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firefly algorithm with multiple workers for the power system unit commitment problem

TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES 2016年第6期24卷 4773-4789页

作者： Koodalsamy, Banumalar Bairavan Veerayan, Manikandan Koodalsamy, Chandrasekaran Simon, Sishaj Pulikottil Mepco Schlenk Engn Coll Dept Elect & Elect Engn Sivakasi Tamil Nadu India Natl Inst Technol Puducherry Dept Elect & Elect Engn Karaikal India Natl Inst Technol Dept Elect & Elect Engn Tiruchirappalli Tamil Nadu India

This paper proposes an improved firefly (FF) algorithm with multiple workers for solving the unit commitment (UC) problem of power systems. The UC problem is a combinatorial optimization problem that can be posed as minimizing a quadratic objective function under system and unit constraints. Nowadays, highly developed computer systems are available in plenty, and proper utilization of these systems will reduce the time and complexity of combinatorial optimization problems with large numbers of generating units. Here, multiple workers are assigned to solve a UC problem as well as the subproblem, namely economic dispatch (ED) in distributed memory models. The proposed method incorporates a group search in a FF algorithm and thereby a global search is attained through the local search performed by the individual workers, which fine tune the search space in achieving the final solution. The execution time taken by the processor and the solution obtained with respect to the number of processors in a cluster are thoroughly discussed for different test systems. The methodology is validated on a 100 unit system, an IEEE 118 bus system, and a practical Taiwan 38 bus power system and the results are compared with the available literature.

关键词： Distributed memory models economic dispatch problem firefly algorithm multiple workers parallel computing unit commitment problem

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firefly algorithm-Driven Development of Resistive Ink-Coated Glass and Mesh Fibers for Advanced Microwave Stealth and EMI Shielding Applications

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JOURNAL OF ELECTRONIC MATERIALS 2024年第11期53卷 7140-7151页

作者： Sahu, Deepanshu Panwar, Ravi Indian Inst Informat Technol Discipline Elect & Commun Engn Design & Mfg Jabalpur 482005 India Indian Inst Technol Sch Mat Sci & Technol BHU Varanasi Varanasi 221005 India

The design and development of efficient microwave-absorbing and electromagnetic interference (EMI) shielding materials and structures to conceal electromagnetic (EM) waves remains a consistent and challenging task. Despite advancements in materials science and microwave engineering, there is a need for optimized materials that offer both effective microwave absorption and EMI shielding while minimizing material layer thickness. This research aims to address this gap by utilizing the firefly algorithm (FFA) to predict the optimal medium properties and thickness of microwave-absorbing and EMI shielding materials under specific constraints. In this context, a comprehensive investigation was carried out at the X-band involving numerical and experimental EM characterization of novel lightweight fiber-based samples. Additionally, the FFA has been applied to optimize these fiber-based microwave structures within the given constraints. Two separate objective functions (OBF) targeting minimum sample thickness, maximum microwave absorption, and shielding effectiveness (SE) bandwidth have been integrated into the FFA to address the thickness-bandwidth trade-off issue. Subsequently, resistive ink-coated glass fiber (IGF) and ink-coated mesh fiber (IMF) were developed and characterized based on the optimal solutions provided by the FFA. Consequently, an optimized IMF sample provides a minimum reflection coefficient (RC) of -19.0 dB at 10.7 GHz with a bandwidth of 2.8 GHz (9.6 to 12.4 GHz) below the -10 dB threshold. Besides, the optimal IGF sample achieves maximum SE of 11 dB at thickness of only 0.8 mm and covers the entire operating band. Furthermore, the response of the proposed structure was assessed for various oblique angles of incidence, revealing significant potential for various practical applications. A strong correlation between measured and theoretical findings underscores the potential of the proposed approach in realizing efficient microwave stealth and EMI shieldi

关键词： Electromagnetic interference shielding fiber firefly algorithm microwave absorption stealth

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firefly algorithm With Luciferase Inhibition Mechanism

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IEEE ACCESS 2019年 7卷 120189-120201页

作者： Peng, Hu He, Yichen Deng, Changshou Wu, Zhijian Jiujiang Univ Sch Informat Sci & Technol Jiujiang 332005 Peoples R China Wuhan Univ Sch Comp Wuhan 430072 Hubei Peoples R China

firefly algorithm (FA) was proposed by Yang which inspired from communication between fireflies through flash. As an efficient swarm intelligence algorithm, FA has been successfully applied in real-world applications. However, FA employs full attraction model that the method of selecting fireflies is sequential selection, namely each firefly can be attracted to other all fireflies in the worst case. This causes FA is prone to fall into local optima and high computational complexity. Inspired by the principle of firefly glow, an enhanced FA variant is proposed, namely, firefly algorithm with luciferase inhibition mechanism (LiFA), where luciferase inhibition mechanism is introduced to improve the effectiveness of selection. Besides, adaptive attraction model is also proposed to reduce the computational complexity and balance exploration and exploitation. Experiments are conducted on CEC2013 test suite to verify the performance of LiFA. The results show that LiFA has the best performance in some complex functions when compared other advanced FA variants.

