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18th International Conference on Intelligent Computing (ICIC)

作者： Yu, Yixuan Zhou, Yongquan Luo, Qifang Wei, Xiuxi Guangxi Univ Nationalities Coll Elect Informat Nanning 530006 Peoples R China Guangxi High Sch Key Lab Complex Syst & Computat Nanning 530006 Peoples R China

ISBN: (纸本)9783031138324;9783031138317

Planar graph coloring is a classic NP - hard problem. So far there is no completely effective method to solve this problem. This paper presents a discrete artificial electric field algorithm to solve the graph coloring problem. The algorithm first codes according to the graph coloring problem to meet the requirements of the graph coloring problem, and then adds part of local search to improve the performance of the algorithm. The algorithm can effectively and accurately solve the coloring problem of plane graphs. Compared with several classical algorithms, the results show that the proposed algorithm has a smaller average number of iterations and a higher success rate in dealing with graph coloring problems, and it can also find the correct coloring scheme in real map problems.

关键词： artificial electric field algorithm Discrete artificial electric field algorithm Graph coloring problem Metaheuristic optimization

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A novel artificial electric field strategy for economic load dispatch problem with renewable penetration

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EVOLUTIONARY INTELLIGENCE 2024年第5-6期17卷 3593-3608页

作者： Verma, Diwakar Soni, Jatin Bhattacharjee, Kuntal Nirma Univ Inst Technol Ahmadabad 382481 Gujarat India

This article presents an innovative method to address the economic load dispatch (ELD) problem in power systems incorporating renewable energy sources within thermal units. Employing the 2-m point estimation technique for determining renewable energy output power, the proposed approach effectively addresses challenges associated with renewable-based ELD by utilizing the artificial electric field method. Inspired by the electrostatic force principle among charged particles, this approach guides particles toward optimal solutions within the search space. Validation on power systems featuring 3, 5, 6, 15, and 40 units demonstrates superior performance compared to established algorithms, confirmed by the Wilcoxon signed-rank test. The research contributes to the advancement of sustainable and efficient power systems.

关键词： artificial electric field algorithm Economic load dispatch Valve point effect 2-m point estimation method

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Image contrast improvement through a metaheuristic scheme

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SOFT COMPUTING 2023年第18期27卷 13657-13676页

作者： Mukhopadhyay, Souradeep Hossain, Sabbir Malakar, Samir Cuevas, Erik Sarkar, Ram Jadavpur Univ Dept Comp Sci & Engn Kolkata India Asutosh Coll Dept Comp Sci Kolkata India Univ Guadalajara Dept Elect Guadalajara Mexico

Contrast enhancement is an important pre-processing task for several image and video processing applications. The objective of a contrast enhancement method is to improve the quality of the visual information contained in the images for further processing. Due to the enormous challenges, it is still considered as an open research problem. Several approaches have been proposed in the literature based on spatial and fr uency domain techniques. Among them, the Incomplete Beta Function (IBF) is a popular scheme used for image contrast enhancement. In the IBF based image contrast enhancement technique, quality of an image is improved by two controlling parameters. Under such conditions, these parameters need to be tuned for obtaining better outcomes. In this paper, a new gray-scale contrast enhancement algorithm is introduced where, instead of tuning the controlling parameters of IBF experimentally, their near-optimal values are calculated with the help of a recently published meta-heuristic algorithm called artificial electric field algorithm (AEFA). The proposed method has been compared with many state-of-the-art techniques in terms of some standard metrics considering three different datasets, namely Kodak, MIT-Adobe FiveK and USC-SIPI. Simulation results demonstrate that the proposed AEFA based image enhancement technique increases the overall image contrast and enriches the information present in the image.

关键词： Contrast enhancement Incomplete beta function Meta-heuristic artificial electric field algorithm Kodak dataset MIT-Adobe FiveK dataset USC-SIPI dataset

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An Efficient Clustering algorithm for Mixed Dataset of Postoperative Surgical Records

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INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS 2020年第1期13卷 757-770页

作者： Petwal, Hemant Rani, Rinkle Thapar Inst Engn & Technol Dept Comp Sci & Engn Patiala Punjab India

