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Nazari A, Abdi H. Solving the Combined Heat and Power Economic Dispatch Problem in Different Scale Systems Using the Imperialist Competitive Harris Hawks Optimization Algorithm. Biomimetics (Basel) 2023; 8:587. [PMID: 38132526 PMCID: PMC10741563 DOI: 10.3390/biomimetics8080587] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Revised: 09/21/2023] [Accepted: 09/23/2023] [Indexed: 12/23/2023] Open
Abstract
The aim of electrical load dispatch (ELD) is to achieve the optimal planning of different power plants to supply the required power at the minimum operation cost. Using the combined heat and power (CHP) units in modern power systems, increases energy efficiency and, produce less environmental pollution than conventional units, by producing electricity and heat, simultaneously. Consequently, the ELD problem in the presence of CHP units becomes a very non-linear and non-convex complex problem called the combined heat and power economic dispatch (CHPED), which supplies both electric and thermal loads at the minimum operational cost. In this work, at first, a brief review of optimization algorithms, in different categories of classical, or conventional, stochastic search-based, and hybrid optimization techniques for solving the CHPED problem is presented. Then the CHPED problem in large-scale power systems is investigated by applying the imperialist competitive Harris hawks optimization (ICHHO), as the combination of imperialist competitive algorithm (ICA), and Harris hawks optimizer (HHO), for the first time, to overcome the shortcomings of using the ICA and HHO in the exploitation, and exploration phases, respectively, to solve this complex optimization problem. The effectiveness of the combined algorithm on four standard case studies, including 24 units as a medium-scale, 48, 84, units as the large-scale, and 96-unit as a very large-scale heat and power system, is detailed. The obtained results are compared to those of different algorithms to demonstrate the performance of the ICHHO algorithm in terms of better solution quality and lower fuel cost. The simulation studies verify that the proposed algorithm decreases the minimum operation costs by at least 0.1870%, 0.342%, 0.05224%, and 0.07875% compared to the best results in the literature.
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Affiliation(s)
| | - Hamdi Abdi
- Electrical Engineering Department, Engineering Faculty, Razi University, Kermanshah 67144-14971, Iran;
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Hamzei M, Khandagh S, Jafari Navimipour N. A Quality-of-Service-Aware Service Composition Method in the Internet of Things Using a Multi-Objective Fuzzy-Based Hybrid Algorithm. Sensors (Basel) 2023; 23:7233. [PMID: 37631769 PMCID: PMC10458659 DOI: 10.3390/s23167233] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Revised: 08/06/2023] [Accepted: 08/10/2023] [Indexed: 08/27/2023]
Abstract
The Internet of Things (IoT) represents a cutting-edge technical domain, encompassing billions of intelligent objects capable of bridging the physical and virtual worlds across various locations. IoT services are responsible for delivering essential functionalities. In this dynamic and interconnected IoT landscape, providing high-quality services is paramount to enhancing user experiences and optimizing system efficiency. Service composition techniques come into play to address user requests in IoT applications, allowing various IoT services to collaborate seamlessly. Considering the resource limitations of IoT devices, they often leverage cloud infrastructures to overcome technological constraints, benefiting from unlimited resources and capabilities. Moreover, the emergence of fog computing has gained prominence, facilitating IoT application processing in edge networks closer to IoT sensors and effectively reducing delays inherent in cloud data centers. In this context, our study proposes a cloud-/fog-based service composition for IoT, introducing a novel fuzzy-based hybrid algorithm. This algorithm ingeniously combines Ant Colony Optimization (ACO) and Artificial Bee Colony (ABC) optimization algorithms, taking into account energy consumption and Quality of Service (QoS) factors during the service selection process. By leveraging this fuzzy-based hybrid algorithm, our approach aims to revolutionize service composition in IoT environments by empowering intelligent decision-making capabilities and ensuring optimal user satisfaction. Our experimental results demonstrate the effectiveness of the proposed strategy in successfully fulfilling service composition requests by identifying suitable services. When compared to recently introduced methods, our hybrid approach yields significant benefits. On average, it reduces energy consumption by 17.11%, enhances availability and reliability by 8.27% and 4.52%, respectively, and improves the average cost by 21.56%.
