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optimization of renewable energy-based transmission system: an application of the genetic, ant colony optimization, Q-learning, and Dijkstra's algorithms

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INTERNATIONAL JOURNAL OF MODELLING AND SIMULATION 2024年第6期44卷 386-405页

作者： Ansari, Sam Bansal, Ramesh C. C. Elsahar, Yasmin Univ Sharjah Dept Elect Engn Sharjah U Arab Emirates Univ Pretoria Dept Elect Elect & Comp Engn Pretoria South Africa Loughborough Univ Wolfson Sch Mech Elect & Mfg Engn Loughborough Leics England

Losses in the electrical power transmission and distribution systems are considered two of the most critical challenges in power grids. Reducing the related losses plays a significant role in increasing system efficiency in addition to diminishing costs. Therefore, optimum power transfer as well as finding a convenient route, are essential factors in electrical grids. This paper intends to substantially reduce the transmission/distribution-related losses by finding the shortest and most optimal path between the renewable energy power plant (producer) and the substations/consumers. A genetic algorithm (GA) is proposed for optimal routing to increase the system's reliability and minimize the losses of the entire network. In this work, by presenting a coding with chromosomes of variable length and considering the construction costs and the power transmission line/path as the fitness function, the appropriate route is obtained. The efficiency of the proposed method is compared with Dijkstra's algorithm, one of the conventional graph search approaches. The ant colony optimization (ACO) algorithm and a reinforcement learning algorithm, namely the Q-learning model, are employed to further explore the optimization efficiency of the proposed renewable energy-based transmission system. The simulation results demonstrate that the proposed models accurately determine the optimal pathway within an excellent time.

关键词： ant colony optimization algorithm Dijkstra's algorithm efficient power transmission genetic algorithm optimization reinforcement learning algorithm renewable energy

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Energy-efficient task allocation for reliable parallel computation of cluster-based wireless sensor network in edge computing

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Digital Communications and Networks 2023年第2期9卷 473-482页

作者： Jiabao Wen Jiachen Yang Tianying Wang Yang Li Zhihan Lv School of Electrical and Information Engineering Tianjin UniversityTianjin300072China College of Mechanical and Electrical Engineering Shihezi UniversityXinjiang832003China School of Data Science and Software Engineering Qingdao UniversityQingdao266000China

To efficiently complete a complex computation task,the complex task should be decomposed into subcomputation tasks that run parallel in edge *** Sensor Network(WSN)is a typical application of parallel *** achieve highly reliable parallel computation for wireless sensor network,the network's lifetime needs to be ***,a proper task allocation strategy is needed to reduce the energy consumption and balance the load of the *** paper proposes a task model and a cluster-based WSN model in edge *** our model,different tasks require different types of resources and different sensors provide different types of resources,so our model is heterogeneous,which makes the model more *** we propose a task allocation algorithm that combines the Genetic algorithm(GA)and the ant colony optimization(ACO)*** algorithm concentrates on energy conservation and load balancing so that the lifetime of the network can be *** experimental result shows the algorithm's effectiveness and advantages in energy conservation and load balancing.

关键词： Wireless sensor network Parallel computation Task allocation Genetic algorithm ant colony optimization algorithm Energy-efficient Load balancing

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Improved ant colony algorithm for path planning of mobile robots based on compound prediction mechanism

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2023年第2期44卷 2147-2162页

作者： Liu, Zhongpu Liu, Jianjuan Henan Univ Technol Sch Elect Engn Inst Mech & Elect Equipment & Measurement & Contr Zhengzhou Peoples R China

For the issues of the ant colony algorithm (ACO) to solving the problems in mobile robot path planning, such as the slowoptimization speed and the redundant paths in planning results, a high-precision improved ant colony algorithm (IPACO) with fast optimization and compound prediction mechanism is proposed. Firstly, aiming at maximizing the possibility of optimal node selection in the process of path planning, a composite optimal node prediction model is introduced to improve the state transition function. Secondly, a pheromone model with initialize the distribution and "reward or punishment" update mechanism is used to updates the global pheromone concentration directionally, which increases the pheromone concentration of excellent path nodes and the heuristic effect;Finally, a prediction-backward mechanism to deal with the "deadlock" problem in the ant colony search process is adopted in the IPACO algorithm, which enhance the success rate in the ACO algorithm path planning. Five groups of different environments are selected to compare and verify the performance of IPACO algorithm, ACO algorithm and three typical path planning algorithms. The experimental simulation results show that, compared with the ACO algorithm, the convergence speed and the planning path accuracy of the IPACO algorithm are improved by 57.69% and 12.86% respectively, and the convergence speed and the planning path accuracy are significantly improved;the optimal path length, optimization speed and stability of the IPACO algorithm are improved. Which verifies that the IPACO algorithm can effectively improve the environmental compatibility and stability of the ant colony algorithm path planning, and the effect is significantly improved.