In data mining, data clustering is a prevalent data analysis methodology that organizes unlabeled data points into distinct clusters based on a similarity measure. In recent years, several clustering algorithms found, dependent on a predefined number of clusters and centered around the dataset with either numeric or categorical attributes only. However, many real-world engineering, scientific., and industrial applications involve datasets with mixed numeric as well as categorical attributes hut lack domain knowledge (target labels). Clustering unlabeled-mixed datasets is a challenging task as (I) it is difficult to estimate the number of clusters in the absence of domain knowledge and (2) mathematical operations cannot be applied directly to the mixed dataset. In this paper, an efficient searching and fast convergent automatic data clustering algorithm based on population-based metaheuristic optimization is proposed to deal with the mixed dataset. The proposed clustering algorithm aims to find the optimal number of cluster partitions automatically. It utilizes a real-coded variable-length candidate solution to detect the optimal number of clusters automatically, The concepts of threshold setting and cut-off ratio are used in the optimization process to refine the clusters. The similarity between data points and different cluster centers is measured using Euclidean distance (for numeric attributes) and the probability of co-occurrence of values (for categorical attributes). The proposed algorithm is compared with existing mixed data clustering techniques based on a statistical significance test and two robustness measures: Average accuracy and Standard deviation. Finally, the proposed algorithm is validated by applying to a real historical postoperative surgical mixed data set obtained from a surgical department of a multispecialty hospital in India. Results show the effectiveness, robustness, and usefulness of the proposed clustering algorithm. (C) 2020 The Authors.

关键词： Data clustering Meta-heuristic artificial electric field algorithm Distance measure Mixed dataset

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An Optimized Framework for Surgical Team Selection

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Computers, Materials & Continua 2021年第11期69卷 2563-2582页

作者： Hemant Petwal Rinkle Rani Department of Computer Science and Engineering Thapar Institute of Engineering and TechnologyPatiala147004India

In the healthcare system,a surgical team is a unit of experienced personnel who provide medical care to surgical patients during *** a surgical team is challenging for a multispecialty hospital as the performance of its members affects the efficiency and reliability of the hospital’s patient *** effectiveness of a surgical team depends not only on its individual members but also on the coordination among *** this paper,we addressed the challenges of surgical team selection faced by a multispecialty hospital and proposed a decision-making framework for selecting the optimal list of surgical teams for a given *** proposed framework focused on improving the existing surgical history management system by arranging surgery-bound patients into optimal subgroups based on similar characteristics and selecting an optimal list of surgical teams for a new surgical patient based on the patient’s *** this end,two population-based meta-heuristic algorithms for clustering of mixed datasets and multi-objective optimization were *** proposed algorithms were tested using different datasets and benchmark ***,the proposed framework was validated through a case study of a real postoperative surgical dataset obtained from the orthopedic surgery department of a multispecialty hospital in *** results revealed that the proposed framework was efficient in arranging patients in optimal groups as well as selecting optimal surgical teams for a given patient.

关键词： Multi-objective optimization artificial electric field algorithm mixed dataset clustering surgical team strength Pareto

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EHHM: electrical Harmony Based Hybrid Meta-Heuristic for Feature Selection

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IEEE ACCESS 2020年 8卷 158125-158141页

作者： Sheikh, Khalid Hassan Ahmed, Shameem Mukhopadhyay, Krishnendu Singh, Pawan Kumar Yoon, Jin Hee Geem, Zong Woo Sarkar, Ram Jadavpur Univ Dept Comp Sci & Engn Kolkata 700032 India Jadavpur Univ Dept Elect & Telecommun Engn Kolkata 700032 India Jadavpur Univ Dept Informat Technol Kolkata 700106 India Sejong Univ Sch Math & Stat Seoul 05006 South Korea Gachon Univ Dept Energy IT Seongnam 13120 South Korea

Selecting the most relevant features from a high dimensional dataset is always a challenging task. In this regard, the feature selection (FS) method acts as a solution to this problem mainly in the domain of data mining and machine learning. It aims at improving the performance of a learning model greatly by choosing the relevant features and ignoring the redundant ones. Besides, this also helps to achieve efficient use of space and time by the learning model under consideration. Though over the years, many meta-heuristic algorithms have been proposed by the researchers to solve FS problem, still this is considered as the open research problem due to its enormous challenges. Particularly, these algorithms, at times, suffer from poor convergence because of the improper tuning of exploration and exploitation phases. Here lies the importance of the hybrid meta-heuristics which help to improve the searching capability and convergence rate of the parent algorithms. To this end, the present work introduces a new hybrid meta-heuristic FS model by combining two meta-heuristics - Harmony Search (HS) algorithm and artificial electric field algorithm (AEFA), which we have named as electrical Harmony based Hybrid Meta-heurtistic (EHHM). The proposed hybrid meta-heuristic converges faster than its predecessors, thereby ensuring its capability to search efficiently. Usability of EHHM is examined by applying it on 18 standard UCI datasets. Moreover, to prove its supremacy, we have compared it with 10 state-of-the-art FS methods. Link to code implementation of proposed method: khalid0007/Metaheuristic-algorithms/FS_AEFAhHS.

