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A novel grey seasonal model with time power for energy prediction

EXPERT SYSTEMS WITH APPLICATIONS 2025年 259卷

作者： Zhou, Weijie Chang, Jiaxin Jiang, Huimin Ding, Song Jiang, Rongrong Guo, Xupeng Changzhou Univ Sch Wu Jinglian Econ Changzhou 213159 Peoples R China Changzhou Univ Sch Business Changzhou 213159 Peoples R China Zhejiang Univ Finance & Econ Sch Econ Hangzhou 310018 Peoples R China Changzhou Univ Jiangsu Energy Strategy Res Base Changzhou 213159 Peoples R China Zhejiang Inst Eight Eight Strategies Hangzhou 310018 Peoples R China Hangzhou Normal Univ Off Leading Grp Digital Reform Hangzhou 311121 Peoples R China

The fluctuation of seasonal time series has attracted considerable attention. However, the complexity of socioeconomic development and the variability of influencing factors make accurate predictions of seasonal time series more difficult. A new discrete grey seasonal model, namely DGSTPM(1,1), is established, which employs the time power term based on the cultural algorithm to effectively deal with the time sequences characterized by nonlinear tendency and periodic fluctuations. Firstly, the operating mechanism of the DGSTPM(1,1) model is expounded by combining the time power item with the traditional grey seasonal model to enhance the forecasting capability. Then, the optimal time power index of the model can be determined by incorporating the cultural algorithm. Subsequently, several properties are thoroughly discussed to illustrate the superiority and uniformity of the new model. Finally, two energy-related cases with different data features and sample sizes are applied to identify the efficacy and robustness of the DGSTPM(1,1) model, referring to monthly crude oil production in the United States and quarterly coke production in China. Based on the various error criteria, SPA and DM tests, the results indicate that the DGSTPM(1,1) outperforms its competitors both in simulation and prediction phases, including DGSTM(1,1), SGM(1,1), SDTGM, FTGM, SARIMA, LSSVM, BPNN, and LSTM models. Meanwhile, the new model can effectively address seasonal time series with nonlinear characteristics owing to its dynamic time power item with the support of the cultural algorithm.

关键词： Grey prediction model Seasonal time series Time power item cultural algorithm Energy prediction

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Prediction of coefficient of discharge (CPK) of A-type piano key weir using ANN and hybrid ANFIS models

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MODELING EARTH SYSTEMS AND ENVIRONMENT 2025年第3期11卷 1-27页

作者： Alam, Javed Muzzammil, Mohd Raza, Md Atif Aligarh Muslim Univ Aligarh India

Piano key weirs are a special type of labyrinth weir and have a higher discharge capacity than linear weirs. Accurate prediction of the coefficient of discharge (CPK) of Piano Key (PK) weirs is essential for their effective design, planning, and optimization of their hydraulic performance. Various regression models available in the literature, based on limited experimental data sets, often may not be able to capture the complex nonlinear relationships governing CPK. To address these limitations, the present study utilizes soft computing techniques, specifically Artificial Neural Networks (ANN), the Adaptive Neuro-Fuzzy Inference System (ANFIS), and two hybrid ANFIS modeling techniques to develop accurate and effective predictive models. This study utilizes extensive data sets available in the literature to model CPK under free-flow conditions. Relative crest length (L/W), relative head (H/P), inlet and outlet key width ratio (Wi/Wo), relative weir height (P/Wu), and relative weir width (B/P) are used as predictor variables. A non-linear regression-based model was developed for comparison purposes. ANN models such as Feed-forward back propagation (FFBP), radial basis function (RBF), and generalized regression neural network (GRNN), along with a traditional ANFIS model, were developed. Additionally, two hybrid ANFIS models were designed and optimized using biogeography-based optimization (ANFIS-BBO) and the cultural algorithm (ANFIS-CA). Standard statistical metrics, such as the coefficient of determination (R2), mean percentage error (MPE), mean absolute deviation (MAD), and root mean square error (RMSE), mean absolute relative error (MARE) and percent bias (PBIAS) were used to evaluate the predictive performance of the models. The results indicated that the ANN and ANFIS models outperformed the traditional regression model in predicting CPK, whereas Hybrid ANFIS models producing the highest predicting accuracy. Among ANN models, the FFBP model produced superior pred

