Hybridizing bat algorithm with artificial bee colony for combined heat and power economic dispatch

Murugan, R.; Mohan, M.R.; Rajan, C.; Sundari, P. Deiva; Sundaram, Arunachalam

doi:10.1016/j.asoc.2018.06.034

Cited by 77 publications

(38 citation statements)

References 49 publications

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“…In the years since these pioneering works, the imaginativeness of researchers has continued to flourish. New metaheuristic methods have been applied to the CHP dispatch problem such as cuckoo search [23][24][25][26], invasive weed optimization [27], the artificial immune system algorithm [28], the firefly algorithm [29], krill herd optimization [30], the harmony search algorithm [31][32][33][34], grey wolf optimization [35,36], whale optimization [37], and the hybridizing bat algorithm with artificial bee colony [38]. This is by no means an exhaustive list of metaheuristic approaches investigated.…”

Section: Optimal Combined Heat and Power Dispatchmentioning

confidence: 99%

“…Beigvand et al (2016) reference the convergence difficulties of more classical optimization techniques due to the structure of the CHP-ED dispatch problem [63]. Murugan et al (2018) reference the ineffectiveness of derivative-based optimization approaches at solving nonconvex problems with complex search spaces and computationally expensive cost functions [38]. Zou et al (2019) state that the classical numerical methods can hardly handle problems which simultaneously have nonconvexity, discontinuity and non-differentiability [64].…”

Section: Optimal Combined Heat and Power Dispatchmentioning

confidence: 99%

“…A rippling effects is seen in the cost curve as steam admission valves open intermittently across the operating region, as seen in Figure 1 [68,69]. As a result, in more recent CHP-ED literature its is common to consider a more accurate cost function for power-only units which includes a rectified sinusoidal term, as follows [37,38,62,[70][71][72]: Consideration of valve-point effects introduces nonconvexity into the objective function of the CHP model. Without consideration of valve-point effects, the cost functions are all convex quadratic functions.…”

Section: Parameter Description Unitmentioning

confidence: 99%

See 2 more Smart Citations

A Critical Review of the Modeling and Optimization of Combined Heat and Power Dispatch

Kazda¹,

Li²

2020

Processes

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Combined heat and power (CHP) systems are attracting increasing attention for their ability to improve the economics and sustainability of the electricity system. Determining how to best operate these systems is difficult because they can consist of many generating units whose operation is governed by complex nonlinear physics. Mathematical programming is a useful tool to support the operation of CHP systems, and has been the subject of substantial research attention since the early 1990s. This paper critically reviews the modeling and optimization work that has been done on the CHP economic dispatch problem, and the CHP economic and emission dispatch problem. A summary of the common models used for these problems is provided, along with comments on future modeling work that would beneficial to the field. The majority of optimization approaches studied for CHP system operation are metaheuristic algorithms. A discussion of the limitations and benefits of metaheuristic algorithms is given. Finally, a case study optimizing five classic CHP system test instances demonstrates the advantages of the using deterministic global search algorithms over metaheuristic search algorithms.

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Section: Optimal Combined Heat and Power Dispatchmentioning

confidence: 99%

Section: Optimal Combined Heat and Power Dispatchmentioning

confidence: 99%

Section: Parameter Description Unitmentioning

confidence: 99%

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A Critical Review of the Modeling and Optimization of Combined Heat and Power Dispatch

Kazda¹,

Li²

2020

Processes

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“…In fact, it was initially developed to optimize numerical and continues problems where various studies demonstrated it robustness in solving such problems [6]. Furthermore, several hybridized versions of Bat algorithm were proposed for solving this type of problems such as [ hand, it has been modified by various researchers in order to adopt it for solving combinatorial problems such as Capacitated Vehicle Routing Problem [10] and optimization in industry such as [11].…”

Section: A Bat Algorithmmentioning

confidence: 99%

A Comparative Study of Recent Swarm Intelligence Approaches on Global Optimization

Alharbi¹

2019

IJMO

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This paper presents a comparative study of three Swarm Intelligence approaches which are: Bat Algorithm (BA), Firefly Algorithm (FA) and Artificial Bee Colony (ABC) Algorithm applied to a set of standard benchmark functions. The results of this study were analyzed and compared on the basis of mean value of obtained objective values. All of these approaches were investigated taking into consideration several dimensionalities which are 10, 20,30, 50 and 100. Statistical results indicated that ABC significantly surpassed BA and FA on the majority of the experimental instances and FA was able to significantly archive better objective values than BA in most of the test cases. The paper finally concludes with some future work directions.

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“…The Artificial Bee Colony (ABC) algorithm [ 57 ], proposed by Karaboga in 2005, is one of the most recent swarm intelligence based optimization technique, which simulates the foraging behavior of a honey bee swarm. The search equation of ABC is reported to be good at exploration but poor at exploitation [ 58 , 59 ]. To overcome this limitation we have modified the ABC algorithm by incorporating the concept of pheromone which is one of the major components of the Ant Colony Optimization (ACO) algorithm [ 60 , 61 ] and a new operation in which successive bees communicate to share their results.…”

Section: Introductionmentioning

confidence: 99%

Gene selection for cancer classification with the help of bees

Moosa¹,

Shakur

Kaykobad³

et al. 2016

BMC Med Genomics

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BackgroundDevelopment of biologically relevant models from gene expression data notably, microarray data has become a topic of great interest in the field of bioinformatics and clinical genetics and oncology. Only a small number of gene expression data compared to the total number of genes explored possess a significant correlation with a certain phenotype. Gene selection enables researchers to obtain substantial insight into the genetic nature of the disease and the mechanisms responsible for it. Besides improvement of the performance of cancer classification, it can also cut down the time and cost of medical diagnoses.MethodsThis study presents a modified Artificial Bee Colony Algorithm (ABC) to select minimum number of genes that are deemed to be significant for cancer along with improvement of predictive accuracy. The search equation of ABC is believed to be good at exploration but poor at exploitation. To overcome this limitation we have modified the ABC algorithm by incorporating the concept of pheromones which is one of the major components of Ant Colony Optimization (ACO) algorithm and a new operation in which successive bees communicate to share their findings.ResultsThe proposed algorithm is evaluated using a suite of ten publicly available datasets after the parameters are tuned scientifically with one of the datasets. Obtained results are compared to other works that used the same datasets. The performance of the proposed method is proved to be superior.ConclusionThe method presented in this paper can provide subset of genes leading to more accurate classification results while the number of selected genes is smaller. Additionally, the proposed modified Artificial Bee Colony Algorithm could conceivably be applied to problems in other areas as well.Electronic supplementary materialThe online version of this article (doi:10.1186/s12920-016-0204-7) contains supplementary material, which is available to authorized users.

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Hybridizing bat algorithm with artificial bee colony for combined heat and power economic dispatch

Cited by 77 publications

References 49 publications

A Critical Review of the Modeling and Optimization of Combined Heat and Power Dispatch

A Critical Review of the Modeling and Optimization of Combined Heat and Power Dispatch

A Comparative Study of Recent Swarm Intelligence Approaches on Global Optimization

Gene selection for cancer classification with the help of bees

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