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Affiliation(s)
- Marzieh Hamzei
- Department of Computer Engineering, Tabriz Branch, Islamic Azad University, Tabriz 51376-53515, Iran
| | - Saeed Khandagh
- Electrical Engineering Department, Tabriz Branch, University of Applied Sciences and Technology, Tabriz 51376-53515, Iran
| | - Nima Jafari Navimipour
- Department of Computer Engineering, Faculty of Engineering and Natural Sciences, Kadir Has University, 34083 Istanbul, Turkey
- Future Technology Research Center, National Yunlin University of Science and Technology, Douliou 64002, Taiwan
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Yang S, Zhang L, Yang X, Sun J, Dong W. A Multiple Mechanism Enhanced Arithmetic Optimization Algorithm for Numerical Problems. Biomimetics (Basel) 2023; 8:348. [PMID: 37622953 PMCID: PMC10452629 DOI: 10.3390/biomimetics8040348] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Revised: 08/01/2023] [Accepted: 08/01/2023] [Indexed: 08/26/2023] Open
Abstract
The Arithmetic Optimization Algorithm (AOA) is a meta-heuristic algorithm inspired by mathematical operators, which may stagnate in the face of complex optimization issues. Therefore, the convergence and accuracy are reduced. In this paper, an AOA variant called ASFAOA is proposed by integrating a double-opposite learning mechanism, an adaptive spiral search strategy, an offset distribution estimation strategy, and a modified cosine acceleration function formula into the original AOA, aiming to improve the local exploitation and global exploration capability of the original AOA. In the proposed ASFAOA, a dual-opposite learning strategy is utilized to enhance population diversity by searching the problem space a lot better. The spiral search strategy of the tuna swarm optimization is introduced into the addition and subtraction strategy of AOA to enhance the AOA's ability to jump out of the local optimum. An offset distribution estimation strategy is employed to effectively utilize the dominant population information for guiding the correct individual evolution. In addition, an adaptive cosine acceleration function is proposed to perform a better balance between the exploitation and exploration capabilities of the AOA. To demonstrate the superiority of the proposed ASFAOA, two experiments are conducted using existing state-of-the-art algorithms. First, The CEC 2017 benchmark function was applied with the aim of evaluating the performance of ASFAOA on the test function through mean analysis, convergence analysis, stability analysis, Wilcoxon signed rank test, and Friedman's test. The proposed ASFAOA is then utilized to solve the wireless sensor coverage problem and its performance is illustrated by two sets of coverage problems with different dimensions. The results and discussion show that ASFAOA outperforms the original AOA and other comparison algorithms. Therefore, ASFAOA is considered as a useful technique for practical optimization problems.
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Affiliation(s)
- Sen Yang
- College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China; (L.Z.); (X.Y.); (J.S.); (W.D.)
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Li H, Li N, Guo Y, Yuan H, Lei B. Meta-Heuristic Device-Free Localization Algorithm under Multiple Path Effect. Entropy (Basel) 2023; 25:1025. [PMID: 37509972 PMCID: PMC10377885 DOI: 10.3390/e25071025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/19/2023] [Revised: 06/28/2023] [Accepted: 06/30/2023] [Indexed: 07/30/2023]
Abstract
In the scenario of device-free localization under multiple effects, the accuracy of localization based on compressed sensing theory is severely affected. Most existing localization techniques directly ignore multiple path effects. However, it is not practical to ignore the multiple path effect due to its high signal strength, which can provide localization information. In this paper, we formulate the sensing matrix optimization problem in compressed sensing for device-free localization scenarios based on multiple reflections. To solve this problem, we model it as a constrained combinatorial optimization problem and propose a hybrid meta-heuristic algorithm. First, smart reflection surfaces and virtual node models are used to construct the desired communication links. Second, we iteratively improve the properties of the measurement matrix by using K-means clustering to obtain reasonable thresholds, and use a meta-heuristic algorithm to optimize the sensing matrix. Finally, the simulation results show that the proposed method efficiently optimizes the sensing matrix and achieves fast and high-precision localization while conserving communication resources.
