Intelligent swarm firefly algorithm for the prediction of China's national electricity consumption

Zhang, Guangfeng; Chen, Leo; Li, Yun; Yu, Hongnian; Hu, Huosheng; Wu, Shaomin

doi:10.1504/ijbic.2019.098407

Cited by 9 publications

(3 citation statements)

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“…To solve diverse applications, numerous algorithms are proposed. [29][30][31] The bat algorithm 32,33 is among the classic swarm intelligence optimization techniques [34][35][36] that used the principle of echolocation of the bat to discover the optimum solution within the scope of the problem. Several researchers have proved this algorithm to verify its feasibility.…”

Section: Related Workmentioning

confidence: 99%

FUCBR: Fuzzy‐based unequal clustering and bat‐based routing protocol for WSNs assisted IoT

Agrawal

Pandey

2023

Concurrency and Computation

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SummaryThe internet of things (IoT) has recently become extremely important in our lives. With the advancement in IoT, wireless sensor networks (WSNs) have gained much importance. In WSNs, the routing of clusters has gained more interest. However, there is still a problem with the hotspot. One way to address this issue is uneven clustering. In these, nodes closer to the base station have the smallest cluster size. It decreases the relay pressure of the nodes which are close to the base station. In this paper, the unequal clustering routing based on fuzzy logic and bat algorithm is proposed, that is, FUCBR. Relative intra‐cluster costs and relative inter‐cluster costs are used in a very innovative manner. Fuzzy logic is used to create unequal clusters while the bat algorithm is used for multi‐hop routing. Unequal clustering minimizes the energy utilization for the member of the clusters. A novel probability technique is implemented in the cluster‐forming process to allow cluster members to determine which cluster they wish to join. The bat algorithm's objective function is carefully chosen to optimize the cluster heads' energy usage. After simulation, it was discovered that the proposed algorithm utilizes less energy and extends the network lifespan. The simulation results show that FUCBR's performance is 30% more improved than fuzzy‐based unequal clustering algorithm, genetic algorithm based unequal clustering and routing protocol, and grey wolf optimizer‐C algorithms. It is suitable for networks that require a long life.

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Section: Related Workmentioning

confidence: 99%

FUCBR: Fuzzy‐based unequal clustering and bat‐based routing protocol for WSNs assisted IoT

Agrawal

Pandey

2023

Concurrency and Computation

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show abstract

“…Various algorithms are proposed to solve different applications. [29][30][31] The bat algorithm 32,33 is one of the most classical swarm intelligent optimization algorithms, [34][35][36][37] which use the principle of bat echolocation to find the optimal solution in the solution space. This algorithm 38,39 has been proved by many scholars to verify its practicability.…”

Section: Figurementioning

confidence: 99%

A unified heuristic bat algorithm to optimize the LEACH protocol

Cai

Geng

et al. 2019

Concurrency and Computation

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Summary Wireless sensor networks (WSN) have high value in the field of wireless communications. As the earliest WSN clustering protocol, Low Energy Adaptive Clustering Hierarchy (LEACH) can effectively reduce the energy consumption of data transmission in sensor networks. However, LEACH has some problems such as cluster head nodes are unevenly distributed. In this paper, a unified heuristic bat algorithm (UHBA) is proposed to optimize elections in cluster heads. This algorithm guarantees that the election of cluster heads can freely transform both global search and local search. Meanwhile, comparing with several other variants of the bat algorithm in CEC2013 test suite, it can be seen from results that UHBA has better performance. Moreover, the application of the algorithm on LEACH is better than other algorithms, which further proves that the algorithm has better results.

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“…Although they are easy to implement, they cannot completely solve all problems in task scheduling simultaneously. Therefore, researchers begin to use swarm intelligence algorithms [7], [8] to solve the problem, such as particle swarm optimization (PSO) [9], ant colony optimization (ACO) [10], bat algorithm (BA), firefly algorithm (FA) [11], [12], cuckoo search (CS) [13], [14] and so on. A large number of research results show that the intelligent algorithm is superior in solving scheduling problems.…”

Section: Introductionmentioning

confidence: 99%

Many-Objective Cloud Task Scheduling

Geng

Wang

et al. 2020

IEEE Access

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Task scheduling problem refers to how to reasonably arrange many tasks provided by users in virtual machines, which is very important in the cloud computing. And the quality of the scheduling performance directly affects the customer satisfaction and the provider benefits. In order to describe the task scheduling problem of cloud computing more precisely and improve the scheduling performance. This paper establishes many-objective cloud model, including four objectives: minimizing time, minimizing costs, maximizing resource utilization, and balancing load. At the same time, a many-objective optimization algorithm based on hybrid angles (MaOEA-HA) is proposed to solve this model. Hybrid angle strategy is designed to optimize the algorithm better, which combines two angle strategies: individual-to-individual angle and individual-to-reference point angle. One by one elimination strategy was introduced to remain individuals with better performance. By comparing with five other advanced many-objective optimization algorithms, MaOEA-HA shows the best performance on the DTLZ test suite. Moreover, different algorithms are applied to solve the cloud task scheduling problem, and MaOEA-HA algorithm achieves best results.INDEX TERMS Cloud computing, hybrid-angle strategy, many-objective algorithm, task scheduling.

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Intelligent swarm firefly algorithm for the prediction of China's national electricity consumption

Abstract: The version in the Kent Academic Repository may differ from the final published version. Users are advised to check http://kar.kent.ac.uk for the status of the paper. Users should always cite the published version of record.

Cited by 9 publications

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FUCBR: Fuzzy‐based unequal clustering and bat‐based routing protocol for WSNs assisted IoT

FUCBR: Fuzzy‐based unequal clustering and bat‐based routing protocol for WSNs assisted IoT

A unified heuristic bat algorithm to optimize the LEACH protocol

Many-Objective Cloud Task Scheduling

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