关键词： Optimization Feature extraction Tuning Evolution (biology) Task analysis Heuristic algorithms Emotion recognition electrical harmony feature selection harmony search artificial electric field algorithm meta-heuristic hybrid optimization UCI datasets

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Optimizing Energy Savings and Operation of Active Distribution Networks Utilizing Hybrid Energy Resources and Soft Open Points: Case Study in Sohag, Egypt

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IEEE ACCESS 2020年 8卷 28704-28717页

作者： Shafik, M. B. Rashed, G. I. Chen, Hongkun Wuhan Univ Sch Elect Engn & Automat Wuhan 430072 Peoples R China Kafrelsheikh Univ Elect Power Syst & Machines Dept Fac Engn Kafrelsheikh 33516 Egypt

This research develops an overall framework for optimal operation of Hybrid Renewable Energy Resources (HRERs) system that involves the combination of PV/WT/Diesel units. In the proposed framework, a co-simulator tool is proposed to find the optimal scheduling of HRERs parameters. It composes HOMER PRO and NEPALN packages that are managed by C & x002B;& x002B;based online optimizer. Different scenarios of hybridization of energy resources are considered achieving technical, economic and environmental perspectives using the HOMER PRO package. The best allocation and sizing of Soft Open Points (SOPs) and the sharing of HRERs are obtained via multi-objective artificial electric field algorithm (AEFA). The simulation results propose a new hybrid grid-connected system to supply a realistic case study in Sohag-Egypt. That proposed HRERs system demonstrated a feasible economic solution at acceptable levels of gases emission and fuel consumption. Besides, the optimal operation of the HRERs system with SOPs potentially achieve significant technical perspectives that enhance the system losses, improve the voltage profile and increase the loading capacities of transmission lines. The technical perspective is handled using NEPLAN software. Furthermore, the simulation studies prove the high flexible planning and operation of distribution system at significant improvements technical, economic and environmental perspectives.

关键词： Active distribution networks hybrid renewable energy resources techno-economic analysis HOMER PRO NEPLAN artificial electric field algorithm

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Minimizing the Cost of Wide Area Monitoring Systems by Optimal Allocation of PMUs and Their Communication Infrastructure

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2020年第8期45卷 6453-6466页

作者： Abdelsalam, Abdelazeem A. Abdelaziz, Almoataz Y. Suez Canal Univ Fac Engn Elect Engn Dept Ismailia 41522 Egypt Future Univ Egypt Fac Engn & Technol Cairo Egypt

Power system utilities need to replace the conventional measuring and monitoring devices to the phasor measurement units (PMUs)-based wide area monitoring system (WAMS) to be able to move from conventional to smart grids. The challenge of this action is the cost of WAMS. Many researchers have tried to deal with this challenge by optimal allocation of PMUs to find the minimum number that fulfils the full observability of the system. The WAMS is not only PMUs but also it comprises communication infrastructures (CI) such as communication links, switches and phasor data concentrators. This paper proposes an optimization algorithm to solve the problem based on two strategies. In the first strategy, the objective function is to find the minimum number of PMUs, i.e. conventional objective function. While in the second strategy, the objective function is the minimum cost of the WAMS, i.e. cost of PMUs and their CI. The objective function in the two strategies is solved under normal operating condition and under contingencies such as failure of PMU and outage of network lines. The proposed artificial electric field algorithm (AEFA) is applied using two strategies on IEEE standard networks: 14-bus, 30-bus, 57-bus and 118-bus. The results show that the number of PMUs using two strategies is not the same and this means that the second strategy should be chosen to solve this problem. The results are also compared with other optimization algorithms, and they depict that the AEFA has the efficiency and superiority to find minimum cost of WAMS and lesser distance path of establishing communication links with higher observability.

