A Novel Improved Bat Algorithm Based on Hybrid Parallel and Compact for Balancing an Energy Consumption Problem

Nguyen, Trong-The; Pan, Jeng‐Shyang; Dao, Thi-Kien

doi:10.3390/info10060194

Cited by 28 publications

(15 citation statements)

References 34 publications

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“…where PDF is the probability distribution function of PV, µ and σ are two parameters of Equation (18), which control the probability change of PDF. The definition of er f can be found in [37]. PDF in this paper is achieved by constructing a polynomial cumulative distribution function (CDF) [54].…”

Section: Compacted Pigeon-inspired Optimizationmentioning

confidence: 99%

“…To evaluate the performance of the PCPIO algorithm proposed in this paper, the test function in [19,37] was used as the criterion. Detailed information about the function is given in Tables 1-3, where TM represents the theoretical minimum of the test function.…”

Section: Experiments With Numerical Optimization Problemsmentioning

confidence: 99%

“…The evolution process of the entire group can be regarded as an optimization process, but the evolution trajectory of a single individual may not be an optimization process. Evolutionary calculations include Genetic Algorithm (GA) [13], Particle Swarm Optimization (PSO) [14][15][16][17], Grey Wolf Optimizer (GWO) [18][19][20][21], Cat Swarm Optimization [22][23][24], Differential Evolution (DE) [25][26][27], Ant Colony Optimization (ACO) [28][29][30], Artificial Bee Colony (ABC) [31,32], Flower Pollination Algorithm (FPA) [33,34], Bat Algorithm (BA) [35][36][37], QUasi-Affine TRansformation Evolutionary (QUATRE) [38][39][40][41], and Multi-Verse Optimizer (MVO) [42,43]. The pigeon swarm algorithm is a new meta-heuristic algorithm proposed by Duan in 2014 [44].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Novel Pigeon-Inspired Optimization Based MPPT Technique for PV Systems

et al. 2020

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The conventional maximum power point tracking (MPPT) method fails in partially shaded conditions, because multiple peaks may appear on the power–voltage characteristic curve. The Pigeon-Inspired Optimization (PIO) algorithm is a new type of meta-heuristic algorithm. Aiming at this situation, this paper proposes a new type of algorithm that combines a new pigeon population algorithm named Parallel and Compact Pigeon-Inspired Optimization (PCPIO) with MPPT, which can solve the problem that MPPT cannot reach the near global maximum power point. This hybrid algorithm is fast, stable, and capable of globally optimizing the maximum power point tracking algorithm. Therefore, the purpose of this article is to study the performance of two optimization techniques. The two algorithms are Particle Swarm Algorithm (PSO) and improved pigeon algorithm. This paper first studies the mechanism of multi-peak output characteristics of photovoltaic arrays in complex environments, and then proposes a multi-peak MPPT algorithm based on a combination of an improved pigeon population algorithm and an incremental conductivity method. The improved pigeon algorithm is used to quickly locate near the maximum power point, and then the variable step size incremental method INC (incremental conductance) is used to accurately locate the maximum power point. A simulation was performed on Matlab/Simulink platform. The results prove that the method can achieve fast and accurate optimization under complex environmental conditions, effectively reduce power oscillations, enhance system stability, and achieve better control results.

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Section: Compacted Pigeon-inspired Optimizationmentioning

confidence: 99%

Section: Experiments With Numerical Optimization Problemsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Novel Pigeon-Inspired Optimization Based MPPT Technique for PV Systems

et al. 2020

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“…A good designed and employed WSN often follows the clustering fashion as efficient ways of saving energy networks that is to generate clusters by arranging the sensor nodes into groups [10]. The power consumption of WSNs is affected directly by the clustering criterion problem [11]. The cluster composes node members (NM) and cluster head (CH).…”

Section: Introductionmentioning

confidence: 99%

Identifying correctness data scheme for aggregating data in cluster heads of wireless sensor network based on naive Bayes classification

Chu

Dao

Pan

et al. 2020

J Wireless Com Network

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Wireless sensor network (WSN) has been paid more attention by scholars due to the practical communication of a system of devices to transfer information gathered from a monitored field through wireless links. Precise and accurate data of aggregating messages from sensor nodes is a vital demand for a success WSN application. This paper proposes a new scheme of identifying the correctness data scheme for aggregating data in cluster heads in hierarchical WSN based on naive Bayes classification. The collecting environmental information includes temperature, humidity, sound, and pollution levels, from sensor nodes to cluster heads that classify data fault and aggregate and transfer them to the base station. The collecting data is classified based on the classifier to aggregate in the cluster head of WSN. Compared with some existing methods, the proposed method offers an effective way of forwarding the correct data in WSN applications.

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“…In WRSNs, unlike Wireless Sensor Networks (WSNs), the battery in each sensor node can be recharged, and one or several mobile chargers are dispatched to charge all sensor nodes when the battery energy is below the threshold. As a result, in WRSNs, the sensor nodes can always operate for a long duration without exhausting the energy [4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

A New on-Demand Recharging Strategy Based on Cycle-Limitation in a WRSN

Wang

Dong

et al. 2019

Symmetry

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Recently, Wireless Rechargeable Sensor Networks (WRSNs) have been attracting increasing attention and seen rapid development. However, in previous studies, pioneering work on charging issues and data gathering strategies are always discussed separately. In this paper, we aimed to develop a strategy which can guarantee a short data acquisition cycle and high charging efficiency. We used a new Combined Recharging and Collecting Data Model (CRCM) to set-up a WRSN. In this network, mobile chargers were used to separately collected data and charge sensor nodes. A K-means algorithm was used to group sensor nodes into different clusters, from which mobile chargers collected data from sensor nodes in the center of these clusters. The Nearest-Job-Next with Preemption (NJNP) algorithm was used to determine the charging route. The data acquisition cycle was first discussed in this model in order to ensure all data from the sensor nodes could be gathered within a certain time period. Additionally, the Periodically Restricted Dynamic Mobile Chargers (PRDMCs) algorithm was proposed to determine the number of mobile chargers. Lastly, we used the normal CRCM for comparison with our new CRCM, and the results showed that the new CRCM can effectively safeguard the data acquisition cycle without requiring the addition of more mobile chargers.

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A Novel Improved Bat Algorithm Based on Hybrid Parallel and Compact for Balancing an Energy Consumption Problem

Cited by 28 publications

References 34 publications

A Novel Pigeon-Inspired Optimization Based MPPT Technique for PV Systems

A Novel Pigeon-Inspired Optimization Based MPPT Technique for PV Systems

Identifying correctness data scheme for aggregating data in cluster heads of wireless sensor network based on naive Bayes classification

A New on-Demand Recharging Strategy Based on Cycle-Limitation in a WRSN

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