关键词： Adaptive attraction firefly algorithm luciferase inhibition swarm intelligence algorithm

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firefly algorithm for solving non-convex economic dispatch problems with valve loading effect

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APPLIED SOFT COMPUTING 2012年第3期12卷 1180-1186页

作者： Yang, Xin-She Hosseini, Seyyed Soheil Sadat Gandomi, Amir Hossein Islamic Azad Univ Cent Tehran Branch Tehran Iran Islamic Azad Univ Sari Branch Sari Iran Univ Cambridge Dept Engn Cambridge CB2 1PZ England

The growing costs of fuel and operation of power generating units warrant improvement of optimization methodologies for economic dispatch (ED) problems. The practical ED problems have non-convex objective functions with equality and inequality constraints that make it much harder to find the global optimum using any mathematical algorithms. Modern optimization algorithms are often meta-heuristic, and they are very promising in solving nonlinear programming problems. This paper presents a novel approach to determining the feasible optimal solution of the ED problems using the recently developed firefly algorithm (FA). Many nonlinear characteristics of power generators, and their operational constraints, such as generation limitations, prohibited operating zones, ramp rate limits, transmission loss, and nonlinear cost functions, were all contemplated for practical operation. To demonstrate the efficiency and applicability of the proposed method, we study four ED test systems having non-convex solution spaces and compared with some of the most recently published ED solution methods. The results of this study show that the proposed FA is able to find more economical loads than those determined by other methods. This algorithm is considered to be a promising alternative algorithm for solving the ED problems in practical power systems. (C) 2011 Elsevier B.V. All rights reserved.

关键词： Economic load dispatch Valve loading effect firefly algorithm Metaheuristic

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firefly algorithm Based on Level-Based Attracting and Variable Step Size

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IEEE ACCESS 2020年 8卷 58700-58716页

作者： Zhao, Jia Chen, Wenping Ye, Jun Wang, Hui Sun, Hui Lee, Ivan Nanchang Inst Technol Sch Informat Engn Nanchang 330099 Jiangxi Peoples R China Jiangxi Prov Key Lab Water Informat Cooperat Sens Nanchang 330099 Jiangxi Peoples R China Natl Local Engn Lab Water Engn Safety & Effect Ut Nanchang 330099 Jiangxi Peoples R China Univ South Australia Sch Informat Technol & Math Sci Adelaide SA 5000 Australia

To address the challenge of local optimization and step size factor parameter setting in the full attraction model of the firefly algorithm (FA), this paper introduces level-based attraction and variable step size to FA. In the level-based attraction model, fireflies are firstly grouped in different levels according to the brightness, and each firefly randomly selects two fireflies from a higher level to learn from. By using the variable step size strategy, the searching step size decreases with the number of iterations. Herein, the level-based attracting model can increase the diversity of individual learning and improve the ability to jump out of local optimization. Meanwhile, dynamic adjustment of step size can balance the detection and development ability of the algorithm and improve the optimization accuracy of the algorithm. To evaluate the effectiveness of the proposed algorithm, comparisons are drawn against optimized FA, particle swarm optimization algorithm, artificial bee colony algorithm and differential evolution algorithm on a variety of test function sets.

关键词： firefly algorithm full attraction model level-based attracting model variable step size

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firefly algorithm based on intelligent single particle learning

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INTERNATIONAL JOURNAL OF COMPUTING SCIENCE AND MATHEMATICS 2020年第4期12卷 309-326页

作者： Chen, Wenping Ye, Jun Wu, Runxiu Liu, Guangming Kang, Ping Nanchang Inst Technol Sch Informat Engn Nanchang 330099 Jiangxi Peoples R China

The particles in the population of the firefly algorithm learn from each other using an all-attractive model, and the algorithm has a strong ability of social learning and global detection. However, the algorithm ignores the role of the global optimal particle, resulting in weak self-learning and local development ability of the algorithm. Therefore, this paper proposes an intelligent single-particle learning firefly algorithm. The algorithm divides the iterative process into two stages, the first stage adopts the standard firefly algorithm to evolve;in the second stage, the intelligent single particle optimisation algorithm is used to optimise the global optimal particle. The iterative process in the first stage ensures the sociality and global detection ability of the particle, and the second stage enhances the ability of self-learning and local development of the algorithm. The experimental results show that the algorithm in this paper has better performance.

