Bacterial Foraging-Based Algorithm for Optimizing the Power Generation of an Isolated Microgrid

Hernández-Ocaña, Betania; Hernández-Torruco, José; Chávez-Bosquez, Oscar; Calva-Yáñez, María Barbara; Portilla-Flores, Edgar Alfredo

doi:10.3390/app9061261

Cited by 17 publications

(12 citation statements)

References 29 publications

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“…Finally, the most recent adaptation is the Two-Swim Modified Bacterial Foraging Optimization Algorithm (TS-MBFOA), where two swims are added in the chemotaxis process: the first is the original swim (except for the step size, which is randomized), and the second swim includes the mutation operator used in evolutionary algorithms. Both swims are included with the objective of improving the exploration and exploitation capabilities of the algorithm [11]. This proposal has recently been successfully used to solve engineering design problems such as tension/compression spring [12], and menu planning [13].…”

Section: Bacterial Foraging Algorithm (Bfoa)mentioning

confidence: 99%

Bacterial Foraging Based Algorithm Front-end to Solve Global Optimization Problems

Hern醤dez-Oca馻¹,

Garc韆-L髉ez²,

Jos�Hern醤dez-Torruco³

et al. 2022

Intelligent Automation &Amp; Soft Computing

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The Bacterial Foraging Algorithm (BFOA) is a well-known swarm collective intelligence algorithm used to solve a variety of constraint optimization problems with wide success. Despite its universality, implementing the BFOA may be complex due to the calibration of multiple parameters. Moreover, the Two-Swim Modified Bacterial Foraging Optimization Algorithm (TS-MBFOA) is a state-of-the-art modification of the BFOA which may lead to solutions close to the optimal but with more parameters than the original BFOA. That is why in this paper we present the design using the Unified Modeling Language (UML) and the implementation in the MATLAB platform of a front-end for the TS-MBFOA algorithm to calibrate the algorithm parameters faster and with no need for editing lines of code. To test our proposal, we solve a numerical optimization problem with constraints known as tension/compression spring, where 30 independent executions were conducted using the TS-MBFOA and then compared with an earlier version called MBFOA. The runtime configuration and the parameter tuning were fluent using our front-end, and the TS-MBFOA obtained the better results. To date, there is no other user-friendly implementation of this specific algorithm in an open-source code, and the front-end is flexible enough to include other numerical optimization problems with minimal effort.

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Section: Bacterial Foraging Algorithm (Bfoa)mentioning

confidence: 99%

Bacterial Foraging Based Algorithm Front-end to Solve Global Optimization Problems

Hern醤dez-Oca馻¹,

Garc韆-L髉ez²,

Jos�Hern醤dez-Torruco³

et al. 2022

Intelligent Automation &Amp; Soft Computing

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“…A bacterial foraging optimization algorithm (BFOA) to optimize an isolated microgrid (IMG) is proposed in reference [20]. The IMG model includes renewable energy sources and a conventional generation unit.…”

Section: Safety Health and Quality Of Lifementioning

confidence: 99%

Artificial Intelligence Applications to Smart City and Smart Enterprise

Impedovo

Pirlo

2020

Applied Sciences

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Smart cities work under a more resource-efficient management and economy than ordinary cities. As such, advanced business models have emerged around smart cities, which have led to the creation of smart enterprises and organizations that depend on advanced technologies. In this Special Issue, 21 selected and peer-reviewed articles contributed in the wide spectrum of artificial intelligence applications to smart cities. Published works refer to the following areas of interest: vehicular traffic prediction; social big data analysis; smart city management; driving and routing; localization; and safety, health, and life quality.

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“…Moreover, in these works, users are attracted to active participation due to cost minimization without sacrificing UC. The authors introduced a novel concept of user priority in energy management systems via power usage scheduling using DR programs [12][13][14][15][16]. Home appliances with priority as well as thermal and operational constraints enable EMC to turn on and off appliances on a priority basis.…”

Section: Introductionmentioning

confidence: 99%

Efficient Energy Management of IoT-Enabled Smart Homes Under Price-Based Demand Response Program in Smart Grid

Hafeez

Wadud

Khan

et al. 2020

Sensors

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There will be a dearth of electrical energy in the prospective world due to exponential increase in electrical energy demand of rapidly growing world population. With the development of internet-of-things (IoT), more smart devices will be integrated into residential buildings in smart cities that actively participate in electricity market via demand response (DR) programs to efficiently manage energy in order to meet this increasing energy demand. Thus, with this incitement, an energy management strategy using price-based DR program is developed for IoT-enabled residential buildings. We propose a wind-driven bacterial foraging algorithm (WBFA), which is a hybrid of wind-driven optimization (WDO) and bacterial foraging optimization (BFO) algorithms. Subsequently, we devised a strategy based on our proposed WBFA to systematically manage the power usage of IoT-enabled residential building smart appliances by scheduling to alleviate peak-to-average ratio (PAR), minimize cost of electricity, and maximize user comfort (UC). This increases effective energy utilization, which in turn increases the sustainability of IoT-enabled residential buildings in smart cities. The WBFA-based strategy automatically responds to price-based DR programs to combat the major problem of the DR programs, which is the limitation of consumer’s knowledge to respond upon receiving DR signals. To endorse productiveness and effectiveness of the proposed WBFA-based strategy, substantial simulations are carried out. Furthermore, the proposed WBFA-based strategy is compared with benchmark strategies including binary particle swarm optimization (BPSO) algorithm, genetic algorithm (GA), genetic wind driven optimization (GWDO) algorithm, and genetic binary particle swarm optimization (GBPSO) algorithm in terms of energy consumption, cost of electricity, PAR, and UC. Simulation results show that the proposed WBFA-based strategy outperforms the benchmark strategies in terms of performance metrics.

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Bacterial Foraging-Based Algorithm for Optimizing the Power Generation of an Isolated Microgrid

Cited by 17 publications

References 29 publications

Bacterial Foraging Based Algorithm Front-end to Solve Global Optimization Problems

Bacterial Foraging Based Algorithm Front-end to Solve Global Optimization Problems

Artificial Intelligence Applications to Smart City and Smart Enterprise

Efficient Energy Management of IoT-Enabled Smart Homes Under Price-Based Demand Response Program in Smart Grid

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