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Affiliation(s)
- Huajing Li
- College of Communications Engineering, PLA Army Engineering University, Nanjing 210007, China
| | - Ning Li
- College of Communications Engineering, PLA Army Engineering University, Nanjing 210007, China
| | - Yan Guo
- College of Communications Engineering, PLA Army Engineering University, Nanjing 210007, China
| | - Hao Yuan
- College of Communications Engineering, PLA Army Engineering University, Nanjing 210007, China
| | - Binghan Lei
- College of Communications Engineering, PLA Army Engineering University, Nanjing 210007, China
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Mohebian P, Aval SBB, Noori M, Lu N, Altabey WA. Visible Particle Series Search Algorithm and Its Application in Structural Damage Identification. Sensors (Basel) 2022; 22:s22031275. [PMID: 35162020 PMCID: PMC8838178 DOI: 10.3390/s22031275] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Revised: 02/02/2022] [Accepted: 02/03/2022] [Indexed: 05/27/2023]
Abstract
Identifying structural damage is an essential task for ensuring the safety and functionality of civil, mechanical, and aerospace structures. In this study, the structural damage identification scheme is formulated as an optimization problem, and a new meta-heuristic optimization algorithm, called visible particle series search (VPSS), is proposed to tackle that. The proposed VPSS algorithm is inspired by the visibility graph technique, which is a technique used basically to convert a time series into a graph network. In the proposed VPSS algorithm, the population of candidate solutions is regarded as a particle series and is further mapped into a visibility graph network to obtain visible particles. The information captured from the visible particles is then utilized by the algorithm to seek the optimum solution over the search space. The general performance of the proposed VPSS algorithm is first verified on a set of mathematical benchmark functions, and, afterward, its ability to identify structural damage is assessed by conducting various numerical simulations. The results demonstrate the high accuracy, reliability, and computational efficiency of the VPSS algorithm for identifying the location and the extent of damage in structures.
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Affiliation(s)
- Pooya Mohebian
- Faculty of Civil Engineering, K. N. Toosi University of Technology, Tehran 196976-4499, Iran; (P.M.); (S.B.B.A.)
| | - Seyed Bahram Beheshti Aval
- Faculty of Civil Engineering, K. N. Toosi University of Technology, Tehran 196976-4499, Iran; (P.M.); (S.B.B.A.)
| | - Mohammad Noori
- Department of Mechanical Engineering, California Polytechnic State University, San Luis Obispo, CA 93405, USA
| | - Naiwei Lu
- School of Civil Engineering, Changsha University of Science and Technology, Changsha 410076, China;
| | - Wael A. Altabey
- International Institute for Urban Systems Engineering, Southeast University, Nanjing 210096, China
- Department of Mechanical Engineering, Faculty of Engineering, Alexandria University, Alexandria 21544, Egypt
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Wang Z, Qin C, Wan B, Song WW. A Comparative Study of Common Nature-Inspired Algorithms for Continuous Function Optimization. Entropy (Basel) 2021; 23:874. [PMID: 34356415 PMCID: PMC8304592 DOI: 10.3390/e23070874] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Revised: 06/25/2021] [Accepted: 06/30/2021] [Indexed: 11/21/2022]
Abstract
Over previous decades, many nature-inspired optimization algorithms (NIOAs) have been proposed and applied due to their importance and significance. Some survey studies have also been made to investigate NIOAs and their variants and applications. However, these comparative studies mainly focus on one single NIOA, and there lacks a comprehensive comparative and contrastive study of the existing NIOAs. To fill this gap, we spent a great effort to conduct this comprehensive survey. In this survey, more than 120 meta-heuristic algorithms have been collected and, among them, the most popular and common 11 NIOAs are selected. Their accuracy, stability, efficiency and parameter sensitivity are evaluated based on the 30 black-box optimization benchmarking (BBOB) functions. Furthermore, we apply the Friedman test and Nemenyi test to analyze the performance of the compared NIOAs. In this survey, we provide a unified formal description of the 11 NIOAs in order to compare their similarities and differences in depth and a systematic summarization of the challenging problems and research directions for the whole NIOAs field. This comparative study attempts to provide a broader perspective and meaningful enlightenment to understand NIOAs.
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Affiliation(s)
- Zhenwu Wang
- Department of Computer Science and Technology, China University of Mining and Technology, Beijing 100083, China;
| | - Chao Qin
- Department of Computer Science and Technology, China University of Mining and Technology, Beijing 100083, China;
| | - Benting Wan
- School of Software and IoT Engineering, Jiangxi University of Finance & Economics, Nanchang 330013, China;
| | - William Wei Song
- School of Software and IoT Engineering, Jiangxi University of Finance & Economics, Nanchang 330013, China;
- Department of Information Systems, Dalarna University, S-791 88 Falun, Sweden
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