关键词： firefly algorithm intelligent single particle optimisation all-attractive model detection and development global optimal particle self-learning

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firefly algorithm for Movable Antenna Arrays

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IEEE WIRELESS COMMUNICATIONS LETTERS 2024年第11期13卷 3157-3161页

作者： Hoang, Manh Kha Le, Tuan Anh Thuc, Kieu-Xuan Luyen, Tong Van Yang, Xin-She Ng, Derrick Wing Kwan Hanoi Univ Ind Fac Elect Engn Hanoi 100000 Vietnam Middlesex Univ Fac Sci & Technol Dept Design Engn & Math London NW4 4BT England Univ New South Wales Sch Elect Engn & Telecommun Sydney NSW 2052 Australia

This letter addresses a multivariate optimization problem for linear movable antenna arrays (MAAs). Particularly, the position and beamforming vectors of the under-investigated MAA are optimized simultaneously to maximize the minimum beamforming gain across several intended directions, while ensuring interference levels at various unintended directions remain below specified thresholds. To this end, a swarm-intelligence-based firefly algorithm (FA) is introduced to acquire an effective solution to the optimization problem. Simulation results reveal the superior performance of the proposed FA approach compared to the state-of-the-art approach employing alternating optimization and successive convex approximation. This is attributed to the FA's effectiveness in handling non-convex multivariate and multimodal optimization problems without resorting approximations.

关键词： Array signal processing Vectors Optimization Linear antenna arrays Brightness Interference Simulation firefly algorithm nature-inspired optimization movable antenna arrays

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firefly algorithm inspired energy aware clustering protocol for wireless sensor network

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INTERNATIONAL JOURNAL OF COMMUNICATION NETWORKS AND DISTRIBUTED SYSTEMS 2019年第3期23卷 380-411页

作者： Bongale, Anupkumar M. Nirmala, C. R. DY Patil Coll Engn Ambi Dept Comp Engn Pune 410506 Maharashtra India Bapuji Inst Engn & Technol Dept Comp Sci & Engn Davangere 577005 Karnataka India

Most of the existing cluster-based routing protocols focus either on cluster head election schemes or on refinement of routing process ignoring process of cluster formation. In this paper, firefly algorithm inspired energy aware clustering protocol for WSN is presented. The idea of the proposed protocol is to elect the cluster head nodes by assigning them intensity value defined through firefly algorithm. Another contribution of this paper is energy efficient cluster formation where cluster members join to cluster heads based on intensity parameter associated with cluster head. The presented protocol ensures that the elected cluster heads are usually located in densely populated area of the network resulting in low distance intra-cluster data transmissions and ultimately leading to low energy consumption. The firefly-based protocol is compared with popular routing protocols such as LEACH, LEACH-C and EOICHD and comparative results indicate that proposed protocol is highly energy efficient than other protocols.

关键词： cluster formation cluster head election firefly algorithm wireless sensor network WSN communication networks distributed systems

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firefly algorithm for Reliable Protection in Distribution Networks

Firefly Algorithm for Reliable Protection in Distribution Ne...

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International Conference on Intelligent Computing, Instrumentation and Control Technologies (ICICICT)

作者： Anand, Rimmi Kumar, Vijay Thapar Univ Comp Sci & Engn Dept Patiala Punjab India

ISBN: (纸本)9781509061068

The aim of this paper is to propose a model for reliable Distribution centers (DCs) in case of unexpected disruption in DCs. Also, random disruptions between links in a distribution network system. The mixed-integer linear programming model (MILP) is formulated that aims to provide reliable DCs in case of random failures. The site-dependent failure probabilities, three investment levels for opening unreliable facility has been considered. The IBM CPLEX 12.6.3 solver has been used to implement recently developed metaheuristic firefly algorithm on the proposed model. Numerical results are presented on basis of random generated examples. The firefly algorithm has outperformed CPLEX on large instances up to 200 customers and 30 DCs.

关键词： Supply chain network design Distribution network Facility location Reliability Disruption firefly algorithm

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firefly algorithm Enhanced by Orthogonal Learning 7th

Firefly Algorithm Enhanced by Orthogonal Learning

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7th Computer Science On-Line Conference (CSOC)

作者： Kadavy, Tomas Pluhacek, Michal Viktorin, Adam Senkerik, Roman Tomas Bata Univ Zlin TG Masaryka 5555 Zlin 76001 Czech Republic

ISBN: (纸本)9783319911892

Orthogonal learning strategy, a proven technique, is combined with hybrid optimization metaheuristic, which is based on firefly algorithm and Particle Swarm Optimization. The hybrid algorithm firefly Particle Swarm Optimization is then compared, together with canonical firefly algorithm, with the newly created Orthogonal Learning firefly algorithm. Comparisons have been conducted on five selected basic benchmark functions, and the results have been evaluated for statistical significance using Wilcoxon rank-sum test.

关键词： firefly algorithm Particle Swarm Optimization Orthogonal learning

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