关键词： ANN ANFIS Biogeography-based optimization cultural algorithm Coefficient of discharge Piano key weir Soft computing

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Improved particle filter algorithm combined with culture algorithm for collision Caenorhabditis elegans tracking

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SCIENTIFIC REPORTS 2025年第1期15卷 1-14页

作者： Yu, Taoyuan Xu, Xiping Li, Yuanpeng Zhang, Ning Zhang, Naiyu Wang, Xiaohui Changchun Univ Sci & Technol Sch Optoelect Engn Changchun Jilin Peoples R China Chinese Acad Sci Lab Chem Biol Changchun Inst Appl Chem Changchun 130022 Jilin Peoples R China Univ Sci & Technol China Sch Appl Chem & Engn Hefei 230026 Anhui Peoples R China

In order to address the issue of tracking errors of collision Caenorhabditis elegans, this research proposes an improved particle filter tracking method integrated with cultural algorithm. The particle filter algorithm is enhanced through the integration of the sine cosine algorithm, thereby facilitating uninterrupted tracking of the target C. elegans. Furthermore, the cultural algorithm is employed to facilitate recognition of the target C. elegans following a collision. In addition, this method integrates the concepts of down-sample and marking to reduce the average processing time of the image. Ultimately, the experiment was conducted on two strains of C. elegans of six ages. The experimental results demonstrate that the proposed method can accurately identify the target worm in the post-collision stage. The proposed method has the potential to be utilized in the field of worm tracking, offering a novel method into the acquisition of collision C. elegans behavior.

关键词： Collision C. Elegans tracking Particle filter Sine cosine algorithm cultural algorithm Visual object tracking

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Electromagnetic Optimization Using a cultural Self-Organizing Migrating algorithm Approach Based on Normative Knowledge

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IEEE TRANSACTIONS ON MAGNETICS 2009年第3期45卷 1446-1449页

作者： Coelho, Leandro dos Santos Alotto, Piergiorgio Univ Padua Dipartimento Ingn Elettr I-35131 Padua Italy Pontif Catholic Univ Parana Automat & Syst Lab BR-80215901 Curitiba Parana Brazil

A new class of stochastic optimization algorithms called the self-organizing migrating algorithm (SOMA) has recently been proposed. SOMA works on a population of potential solutions called specimen and is based on the self-organizing behavior of groups of individuals in a "social environment." This paper introduces a modified SOMA approach based on an operator featuring normative knowledge, a characteristic of cultural algorithms. The efficiency of the proposed method is tested on Loney's solenoid design.

关键词： cultural algorithm electromagnetic optimization Loney's solenoid design self-organizing migrating algorithm (SOMA)

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A novel class of niche hybrid cultural algorithms for continuous engineering optimization

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INFORMATION SCIENCES 2014年 267卷 158-190页

作者： Ali, Mostafa Z. Awad, Noor H. Jordan Univ Sci & Technol Irbid 22110 Jordan

This paper proposes a novel class of niche hybrid cultural algorithms for solving engineering problems with continuous design variables. The first algorithm, Niche cultural algorithm (NCA), embeds niching within the cultural framework to maintain multiple groups within the population of agents in order to locate multiple optima. The second algorithm, hybridizes niche cultural algorithms with Tabu search (H-NCA). This technique offers a novel architecture of hybrid approaches, which combines Niche cultural algorithms (NCA) with Tabu search (TS). The proposed hybridization scheme enables the algorithm to overleap local optima and improve performance. The third algorithm, Improved Hybrid Niche cultural algorithms (IH-NCA), is employed to enhance convergence rate and accuracy of H-NCA with fewer computations. In IH-NCA, the algorithm switches between two selection strategies based on roulette wheel and stochastic tournament selection. This enhances the algorithm's ability to further escape stagnation and premature convergence with varying stochastic noise and selection pressure. Simulations were performed over miscellaneous engineering optimization problems that include minimization of constrained functions and structural engineering optimization. A comparative study is carried out with other state-of-the-art optimization techniques. The findings affirm the efficiency and robustness of the new methodologies over the other existing relevant approaches. (C) 2014 Elsevier Inc. All rights reserved.

