Swarm bat algorithm with improved search (SBAIS)

Chaudhary, Reshu; Banati, Hema

doi:10.1007/s00500-018-03688-4

Cited by 20 publications

(11 citation statements)

References 41 publications

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“…The average convergence time associated with the BA with control parameters obtained from the BA‐BA strategy is 0.849 second. So, the reduction of average convergence time from the B 3 26 compared to the BA‐BA strategy is (6.034 − 0.849)/0.849 * 100 = 610%. Similarly, the reduction of average convergence time of using the BA‐BA strategy compared to the PSO and CS algorithm are 518% and 395%, respectively.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…The swarm‐sizes used with the BA‐BA strategy are obtained from Table 2. The results obtained from Table 4 show the best benchmark strategy is B 3 26 with 6.034 seconds average convergence time, meanwhile, the worst benchmark strategy is B 9 31 with 69 seconds average convergence time. It is also clear from Table 4 that, the convergence time obtained from the BA‐BA strategy is less than 1 second which is substantially lower than the ones obtained from all benchmark strategies (B 1 ‐B 10 ).…”

Section: Simulation Resultsmentioning

confidence: 99%

“…The results obtained from using the PSO and CS algorithm when they are used for a different number of peaks are shown in Table 4 to easily compare their results with the state of the art BA and the values from the BA‐BA strategy. It is clear from Table 4 that the best average BA strategy is B 3 26 with a convergence time equal to 6.034 seconds, meanwhile, the average PSO and CS convergence time are 5.25 and 4.2, respectively. The average convergence time associated with the BA with control parameters obtained from the BA‐BA strategy is 0.849 second.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…If the rate of pulse emission r i that can be obtained from (7) is less than a random number, then the new position d j k should be updated with the new position shown in (8) 11,21 . The value of r j is called the pulse transmission and can be determined from (7).

{r_{j}}^{k} = r_{0} ([], 1 - e^{(- γ j)})

where, the value of γ is exponential constant and its value is about 0.9 in many kinds of research 21,26

{d_{j}}^{k} = d_{best} + italicεϕ \overset{A}{true}

where, ε is a random number , ε ∈[−1,1], 11,21 and ϕ is used to enhance the stability or limitations to the number walk around the best solution,

\overset{A}{true}

is called the average loudness of bats and its value equal to the average of A in the current iteration.…”

Section: The Ba‐ba Strategymentioning

confidence: 99%

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Optimal control parameters for bat algorithm in maximum power point tracker of photovoltaic energy systems

Eltamaly

2021

Int Trans Electr Energ Syst

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Summary Partial shading conditions generate multiple peaks in the P‐V curve of photovoltaic (PV) arrays. Smart MPPT optimization techniques should be used to capture the global peak (GP) and avoid being trapped in one of the local peaks (LPs). The tracking of the GP should be fast and reliable to enhance the stability and increase the generated efficiency of the PV systems. Bat algorithm (BA) is one of the fastest swarm optimization techniques. The BA control parameters (BA‐CPs) have substantial effects on their performance. This paper introduced a nested BA strategy called BA‐BA strategy to determine the optimal values of control parameters of BA for the lowest convergence time and failure convergence rate to be used in the online MPPT of PV systems. The inner BA loop used the BA as an MPPT of the PV system, meanwhile, the outer BA loop used the inner BA loop as a fitness function to determine the optimal BA‐CPs for minimum convergence time and failure rate. Ten benchmark BA strategies, particle swarm optimization (PSO), and cuckoo search (CS) algorithm have been used to compare their results with the results obtained from the BA‐BA strategy. The results of the BA‐BA strategy reduced the convergence time of 250% of the time associated with the best benchmark BA strategy, 518%, and 395% as compared to the PSO, and CS algorithm, respectively. The simulation and experimental results obtained from the BA‐BA strategy showed its superior for determining the optimal control parameters for BA in MPPT of PV systems or any other applications.

