Application of Hybrid Firefly Algorithm for Sheath Loss Reduction of Underground Transmission Systems

Huang, Shyh-Jier; Liu, Xian-Zong; Su, Wei-Fu; Yang, Shu-Hsuan

doi:10.1109/tpwrd.2013.2265913

Cited by 16 publications

(5 citation statements)

References 14 publications

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“…Recently, hybridizing local search strategies with PSO and FA has attracted more attentions, and the experimental results verify the promising comprehensive performance, including faster convergence speed and higher solution quality, of the hybrid algorithms (Li, Yang, & Nguyen, 2012;Huang, Liu, Su, & Yang, 2013). Meanwhile, detecting strategies (Xia, Liu, & Hu, 2014) and opposition-based learning (OBL) strategies (Yu, Zhu, Ma, & Mao, 2015) are also introduced to help the population to jump out of locally optimal solutions.…”

Section: Metaheuristic Methodsmentioning

confidence: 95%

Multimodal optimization for time-cost trade-off in construction projects using a novel hybrid method based on FA and PSO

Albayrak

Özdemir

2018

rdlc

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Completion of the activities within optimal time and cost plays a significant role in construction projects. Recently, project managers have to decrease the total durations and costs of the projects more than before due to the competitive environment. Mostly, decision makers usually seek different alternatives which reduce time and cost. As well as being one of the most major topics of construction management, this problem called time-cost trade-off (TCTO) which is extremely difficult to solve with traditional mathematical methods. In recent years, metaheuristic algorithms are outstanding methods in this field due to their flexible and adaptable structure. This paper presents a new algorithm called F-PSO which consists of hybridizing Firefly Algorithm (FA) with Particle Swarm Optimization (PSO). In this method, the problem is modelled with various execution modes to select the optimal one for each activity. The applicability and validity of the proposed method is confirmed by performing 18-activity project as a benchmark problem. Comparison of numerical results with different metaheuristic algorithms demonstrates the effectiveness and efficiency of F-PSO with regard to optimality of time and cost outcomes.

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Section: Metaheuristic Methodsmentioning

confidence: 95%

Multimodal optimization for time-cost trade-off in construction projects using a novel hybrid method based on FA and PSO

Albayrak

Özdemir

2018

rdlc

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“…Furthermore, the advantages of optimization ideas can improve the algorithm's performance. In 2013, Huang et al [31] proposed a hybrid firefly algorithm combining local random search methods, which effectively improved the local search ability of the algorithm (HFA). In addition, HFA is applied to reduce the jacket loss of downhole transmission and good results are achieved.…”

Section: Hybrid Firefly Algorithmmentioning

confidence: 99%

Improved Hybrid Firefly Algorithm with Probability Attraction Model

Bei

Zhang²,

Wang

et al. 2023

Mathematics

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An improved hybrid firefly algorithm with probability attraction model (IHFAPA) is proposed to solve the problems of low computational efficiency and low computational accuracy in solving complex optimization problems. First, the method of square-root sequence was used to generate the initial population, so that the initial population had better population diversity. Second, an adaptive probabilistic attraction model is proposed to attract fireflies according to the brightness level of fireflies, which can minimize the brightness comparison times of the algorithm and moderate the attraction times of the algorithm. Thirdly, a new location update method is proposed, which not only overcomes the deficiency in that the relative attraction of two fireflies is close to 0 when the distance is long but also overcomes the deficiency that the relative attraction of two fireflies is close to infinity when the distance is small. In addition, a combinatorial variational operator based on selection probability is proposed to improve the exploration and exploitation ability of the firefly algorithm (FA). Later, a similarity removal operation is added to maintain the diversity of the population. Finally, experiments using CEC 2017 constrained optimization problems and four practical problems in engineering show that IHFAPA can effectively improve the quality of solutions.

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“…Nandy et al (2012) combined the firefly metaheuristic with back-propagation algorithm to achieve a faster convergence rate in training a feed-forward neural network. Huang et al (2013) proposed a hybrid FA by mimicking a bionic random process, and applied it to solve the decision-making problem in underground cable transmission systems.…”

Section: Firefly Algorithmmentioning

confidence: 99%

New progresses in swarm intelligence-based computation

Duan

Luo

2015

IJBIC

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Nature is a great and immense source of inspiration for solving complex problems in the real world. The well-known examples in nature for swarms are bird flocks, fish schools and the colony of social insects. Birds, ants, bees, fireflies, bats, and pigeons are all bringing us various inspirations for swarm intelligence. In 1990s, swarm intelligence algorithms based on ant colony have highly attracted the interest of researchers. During the past two decades, several new algorithms have been developed depending on different intelligent behaviours of natural swarms. This review presents a comprehensive survey of swarm intelligence-based computation algorithms, which are ant colony optimisation, particle swarm optimisation, artificial bee colony, firefly algorithm, bat algorithm, and pigeon inspired optimisation. Future orientations are also discussed thoroughly.

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Application of Hybrid Firefly Algorithm for Sheath Loss Reduction of Underground Transmission Systems

Cited by 16 publications

References 14 publications

Multimodal optimization for time-cost trade-off in construction projects using a novel hybrid method based on FA and PSO

Multimodal optimization for time-cost trade-off in construction projects using a novel hybrid method based on FA and PSO

Improved Hybrid Firefly Algorithm with Probability Attraction Model

New progresses in swarm intelligence-based computation

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