms. 5. a list containing one or more of the eight letters (A, B, C, D, E, F, G, or H) that correspond to the criteria (see above) that the author claims that the work satisfies; (B) The result is equal to or better than a result that was accepted as a new scientific result at the time when it was published in a peer-reviewed scientific journal. (D) The result is publishable in its own right as a new scientific result  independent of the fact that the result was mechanically created. (E) The result is equal to or better than the most recent human-created solution to a long-standing problem for which there has been a succession of increasingly better human-created solutions. (G) The result solves a problem of indisputable difficulty in its field. 6. a statement stating why the result satisfies the criteria that the contestant claims (see examples of statements of human-competitiveness as a guide to aid in constructing this part of the submission); re: (B) "The result is equal to or better than a result that was accepted as a new scientific result at the time when it was published in a peer-reviewed scientific journal." We propose a centralized training and distributed solution construction approach to jointly optimize the task execution order and charging schedule of UAVs, showing its superiority in terms of its ability to find feasible solutions and overall performance. Additionally, in UAV charging optimization, our algorithm outperforms the state-of-the-art removal-heuristic algorithm (published in IEEE Transactions on Cybernetics in 2021). re: (D) "The result is publishable in its own right as a new scientific result  independent of the fact that the result was mechanically created." We propose a novel method utilizing genetic programming expression to solve the UAV charging and inspection problems. By evolving heuristic rules through genetic programming, this method offers a new approach to addressing complex optimization problems. The proposed method significantly improves the efficiency and effectiveness of UAV task assignment and charging scheduling, reducing operational costs. It automatically generates and optimizes heuristic rules based on UAV and task-related features, demonstrating a substantial advancement over existing methods. Additionally, this method can be applied not only to UAV task assignment and charging scheduling but also to other areas requiring automated and intelligent decision-making, such as logistics distribution and intelligent transportation systems. The results of this paper are promising and the paper has been accepted as a full paper by SMC 2023, has received high praise: - "It presents novel contributions and is well-organized and written." - "The paper is well-written, the content is rigorous and the work is solid." - "The paper is well-written, making it easy to follow the presented ideas. It offers a sufficient level of novelty, and the results obtained are promising when compared to the sota method." It has been nominated for the Best Student Paper Award: Dear Xiang-Ling Chen, Xiao-Cheng Liao, and Wei-Neng Chen: Thank you for your participation as authors of the following paper in the IEEE SMC 2023 conference upcoming in Honolulu, Hawaii. #0642 "Heuristic Navigation Model based on Genetic Programming for Multi-UAV Power Inspection Problem with Charging Stations" I am the Awards Committee Chair for the IEEE SMC Society. The Awards Committee handles, among other Society awards, the best paper award selections and process associated with IEEE SMC 2023 papers considered as award finalists. I am contacting you to inform you that your paper has been selected as one of the finalist papers, among several, for the SMC Best Student Paper Award, which is for both the best student oral presentation and paper to be determined after judging by a panel of judges at the conference. Congratulations. re: (E) "The result is equal to or better than the most recent human-created solution to a long-standing problem for which there has been a succession of increasingly better human-created solutions." By utilizing genetic programming to evolve heuristic rules, our method introduces a novel approach to solving complex optimization problems. This automated and adaptive technique surpasses traditional human-designed heuristic methods, which typically require manual intervention and exhibit limited flexibility in handling dynamic and complex scenarios. Our method significantly improves the efficiency and effectiveness of UAV task assignment and charging scheduling, resulting in an average cost reduction of 30% or more when addressing large-scale problems. Experimental results demonstrate that our algorithm outperforms existing state-of-the-art human-created algorithms, including the best Removal Heuristic (RH) method, in terms of finding feasible solutions and overall performance. re: (G) "The result solves a problem of indisputable difficulty in its field." TAPCS is particularly difficult due to the existence of charging stations makes it more difficult to manage the UAVs, as we must consider two aspects: (1) all tasks must be completed effectively and (2) the UAV must obey the constraints of battery power availability during flight route. These two aspects are interrelated and further complicate the whole problem. Also, it is very challenging to design effective heuristic rules manually, as the selection rule has to consider various factors like the remaining power of UAVs, the distances to the next task, the spatial distribution of the remaining tasks. TAPCS adds charging, task assignment, and flexibility requirements to other widely studied combinatorial optimization problems such as vehicle/arc routing problems, which are already NP-Hard. 7. a full citation of the paper (that is, author names; title, publication date; name of journal, conference, or book in which article appeared; name of editors, if applicable, of the journal or edited book; publisher name; publisher city; page numbers, if applicable); Chen, Xiang-Ling, Xiao-Cheng Liao, and Wei-Neng Chen. "Heuristic Navigation Model Based on Genetic Programming for Multi-UAV Power Inspection Problem with Charging Stations." 2023 IEEE International Conference on Systems, Man, and Cybernetics (SMC). IEEE, 2023 8. a statement either that "any prize money, if any, is to be divided equally among the co-authors" OR a specific percentage breakdown as to how the prize money, if any, is to be divided among the co-authors; Any prize money, if any, is to be divided equally among the co-authors. 9. a statement stating why the authors expect that their entry would be the "best," and Firstly, this paper aims to address an extremely important real-world problem: UAV power grid inspection. UAV power grid inspection plays a crucial role in maintaining the stability of the power system. Compared to human-designed methods, our proposed approach further reduces the average cost of UAVs by more than 30% on large-scale instances. This reduction can be attributed to more rational choices of charging stations/tasks and fewer charging cycles, thereby lowering operational costs. Moreover, the optimized heuristic rules can be extended to other domains requiring automated and intelligent decision-making, such as logistics distribution, thereby enhancing social productivity and the level of intelligence. Secondly, this paper proposes a method to evolve human-understandable heuristic rules. Our approach does not require the complex heuristics that are typically manually designed, which not only involve a vast amount of domain knowledge and expertise but also fail to consider all influencing factors. Our method utilizes genetic programming to automatically evolve heuristic rules, which, compared to traditional human-designed heuristic methods, exhibits higher adaptability and flexibility, demonstrating excellent performance in solving complex optimization problems. This means that practitioners do not need additional experience and expertise from other domains to use our algorithm, which can greatly promote the practical application of research. 10. An indication of the general type of genetic or evolutionary computation used, such as GA (genetic algorithms), GP (genetic programming), ES (evolution strategies), EP (evolutionary programming), LCS (learning classifier systems), GI (genetic improvement), GE (grammatical evolution), GEP (gene expression programming), DE (differential evolution), etc. GEP (gene expression programming) 11. The date of publication of each paper.  If the date of publication is not on or before the deadline for submission, but instead, the paper has been unconditionally accepted for publication and is “in press” by the deadline for this competition, the entry must include a copy of the documentation establishing that the paper meets the "in press" requirement. Published: 29 January 2024 (https://ieeexplore.ieee.org/document/10394169)