关键词： Mobile robot Path planning Path prediction model ant colony optimization algorithm Reward and punishment update

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Mcaaco: a multi-objective strategy heuristic search algorithm for solving capacitated vehicle routing problems

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COMPLEX & INTELLIGENT SYSTEMS 2025年第5期11卷 1-21页

作者： Chen, Yanling Wei, Jingyi Luo, Tao Zhou, Jie Shihezi Univ Dept Informat Sci & Technol North 4th Rd Shihezi 832000 Xinjiang Peoples R China

Vehicle routing is a critical issue in the logistics and distribution industry. In practical applications, optimizing vehicle capacity allocation can significantly improve route optimization performance and service coverage. However, solving this problem remains challenging due to the complex constraints involved. Therefore, to address this real-world challenge, a novel intelligent optimization method, multi-objective capacity adjustment ant colony optimization algorithm (MCAACO), is proposed, which integrates advanced multi-objective optimization strategies, including capacity adjustment operators and crossover operators. Combined with pheromone updating and Pareto front-end optimization, the method effectively resolves the conflict between vehicle capacity constraints and multi-objective optimization. To further enhance the algorithm's performance, dynamic pheromone updating mechanisms and elite individual retention strategies are proposed. Additionally, an adaptive parameter adjustment strategy is designed to balance global search and local exploitation capabilities. Through a series of experiments, it is demonstrated that compared to multi-objective particle swarm optimization (MOPSO), non-dominated sorting genetic algorithm II (NSGA-II), and multi-objective sparrow search algorithm (MOSSA), the proposed MCAACO significantly reduces travel paths by an average of 3.05% and increases vehicle service coverage by an average of 3.2%, while satisfying vehicle capacity constraints. Experimental indicators demonstrate that the breakthrough algorithm significantly addresses the issues of high costs and low efficiency prevalent in the practical logistics distribution industry.

关键词： Capacitated vehicle routing planning (CVRP) ant colony optimization algorithm Crossover operator Capacity adjustment operator Multi-objective optimization

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VS-TACO: A Tuned Version of ant colony optimization for Generating Variable Strength Interaction in T-Way Testing Strategy 2022

VS-TACO: A Tuned Version of Ant Colony Optimization for Gene...

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11th International Conference on Software and Computer Applications, ICSCA 2022

作者： Zahir Ahmad, Mohd Zamri Othman, Rozmie Razif Ramli, Nuraminah Rashid Ali, Mohd Shaiful Aziz Faculty of Electronic Engineering Technology Universiti Malaysia Perlis Arau Perlis02600 Malaysia

ISBN: (纸本)9781450385770

Ever since, software technologies have been through a rapid evolution. In a real application, the interaction between input variables may vary, thus the exhaustive testing is no longer practical since it is time-consuming and lead to combinatorial explosion. One of the strategies that able to cater fault due to the interaction is ant colony optimization (ACO) algorithm. Typically, amount of ants in the ACO algorithm is fixed at certain number while the search space technique (i.e. to explore or exploit new possible solutions) is randomized for each iteration in the entire algorithm, are potentially affect the optimization's efficiency. Thus this paper proposes a new variant of ACO algorithm called as a tuned version of ACO for generating variable strength interaction in t-way testing strategy (VS-TACO). VS-TACO applied a Mamdani fuzzy logic in order to dynamically choose the number of ant and decide which search space technique to be used. Experiments that have been conducted on VS-TACO and benchmarked with other strategies, shows VS-TACO produce a competitive result in term of test suite size. © 2022 ACM.

