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. Paper 1: A Gated Recurrent Unit Model with Fibonacci Attenuation Particle Swarm Optimization for Carbon Emission Prediction Paper 2: Pair barracuda swarm optimization algorithm: a natural-inspired metaheuristic method for high dimensional optimization problems Paper 3: A novel hermit crab optimization algorithm 2. The name, complete physical mailing address, e-mail address, and phone number of EACH author of EACH paper(s). Authors: a) Jia Guo jia.guo.46@hosei.ac.jp +81 042-387-6023 b) Yuji Sato yuji@hosei.ac.jp +81 042-387-6023 c) Guoyuan Zhou zhouguoyuan@webmail.hzau.edu.cn +86 027-87282027 d) Jiacheng Li lijiacheng@kanagawa-u.ac.jp +81 045-481-5661 e) Binghua Shi shibinghua@hbue.edu.cn +86 027-81978119 f) Yi Di diyi8710@hbue.edu.cn +86 027-81978119 g) Zhou Yan yanzhou@hbue.edu.cn +86 027-81978119 h) Ke Yan yan-ke@cscec.com +86 027-65275498 Address for authors (a,b): Faculty of Computer and Information Sciences, Hosei University, 3-7-2 Kajino-cho, Koganei-shi, Tokyo 184-8584, Japan Address for author (c): College of Informatics, Huazhong Agricultural University, No.1,Shizishan Street, Wuhan 430070, Hubei China. Address for author (d): Department of Applied Systems and Mathematics, Kanagawa University, 3-27-1 Rokkakubashi, Kanagawa-ku, Yokohama, 221-8686 Japan Address for author (e,f,g): Zhixing Building, Hubei University of Economics, No.8 Yangqiaohu Road, Canglongdao Development Park, Jiangxia District, 430205, Wuhan, Hubei Province, P. R. China. Address for author (h): Zhongjian Science and Technology Industrial Park, No. 799 Gaoxin Avenue, Hongshan District, 430074, Wuhan, China 3. The name of the corresponding author (i.e., the author to whom notices will be sent concerning the competition). (a) Jia Guo jia.guo.46@hosei.ac.jp 4. The abstract of the paper(s). Paper 1: Predicting carbon emissions is important in various sectors, including environmental management, economic planning, and energy policy. Traditional forecasting models typically require extensive training data to achieve high accuracy. However, carbon emission data are usually available on an annual basis, which is insufficient for effectively training conventional forecasting models. To address this challenge, this paper introduces an innovative carbon emissions prediction model that integrates Fibonacci attenuation particle swarm optimization (FAPSO) with the gated recurrent unit (GRU). The FAPSO algorithm is used to optimize the hyperparameters of the GRU, thereby alleviating the decline in prediction accuracy that conventional recurrent neural networks often face when dealing with limited training data. To evaluate the effectiveness of the FAPSO-GRU model, we tested it using carbon emission data from Hainan Province. Compared to the conventional GRU model, the FAPSO-GRU model achieved a significant reduction in the mean absolute error (42.27%), root mean square error (42.38%), and mean absolute percentage error (43.06%). Furthermore, we validated the FAPSO-GRU model with real data from Beijing, Guangdong, Hubei, Hunan, and Shanghai. The experimental results convincingly demonstrate that the proposed model provides a highly accurate solution for carbon emission prediction tasks, effectively addressing the limitations posed by limited training data. Paper 2: High-dimensional optimization presents a novel challenge within the realm of intelligent computing, necessitating innovative approaches. When tackling high-dimensional spaces, traditional evolutionary tools often encounter pitfalls, including dimensional catastrophes and a propensity to become trapped in local optima, ultimately compromising result accuracy. To address this issue, we introduce the Pair Barracuda Swarm Optimization (PBSO) algorithm in this paper. PBSO employs a unique strategy for constructing barracuda pairs, effectively mitigating the challenges posed by high dimensionality. Furthermore, we enhance global search capabilities by incorporating a support barracuda alongside the leading barracuda pair. To assess the algorithm’s performance, we conduct experiments utilizing the CEC2017 standard function and compare PBSO against five state-of-the-art natural-inspired optimizers in the control group. Across 29 test functions, PBSO consistently secures top rankings with 9 first-place, 13 second-place, 5 third-place, 1 fourth-place, and 1 fifth-place finishes, yielding an average rank of 2.0345. These empirical findings affirm that PBSO stands as the superior choice among all test algorithms, offering a dependable solution for high-dimensional optimization challenges. Paper 3: High-dimensional optimization has numerous potential applications in both academia and industry. It is a major challenge for optimization algorithms to generate very accurate solutions in high-dimensional search spaces. However, traditional search tools are prone to dimensional catastrophes and local optima, thus failing to provide high-precision results. To solve these problems, a novel hermit crab optimization algorithm (the HCOA) is introduced in this paper. Inspired by the group behaviour of hermit crabs, the HCOA combines the optimal search and historical path search to balance the depth and breadth searches. In the experimental section of the paper, the HCOA competes with 5 well-known metaheuristic algorithms in the CEC2017 benchmark functions, which contain 29 functions, with 23 of these ranking first. The state of work BPSO-CM is also chosen to compare with the HCOA, and the competition shows that the HCOA has a better performance in the 100-dimensional test of the CEC2017 benchmark functions. All the experimental results demonstrate that the HCOA presents highly accurate and robust results for high-dimensional optimization problems. 