A Comprehensive Review on Bacteria Foraging Optimization Technique

Rani, B. Selva; Cherukuri, Aswani Kumar

doi:10.1007/978-3-662-46309-3_1

Cited by 8 publications

(3 citation statements)

References 51 publications

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“…where C(n) indicates the count of steps moved by bacteria randomly represented by tumble process, φ(i) represents the tumble generating a unit length randomly, and θ n represents the position of the nth bacterium at the ith chemotactic, jth reproduction steps, and kth elimination dispersal event [24].…”

Section: Hdmentioning

confidence: 99%

Optimization of Switching Control and Microgrid Energy Management System with Alternate Arm Converter Based on Bacterial Foraging Algorithm

Rama,

Rajini

2023

International Transactions on Electrical Energy Systems

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The global demand of electrical power has increased enormously due to various reasons such as fast-changing and challenging technologies, climatic change, economic growth, and lifestyle of mankind. Due to global warming issues and the depletion of conventional sources of power generation, the utilization of renewable energy sources has drastically increased. So, many challenges exist in integrating the microgrid with AC grid and load. The alternative arm converter (AAC) is among the most innovative converter topologies used in high voltage direct current (HVDC) applications. This research work presents a new control strategy to generate pulses to trigger the switches in the AAC in a proper sequence to obtain a smooth output waveform. The AAC output is controlled by implementing various controllers such as the proportional-integral-derivative (PID) controller, fractional order PID (FOPID) controller, and FOPID controller tuned by metaheuristic algorithm-bacterial foraging optimization technique (BFOT). Also, a comparative analysis is performed based on the spectral analysis of the output voltage obtained. In comparison to other controllers, the FOPID controller optimized by the bacterial foraging optimization technique (BFOT) produced the least total harmonic distortion (THD) of the AAC output voltage. In addition, this paper also discusses about the performance and analysis on the design of an energy management system (EMS) to optimally utilize the energy sources such as PV system, wind system, and battery based on their availability which feed the AAC. The energy management system controls the entire integrated system in association with an integrated CUK-SEPIC converter to fulfil the load demand at the point of common coupling, either from the microgrid or the AC grid. A nine-level AAC model is designed integrating the microgrid, grid, and industrial loads with an energy management system using MATLAB/Simulink. The performance parameters of the entire model are analysed at every stage to provide stabilized output to meet the load demand.

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Section: Hdmentioning

confidence: 99%

Optimization of Switching Control and Microgrid Energy Management System with Alternate Arm Converter Based on Bacterial Foraging Algorithm

Rama,

Rajini

2023

International Transactions on Electrical Energy Systems

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“…The main criteria to differentiate one metaheuristic from the other is how an algorithm achieves this balance. The existing metaheuristic may be grouped into broad categories as evolutionary algorithms (EA) [3,4], swarm intelligence-based algorithms (SIA) [5][6][7][8][9][10][11][12][13], ecology-based algorithms (ECOA) [14][15][16], and physical science-based algorithms (PSA) [17][18][19][20][21].…”

Section: Introductionmentioning

confidence: 99%

“…SIAs utilize the concept of self-organized and group behavior during the optimization process. Popular examples of SIAs are ant colony optimization (ACO) [5], which is based on the process of ants seeking the shortest path between colony and food source; particle swarm optimization (PSO) [6,7], inspired by the social behavior of fish schooling or bird flocking; artificial bee colony (ABC) optimization [8], inspired by the intelligent foraging behavior of honey bee swarm; the firefly algorithm (FFA) [9], inspired by the flashing behavior of fireflies; the krill herd algorithm (KHA) [10] simulates the heading behavior of krill; the bacterial foraging algorithm (BFA) [11], inspired by the social foraging behavior of E Coli bacteria; whale optimization (WO) [12] simulates the bubble-net hunting mechanism of humpback whales; grey wolf optimization (GWO) [13] simulates the leadership and social hierarchy of grey wolf when hunting prey.…”

Section: Introductionmentioning

confidence: 99%

Modified Quasi-Opposition-Based Grey Wolf Optimization for Mathematical and Electrical Benchmark Problems

Dubey¹,

Dubey²,

Salkuti

2022

Energies

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This paper proposes a modified quasi-opposition-based grey wolf optimization (mQOGWO) method to solve complex constrained optimization problems. The effectiveness of mQOGWO is examined on (i) 23 mathematical benchmark functions with different dimensions and (ii) four practical complex constrained electrical problems that include economic dispatch of 15, 40, and 140 power generating units and a microgrid problem with different energy sources. The obtained results are compared with the reported results using other methods available in the literature. Considering the solution quality of all test cases, the proposed technique seems to be a promising alternative for solving complex constrained optimization problems.

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Towards a Reference Architecture for Swarm Intelligence-Based Internet of Things

Zedadra

Savaglio

Jouandeau

et al. 2018

Internet and Distributed Computing Systems

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The Internet of Things (IoT) represents the global network which interconnects digital and physical entities. It aims at providing objects with intelligence that allows them to perceive, decide and cooperate with other objects, machines, systems and even humans to enable a whole new class of applications and services. Agent-Based Computing paradigm has been exploited to deal with the IoT system development. Many research works focus on making objects able to think by themselves thus imitating human brain. Swarm Intelligence studies the collective behavior of systems composed of many individuals who interact locally with each other and with their environment using decentralized and selforganized control to achieve complex tasks. Swarm intelligence-based systems provide decentralized, self-organized and robust systems with consideration of coordination frameworks. We explore in this paper the exploitation of swarm intelligence-based features in IoT-based systems. Therefore, we present a reference swarm-based architectural model that enables cooperation among devices in IoT systems.

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A Comprehensive Review on Bacteria Foraging Optimization Technique

Cited by 8 publications

References 51 publications

Optimization of Switching Control and Microgrid Energy Management System with Alternate Arm Converter Based on Bacterial Foraging Algorithm

Optimization of Switching Control and Microgrid Energy Management System with Alternate Arm Converter Based on Bacterial Foraging Algorithm

Modified Quasi-Opposition-Based Grey Wolf Optimization for Mathematical and Electrical Benchmark Problems

Towards a Reference Architecture for Swarm Intelligence-Based Internet of Things

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