关键词： cultural algorithm Niche Hybrid algorithm Multimodal optimization

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cultural coalitions detection approach using GPU based on hybrid Bat and cultural algorithms

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APPLIED SOFT COMPUTING 2020年 93卷 106368-106368页

作者： Kechid, Amine Drias, Habiba USTHB Artificial Intelligence Res Lab Dept Comp Sci BP 32 El Alia Bab Ezzouar Algiers 16111 Algeria

Currently, robot technology plays a crucial role in our modern life. Its great favor over humanity was not limited to the industrial domain, but also on the social one. Social robots become equipped with an artificial culture that allows them to interact with humans. Although the great importance of this technology, some misdeeds can create a real catastrophe. Coalition creation is one of its most dangerous troubles, where a subset of robots cooperates to impose pernicious decisions. There are efficient coalitions detection methods, but their exponential computation time makes their use limited only to small data. This paper is an extended version of Kechid and Drias (2019) presented at the IEA/AIE 2019 conference. In this paper, we propose a cultural coalitions detection approach using GPU based on hybrid Bat and cultural algorithms. Unlike the existing literature, We view the problem of finding coalitions as an optimization problem to get relevant solutions in a significantly reduced amount of time. The proposed approach can increase the population diversity and improve the searching ability for an optimal exploration-exploitation balance. Also, it can launch several cultural bats in GPU to make real parallelism. Experimental results on several datasets show that the proposed method will considerably reduce the runtime. These datasets represent the result of artificial cultural agents playing the colored trails (CT) game. Concerning the creation of profiles, we use real datasets generated based on the World Values survey. (C) 2020 Elsevier B.V. All rights reserved.

关键词： GPU Parallel approaches Hybrid approaches cultural algorithm Bat optimization Culture Coalitions Social agents

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cultural quantum-inspired shuffled frog leaping algorithm for direction finding of non-circular signals

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INTERNATIONAL JOURNAL OF COMPUTING SCIENCE AND MATHEMATICS 2013年第4期4卷 321-331页

作者： Gao, Hongyuan Xu, Congqiang Harbin Engn Univ Coll Informat & Commun Engn 145Nantong St Harbin 150001 Heilongjiang Peoples R China

In order to resolve complex continuous optimisation problem, a cultural quantum-inspired shuffled frog leaping (CQSFL) algorithm is proposed. The proposed CQSFL applies the quantum knowledge strategy and new quantum leaping equations to shuffled frog leaping algorithm, and thus has the advantages of low computational complexity and fast convergence. As a key step of CQSFL algorithm, leaping movement is modelled as guided cultural behaviour and thus may improve the capability of SFLA to find the optimal solution. Then we applied the proposed CQSFL algorithm in direction finding problem of non-circular signals, which is a hot spot in domain of communication. Then, based on CQSFL algorithm and non-circular maximum likelihood (NML) algorithm, a new direction finding method is proposed, which is called CQSFL-NML algorithm. Monte-Carlo simulations have proved that the CQSFL-NML method has good performance for non-coherent and coherent non-circular signals.