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Section: Simulation Resultsmentioning

confidence: 99%

Section: Simulation Resultsmentioning

confidence: 99%

Section: Simulation Resultsmentioning

confidence: 99%

{r_{j}}^{k} = r_{0} ([], 1 - e^{(- γ j)})

where, the value of γ is exponential constant and its value is about 0.9 in many kinds of research 21,26

{d_{j}}^{k} = d_{best} + italicεϕ \overset{A}{true}

where, ε is a random number , ε ∈[−1,1], 11,21 and ϕ is used to enhance the stability or limitations to the number walk around the best solution,

\overset{A}{true}

is called the average loudness of bats and its value equal to the average of A in the current iteration.…”

Section: The Ba‐ba Strategymentioning

confidence: 99%

See 2 more Smart Citations

Optimal control parameters for bat algorithm in maximum power point tracker of photovoltaic energy systems

Eltamaly

2021

Int Trans Electr Energ Syst

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show abstract

“…Initialization Parameters OCS [5] Random pa = 0.25, β = 1.5, α = 0.8 ICS Equation (10) pa = 0.05, β = 1.5, α = 0.1 BA [28] Equation 10fmin = 0, fmax = 1, ω = 0.5, A0 = 0.85, r0 = 0.85, α = 0.75, γ = 0.6 PSO [29] Equation (10) ω = 0.42, cl = 1.55, cg = 1.55 GWO [30] Equation (10) a from 2 to 0 and r1, r2 = random [0, 1] ABC [31] Equation (10) 50% employed bees and 50% onlooker bees…”

Section: Techniquementioning

confidence: 99%

An Improved Cuckoo Search Algorithm for Maximum Power Point Tracking of Photovoltaic Systems under Partial Shading Conditions

Eltamaly

2021

Energies

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The problem of partial shading has serious effects on the performance of photovoltaic (PV) systems. Adding a bypass diode in shunt to each PV module avoids hot-spot phenomena, but causes multi-peaks in the power–voltage (P–V) characteristics of the PV array, which cause traditional maximum power point tracking (MPPT) techniques to become trapped in local peaks. This problem has forced researchers to search for smart techniques to track global peaks and prevent the possibility of convergence at local peaks. Swarm optimization techniques have been used to fill this shortcoming; unfortunately, however, these techniques suffer from unacceptably long convergence time. Cuckoo search (CS) is one of the fastest and most reliable optimization techniques, making it an ideal option to be used as an MPPT of PV systems under dynamic partial shading conditions. The standard CS algorithm has a long conversion time, high failure rate, and high oscillations at steady state; this paper aims to overcome these problems and to fill this research gap by improving the performance of the CS. The results obtained from this technique are compared to five swarm optimization techniques. The comparison study shows the superiority of the improved CS strategy introduced in this paper over the other swarm optimization techniques.

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LBR‐GWO: Layered based routing approach using grey wolf optimization algorithm in wireless sensor networks

Dwivedi¹,

Patro²,

Srivastava³

et al. 2021

Concurrency and Computation

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The sensors deployed inside a wireless sensor network (WSN) have limited energy sources, significantly impacting the network throughput. This article's research objective is to increase network lifetime by developing an energy-efficient layered-based routing algorithm for WSNs using the grey wolf optimization (LBR-GWO) algorithm. In this, the grey wolf's leadership hierarchy has followed, which improves the network's energy capability. The entire region of the deployed nodes divides into four layers. In these nodes, layer one is chosen as cluster heads. If more than two nodes are present in layer one, then the cluster head is selected based on the game theory model; otherwise, the decision is made based on the node's residual energy. While the existing algorithm has several complex control parameter points, the current algorithm has fewer complex parameters. Therefore, in comparison to other algorithms, this algorithm is easy to apply in cluster-based sensor networks. Simulation findings prove the LBR-GWO algorithm supremacy for balancing energy consumption across the nodes and improving the network's lifetime compared to the LEACH, HEED, and PSO protocols.

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Swarm bat algorithm with improved search (SBAIS)

Cited by 20 publications

References 41 publications

Optimal control parameters for bat algorithm in maximum power point tracker of photovoltaic energy systems

Optimal control parameters for bat algorithm in maximum power point tracker of photovoltaic energy systems

An Improved Cuckoo Search Algorithm for Maximum Power Point Tracking of Photovoltaic Systems under Partial Shading Conditions

LBR‐GWO: Layered based routing approach using grey wolf optimization algorithm in wireless sensor networks

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