关键词： t-way testing ant colony optimization algorithm variable strength interaction testing

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Uncertain Multi-Objective Hazardous Materials Transport Route Planning Considering Resilience and Low-Carbon

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IEEE ACCESS 2023年 11卷 26921-26931页

作者： Wang, Zhanzhong Wang, Yan Jiao, Yuling Jilin Univ Transportat Coll Changchun 130022 Peoples R China

Hazardous material transport accidents are events with a low probability and high consequence risk. With an increase in the proportion of hazardous materials transported on domestic roads, an increasing number of scholars have begun to study this field. In this study, a multi-objective hazardous materials transport route planning model considering road traffic resilience and low carbon, which considers the uncertainty of demand and time and is under the limit of the time window. It transports many types of hazardous materials from multiple suppliers to multiple retails with three goals (transportation cost, risk, and carbon emission). This model fills the gap in the research on hazardous material transportation in the field of low carbon, and this is the first time that road traffic resilience is considered in the transport of hazardous materials as one of the weight factors of risk calculation. We designed a improved ant colony optimization algorithm (ACO) to obtain the pareto optimal solution set. We compared the improved ACO with genetic algorithm and simulated annealing algorithm. The results show that the improved ACO has better solution quality and space, which verifies the validity and reliability of the improved ACO.

关键词： Hazardous materials Road traffic Resilience Transportation Path planning Carbon dioxide Accidents Routing Hazardous materials transportation uncertainty theory ant colony optimization algorithm resilience multi-objective

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Integrated fault location method for distribution networks based on IACO-PS

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JOURNAL OF POWER ELECTRONICS 2023年第1期23卷 112-126页

作者： Zhang, Shuqing Zhang, Xiaowen Jiang, Anqi Zhang, Liguo Li, Mingliang Yanshan Univ Inst Elect Engn Qinhuangdao Hebei Peoples R China

This paper develops a new hybrid method based on an improved ant colony optimization algorithm that incorporates pattern search (IACO-PS) for determining the location of faults in a distribution network. The performance of the conventional ant colony optimization (ACO) algorithm is improved using the opposite-based learning strategy to generate the initial population and adding a weight coefficient into the pheromone update mechanism to dynamically adjust the pheromone volatilization factor. The hybrid IACO-PS algorithm combines the individual strengths of ACO and PS. In addition, the fitness function is constructed by counting the false and missing fault information into the fault variable. In optimizing benchmark function experiments, the proposed hybrid IACO-PS presents a superior performance when compared to other improved versions of ACO. The effectiveness of the proposed approach is corroborated by tests performed on an IEEE 134-bus network. Simulation results show that the proposed hybrid IACO-PS method can determine the location of a fault even in the presence of fault distortion. In addition, it is immune to noise and data loss errors. Finally, the method proposed in this paper significantly outperforms other published fault location methods, and it can accurately locate faults and identify the type of distortion.

关键词： Fault location Distribution network ant colony optimization algorithm Pattern search

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ACOA-optimized microstrip gysel power divider with semi-radial-shaped resonators for broadband applications

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MULTISCALE AND MULTIDISCIPLINARY MODELING EXPERIMENTS AND DESIGN 2024年第6期7卷 5203-5216页

作者： Aljeboori, Zainab Wafi Jawad Malekshahi, Mazdak Rad Razi Univ Fac Engn Dept Elect Engn Kermanshah 67149 Iran

In this article, a microstrip Gysel power divider (MGPD) based on the ant colony optimization algorithm (ACOA) has been designed, simulated, and fabricated with the help of novel semi-radial-shaped resonators (SRSRs). In addition, attenuators and open-end stubs have been incorporated to generate a broad cut-off band and diminish unwanted harmonics. The implemented circuit boasts a central frequency of 1.24 GHz. The L and C parameters of the equivalent circuit of the used resonators have been predicted with the assistance of the ACOA method. The subsequent phase was the fabrication of this MGPD, after which its performance was evaluated in light of the results acquired from the simulation. It was discovered that there was a high degree of concordance between the two. Conversely, the fabricated circuit offers several advantages, such as a suitable S12 of - 3.08 dB, a high return loss of less than 20 dB at the working frequency, a compact size of 0.12 lambda g\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\lambda }_{g}$$\end{document} by 0.37 lambda g\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\lambda }_{g}$$\end{document}, a high fractional bandwidth (FBW) of 125%, and the ability to eliminate undesired harmonics. The circuit effectively suppresses unwanted harmonics, up to the 19th harmonic, ensuring strong responsiveness in the passband with minimal ripple. Therefore, a wide variety of electronic devices, including radar transmitter and receiver circuits, computer network hardware, electronic labs, and many other high-frequency systems, can utilize the described circuit.

