Artificial bee colony optimization for combined economic load and emission dispatch

Dixit, Gaurav Prasad; Dubey, Hari Mohan; Pandit, Manjaree; Panigrahi, B. K.

doi:10.1049/cp.2011.0386

Cited by 28 publications

(12 citation statements)

References 10 publications

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“…In the ABC implementation [39,58], initially scout bees are sent out to scout the problem space. The position of the scout bees follows:…”

Section: 2abc Algorithmmentioning

confidence: 99%

“…If the neighboring food source contains a better solution, the new position is kept. Otherwise the old position is maintained [36,39,[56][57]59]. The equation to select neighboring food source is given by: The info of the new or existing nectar amount then is relayed to awaiting onlookers when the employed bees return.…”

Section: 2abc Algorithmmentioning

confidence: 99%

“…Similar to employed bees, if the neighboring food source contains a better solution, the new position is kept [36,39,[56][57]59]. Otherwise, the old position is maintained.…”

Section: 2abc Algorithmmentioning

confidence: 99%

“…number of iterations with no improvement in results, the search process will be reset to explore new solutions [38][39]. This gives the ability for ABC to escape the local minima and explore new solutions globally when the need arises.…”

Section: Introductionmentioning

confidence: 99%

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Comparison between Binary Particles Swarm Optimization (BPSO) and Binary Artificial Bee Colony (BABC) for nonlinear autoregressive model structure selection of chaotic data

Zabidi¹,

Yassin²,

Tahir³

et al. 2018

J. Fundam and Appl Sci.

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This paper presents a comparison between the Binary Artificial Bee Colony (BABC) and Binary Particle Swarm Optimization (BPSO) algorithm for structure selection of a Nonlinear Auto-Regressive Model (NAR) of the chaotic Mackey optimization algorithms are swarm BPSO mimicking the swarming behavior of birds. Recent research has suggested that the ABC algorithm has better solution quality compared to PSO. However, resea this advantage applies to the structure selection case in system identification has not been investigated. We conduct extensive tests to determine the convergence performance of both algorithms.Author Correspondence, e-mail: . Zabidi et al. J Fundam Appl Sci. 2017, 9(3S), 730-754 731 It found that the BABC had managed to significantly outperform BPSO in terms of convergence consistency with a slight advantage in terms of solution quality.

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“…In the ABC implementation [39,58], initially scout bees are sent out to scout the problem space. The position of the scout bees follows:…”

Section: 2abc Algorithmmentioning

confidence: 99%

Section: 2abc Algorithmmentioning

confidence: 99%

“…Similar to employed bees, if the neighboring food source contains a better solution, the new position is kept [36,39,[56][57]59]. Otherwise, the old position is maintained.…”

Section: 2abc Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Comparison between Binary Particles Swarm Optimization (BPSO) and Binary Artificial Bee Colony (BABC) for nonlinear autoregressive model structure selection of chaotic data

Zabidi¹,

Yassin²,

Tahir³

et al. 2018

J. Fundam and Appl Sci.

View full text Add to dashboard Cite

show abstract

“…When using artificial colony algorithm to solve the optimization problem, the location of the food source was abstracted as the optimal solution, the fitness function value of optimization question determines the quality of the food source. Artificial bees mainly include the employed bees, onlookers and scout bees [14][15]. The fitness function is the energy function of interface circuit.…”

Section: Solving the Zero Value Of Energy Function With Artificial Bementioning

confidence: 99%

Artificial Bee Colony and Neural Networks Optimization Test Generation Algorithm for Multiple Faults of Digital Circuits

Zhao¹,

Bai-sheng²,

Li³

2016

IJUNESST

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A quantum-inspired particle swarm optimization approach for environmental/economic power dispatch problem using cubic criterion function

Mahdi

Vasant

Abdullah-Al-Wadud

et al. 2017

Int Trans Electr Energ Syst

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Summary In electrical power dispatch problem, economic dispatch (ED) and environmental dispatch problems play a crucial part. Economic dispatch problem refers to the minimization of generation cost, where environmental dispatch problem refers to the minimization of emission of pollutants like CO2, SO2, and NOx from the power generation system. A quantum‐inspired particle swarm optimization (QPSO) technique is presented in this paper to solve many‐objective environmental economic dispatch (EED) problems. Emissions of CO2, SO2, and NOx are considered 3 different objectives, thus making it a 4‐objective problem considering ED. Many‐objective EED problems are defined by using a cubic criterion function, and a max/max price penalty factor is considered to convert all the objectives into a single objective to compare the final results with other well‐known methods found in the literature like Lagrangian relaxation, particle swarm optimization, simulated annealing, and quantum‐behaved bat algorithm. Quantum‐inspired particle swarm optimization is implemented on a 6‐unit system to solve many‐objective EED problems, and at the same time, to show the effectiveness of QPSO in large systems, it is also implemented in a 26‐unit power generation system for ED problem. The obtained results demonstrate and verify the effectiveness and robustness of QPSO to solve many‐objective EED problems. This also shows that QPSO can effectively be implemented in such power dispatch problems.

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Artificial bee colony optimization for combined economic load and emission dispatch

Cited by 28 publications

References 10 publications

Comparison between Binary Particles Swarm Optimization (BPSO) and Binary Artificial Bee Colony (BABC) for nonlinear autoregressive model structure selection of chaotic data

Comparison between Binary Particles Swarm Optimization (BPSO) and Binary Artificial Bee Colony (BABC) for nonlinear autoregressive model structure selection of chaotic data

Artificial Bee Colony and Neural Networks Optimization Test Generation Algorithm for Multiple Faults of Digital Circuits

A quantum-inspired particle swarm optimization approach for environmental/economic power dispatch problem using cubic criterion function

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