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, C, D, E, G) 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). (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. The combined results of FAPSO-GRU for carbon emission prediction, PBSO for high-dimensional optimization, and HCOA for enhanced optimization strategies demonstrate significant advancements that align with or surpass those previously accepted as new scientific results in peer-reviewed journals. Each model introduces novel methodologies that achieve substantial improvements in accuracy and performance, validated through rigorous testing and comparative analysis. These innovations and proven successes ensure that the results are on par with or exceed recent scientific contributions accepted by the academic community. (C) The result is equal to or better than a result that was placed into a database or archive of results maintained by an internationally recognized panel of scientific experts. The integrated approaches of FAPSO-GRU for carbon emission prediction, PBSO for high-dimensional optimization, and HCOA for enhanced optimization strategies present significant advancements that align with the standards of internationally recognized scientific panels. Each model demonstrates exceptional performance and substantial improvements over existing methods, validated through comprehensive testing across various datasets and benchmarks. These innovative and robust results meet or exceed the criteria set by expert panels, making them suitable for inclusion in prestigious scientific databases. (D) The result is publishable in its own right as a new scientific result independent of the fact that the result was mechanically created. The combined results of the three papers introduce significant and innovative methodologies—FAPSO-GRU for carbon emission prediction, PBSO for high-dimensional optimization, and HCOA for enhanced optimization strategies. Each model demonstrates superior performance and effectiveness, showcasing substantial scientific value and originality. The novel contributions and proven success of these approaches ensure they are worthy of independent publication as new scientific results, making them valuable contributions to their respective fields. (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. The integrated approach of the three papers—introducing FAPSO-GRU for carbon emission prediction, PBSO for high-dimensional optimization, and HCOA for enhanced optimization strategies—demonstrates significant advancements over the latest human-created solutions in their respective fields. Each model achieves superior performance metrics and robustness, collectively setting new benchmarks in prediction accuracy and optimization efficiency. This unified contribution surpasses the most recent and advanced human-created algorithms, addressing long-standing challenges with innovative and effective methodologies. (G) The result solves a problem of indisputable difficulty in its field. The combined results of FAPSO-GRU for carbon emission prediction, PBSO for high-dimensional optimization, and HCOA for enhanced optimization strategies address and solve problems of indisputable difficulty in their respective fields. Each model demonstrates significant advancements, such as improved prediction accuracy for carbon emissions, effective mitigation of high-dimensional optimization challenges, and robust solutions for optimization problems. These innovations highlight their ability to tackle and solve complex and long-standing issues, showcasing their significant impact and importance. 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). Paper 1: Guo, J.; Li, J.; Sato, Y.; Yan, Z. A Gated Recurrent Unit Model with Fibonacci Attenuation Particle Swarm Optimization for Carbon Emission Prediction. Processes 2024, 12, 1063. https://doi.org/10.3390/pr12061063 Paper 2: Guo, J., Zhou, G., Yan, K. et al. Pair barracuda swarm optimization algorithm: a natural-inspired metaheuristic method for high dimensional optimization problems. Sci Rep 13, 18314 (2023). https://doi.org/10.1038/s41598-023-43748-w Paper 3 Guo, J., Zhou, G., Yan, K. et al. A novel hermit crab optimization algorithm. Sci Rep 13, 9934 (2023). https://doi.org/10.1038/s41598-023-37129-6 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. All prize money, if any, will be allocated to the corresponding author Jia Guo. All authors agree. 9. A statement stating why the authors expect that their entry would be the "best". The authors expect their entry to be the "best" due to the innovative and groundbreaking methodologies introduced throughout the entire process from theory to application. PBSO and HCOA, inspired by natural phenomena, propose new high-precision optimization strategies, which in turn inspired the development of the FAPSO-GRU for carbon emission prediction. These combined approaches address some of the most complex and long-standing challenges in their respective fields. Each model not only demonstrates significant theoretical advancements but also validates its accuracy, performance, and robustness through comprehensive testing and comparative analysis in practical applications. These results not only surpass existing methods but also set new benchmarks, showcasing the authors' exceptional contributions to solving well-recognized problems. The successful translation from nature-inspired theoretical innovations to practical applications ensures that this entry stands out as a leading scientific achievement, deserving recognition as the "best" in the competition. 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. ES (evolution strategies) 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. Paper 1: 22 May 2024 Paper 2: 25 October 2023 Paper 3: 19 June 2023