关键词： artificial electric field algorithm Measurement redundancy Observability Phasor measurement units (PMUs) Wide area monitoring system (WAMS) Zero-injection bus

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Day-ahead optimal reactive power ancillary service procurement under dynamic multi-objective framework in wind integrated deregulated power system

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ENERGY 2021年 223卷 120028-120028页

作者： Sharma, Akanksha Jain, Sanjay K. Thapar Inst Engn & Technol Patiala Punjab India

This paper deals with the investigation of a day-ahead reactive power ancillary service procurement problem to minimize cost and voltage deviation under wind power generation uncertainties in a pool -based deregulated system. This reactive power procurement problem is formulated as a dynamic bi-objective optimization problem and is solved using a developed Pareto-based multi-objective artificial electric field algorithm (MO-AEFA). The developed MO-AEFA utilizes nondominated sorting principle and an external archive to store Pareto optimal solutions. Sign and reorder mutation operators are used to avoid trapping in local optima and enhance population diversity. The numerical results obtained from MO-AEFA are compared with other algorithms to validate the efficacy of proposed approach. The opti-mization algorithm utilizes a fast Newton power flow employing sparse matrix techniques to diminish computational burden. The capacitor switching is decided with consideration of marginal prices. The proposed methodology has been tested on modified IEEE 30-bus and IEEE 118-bus test systems. The performance of both test systems is analyzed for two studies namely, VAr dispatch without wind inte-gration, and VAr dispatch under wind integration. The analysis with wind integration is further inves-tigated for different wind penetration levels. The convergence characteristic of Pareto solutions is measured through statistical distance and diversity metrics. (c) 2021 Elsevier Ltd. All rights reserved.

关键词： Dynamic optimal power flow Multi-objective reactive power dispatch Locational marginal price Reactive power procurement artificial electric field algorithm

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Optimal design of hybrid grid-connected photovoltaic/wind/battery sustainable energy system improving reliability, cost and emission

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ENERGY 2022年 257卷

作者： Naderipour, Amirreza Kamyab, Hesam Klemes, Jiri Jaromir Ebrahimi, Reza Chelliapan, Shreeshivadasan Nowdeh, Saber Arabi Abdullah, Aldrin Marzbali, Massoomeh Hedayati Univ Sains Malaysia Sch Housing Bldg & Planning George Town 11800 Malaysia Univ Teknol Malaysia Malaysia Japan Int Inst Technol Jalan Sultan Yahya Petra Kuala Lumpur 54100 Malaysia Saveetha Inst Med & Tech Sci Saveetha Dent Coll & Hosp Dept Biomat Chennai 600077 Tamil Nadu India Azad Univ Dept Elect Engn Gorgan Branch Gorgan Golestan Iran Golestan Tech & Vocat Training Ctr Gorgan Golestan Iran Brno Univ Technol VUT Brno Fac Mech Engn NETME Ctr Sustainable Proc Integrat Lab SPIL Tech 2896-2 Brno 61669 Czech Republic Univ Teknol Malaysia Razak Fac Technol & Informat Engn Dept Jalan Sultan Yahya Petra Kuala Lumpur 54100 Malaysia

In this paper, the optimal designing framework for a grid-connected photovoltaic-wind energy system with battery storage (PV/Wind/Battery) is performed to supply an annual load considering vanadium redox battery (VRB) storage and lead-acid battery (LAB) to minimise the cost of system lifespan (CSLS) including the cost of components, cost of purchasing power from the grid and cost of CO2 emissions and the reliability constraint is defined as energy not supplied probability (ENSP). The optimal configuration of the system is found via an artificial electric field algorithm (AEFA). The capability of the design framework with the VRB is evaluated on sizing, CSLS, ENSP and cost of energy (COE) and in achieving a reliable and economical energy system in comparison with design based on the LAB. The results demonstrated that the CSLS (0.7-1%) and COE (0.87-1.2%) are reduced and reliability (7-10%) is improved more in the grid-connected designing framework for 1% of maximum ENSP (ENSPmax = 1%) in comparison with the stand-alone framework due to power purchasing capability from the grid and minimising the CO2 emission. The results cleared that the CSLS (32.7%), and COE (32.8%) are decreased and reliability (2-4.3%) is improved more for ENSPmax = 1% in system design with VRB than the system design using LAB storage due to higher depth of discharge and further efficiency. The results proved that increasing the ENSP constraint causes decreases in the CSLS and COE and enhances the reliability. The better performance of the proposed multi-criteria design framework via the AEFA is confirmed in comparison with the particle swarm optimisation (PSO) and grey wolf optimiser (GWO) to obtain the best configuration of the hybrid system with the lowest CSLS and COE and more reliability. (c) 2022 Elsevier Ltd. All rights reserved.

关键词： Improving reliability Cost and emission Hybrid PV Wind battery system Grid-connected system Vanadium redox battery storage artificial electric field algorithm

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