关键词： shuffled frog leaping algorithm cultural algorithm direction finding quantum computation non-circular signals

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An integrated cultural particle swarm algorithm for multi-objective reliability-based design optimization

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE 2014年第7期228卷 1185-1196页

作者： Li, Zhongkai Tian, Guangdong Cheng, Gang Liu, Houguang Cheng, Zhihong China Univ Min & Technol Sch Mechatron Engn Xuzhou 221116 Peoples R China Northeast Forestry Univ Transportat Coll Harbin Peoples R China

Uncertainties in design variables and problem parameters are often inevitable in multi-objective optimizations, and they must be considered in an optimization task if reliable Pareto optimal solutions are to be sought. Multi-objective reliability-based design optimization has been raised as a question in design for reliability, but the disadvantages of fixed evolutionary parameters, nonuniformly distributed Pareto optimal solutions and high computational cost hinder engineering applications of reliability-based design. To deal with it, this work proposes an integrated multi-objective cultural-based particle swarm algorithm to solve the double-loop reliability-based design optimization. In the inner optimization loop, the cultural space is composed of the elitism, situational and normative knowledge to adjust the parameters for swarm space, and the crowding distance ranking is introduced to update the global and local optimum and control the maximum number of solutions in elitism knowledge. The hybrid mean value method is improved to perform reliability analysis in the outer loop to suit both concave and convex types of performance functions. In addition, the car side-impact and the injection molding machine are chosen as multi-objective reliability design examples to demonstrate the effectiveness of the proposed approach. Simultaneously, results of car side-impact problem are compared with two traditional multi-objective reliability optimization algorithms, i.e., nondominated sorting genetic algorithm and crowding distance ranking-based multi-objective particle swarm optimizer, to assess the efficiency of the proposed approach. The results denote the proposed cultural-based multi-objective particle swarm optimizer is effective and feasible to solve the reliability-based design optimization problems.

关键词： reliability analysis cultural algorithm Reliability-based design optimization multi-objective evolutionary algorithm particle swarm optimization

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A social intelligent system for multi-objective optimization of classification rules using cultural algorithms

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COMPUTING 2013年第4期95卷 327-350页

作者： Srinivasan, Sujatha Ramakrishnan, Sivakumar Cauvery Coll Women Dept Comp Sci Tiruchirappalli Tamil Nadu India AVVM Sri Pushpam Coll Dept Comp Sci Poondi Tamil Nadu India

cultural algorithms (CA) use social intelligence to solve problems in optimization. The CA is a class of evolutionary computational models inspired from observing the cultural evolutionary process in nature. cultural algorithms employ a basic set of knowledge sources, each related to knowledge observed in various animal species. Knowledge from these sources is then combined to influence the decisions of the individual agents in solving problems. Classification using "IF-THEN" rules comes under descriptive knowledge discovery in data mining and is the most sought out by users since they represent highly comprehensible form of knowledge. The rules have certain properties which make them useful forms of actionable knowledge to the users. The rules are evaluated using these properties represented as objective and subjective measures. The rule properties may be conflicting. Hence discovery of rules with specific properties is considered as a multi-objective optimization problem. In the current study an extended cultural algorithm which applies social intelligence in the data mining domain to present users with a set of rules optimized according to user specified metrics is proposed. Preliminary experimental results using benchmark data sets reveal that the algorithm is promising in producing rules with specific properties.

关键词： Multi-objective optimization Classification rules Evolutionary algorithms Social intelligence cultural algorithm Data mining

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A cultural Immune Quantum Evolutionary algorithm and Its Application

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JOURNAL OF COMPUTERS 2013年第1期8卷 163-169页

作者： Liu, Sheng You, Xiaoming Wu, Zhong Shanghai Univ Engn Sci Sch Management Shanghai Peoples R China

This paper proposes to integrate immune quantum evolutionary algorithm into cultural algorithms frame to develop a more efficient cultural immune quantum evolutionary algorithm (CIQEA). The computing model consists of a population space based on quantum-inspired evolutionary algorithm and a belief space based on immune vaccination, the population space and belief space have their own population evolved independently and parallel. The population space contributes vaccines to the belief population periodically, and the belief space continually evolve these vaccines which are used to provide better evolutionary direction to the population space, to constitute the dual-evolution and dual-improvement mechanism that can improve the global optimization abilities and convergence speed greatly. The convergence of CIQEA is proved based on markov random process and its superiority is shown by the experimental results on the knapsack problem.

关键词： quantum bit quantum-inspired evolutionary algorithm(QEA) immune operator cultural algorithm knapsack problems

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