关键词： Semi-radial-shaped resonators Gysel power divider ant colony optimization algorithm Open-ended stubs

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A novel WO-ant: whale-ant optimization algorithm for detection of Parkinson’s disease

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International Journal of Information Technology (Singapore) 2025年第3期17卷 1873-1881页

作者： Singh, Neha Sinha, Sapna Singh, Laxman Amity Institute of Information Technology Amity University Uttar Pradesh Noida India Department of Computer Science and Engineering (AI-ML) KIET Group of Institutions U.P Ghaziabad India

Parkinson’s disease (PD) is a neurological condition that impacts the quality of life for millions of people all over the world. A prompt and precise diagnosis of PD is absolutely necessary for the successful treatment and management of the condition. For the detection of PD, numerous methods exist in the literature, including speech-based methods, gait-based methods, and handwriting-based methods. Compared to the other two methods, the speech-based method is regarded as the most effective and competitive. The Whale-ant optimization algorithm (WO-ant) is a novel swarm optimization strategy that is presented in this paper as a potential method for the detection of PD via feature selection. To help in early detection, the proposed method attempts to enhance classification accuracy of PD detection by selecting an optimum subset of features from a wide feature space. In this paper, authors evaluate the classification efficacy of WO-ant for PD using a speech-based method. MATLAB is used to implement the proposed method on the PD dataset obtained from ***. The efficacy of the algorithm is evaluated based on the optimal subset of features, accuracy, and computational cost. WO-ant accurately reduced the number of features from 23 to 4 giving an accuracy of 90.29%. According to the numerical results, the algorithm performed well in terms of accuracy and selection of the optimal number of features. © Bharati Vidyapeeth's Institute of Computer Applications and Management 2024.

关键词： ant colony optimization algorithm Feature selection Parkinson’s disease Whale optimization algorithm Whale-ant optimization algorithm

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Comparative Analysis of Different Prediction Models of Inorganic Fertilizers N, P, and K Under Different Soil Fertility Conditions 36

Comparative Analysis of Different Prediction Models of Inorg...

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36th Chinese Control and Decision Conference (CCDC)

作者： Luo, Yuzhu Yu, Xiaoyu Zhou, Yue Bai, Bing Dong, Huan Peng, Xiuyuan Liaoning Acad Agr Sci Inst Informat Shenyang Peoples R China Liaoning Acad Agr Sci Inst Plant Nutr & Environm Resources Shenyang Peoples R China

ISBN: (纸本)9798350387780;9798350387797

Nutrient management is a key measure to achieve the target crop yield. Choosing appropriate predictive models and adjusting nutrient supply according to actual conditions can effectively reduce nutrient loss, improve crop yield and product quality. Therefore, we conducted research on the prediction models of inorganic fertilizers N, P, and K in greenhouse tomatoes based on the target yield. In this paper, we analyzed four different neural network prediction models: the neural network prediction model based on ant, colony optimization algorithm, the neural network prediction model based on sparrow search algorithm, neural network prediction model based on genetic algorithm and neural network prediction model based on particle swarm optimization algorithm, and compared and analyzed the prediction results of inorganic fertilizer N, P and K under different soil fertility conditions to understand which neural network prediction model will produce the best prediction effect. The simulation results showed that under medium soil fertility, the neural network prediction model based on genetic algorithm had the best prediction effect of inorganic fertilizer N, P, and K. The verification results showed that its mean square error (MSE) and coefficient of determination (R-2) were the best, which were 0.0031 and 0.8200 respectively. However, under low soil fertility and high soil fertility, the neural network prediction model based on the sparrow search algorithm had the best prediction performance, and its MSE and R-2 were the best.

关键词： ant colony optimization algorithm sparrow search algorithm genetic algorithm particle swarm optimization algorithm inorganic fertilizer prediction

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