Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm

Mirjalili, Seyedali

doi:10.1016/j.knosys.2015.07.006

Cited by 3,306 publications

(1,496 citation statements)

References 81 publications

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“…This algorithm has been proved better than various existing state of art techniques [1]. The MFOA can be applied for the routing in the wireless sensor network (WSN).…”

Section: Moth Flame Based Routing Algorithm In Wireless Sensor Networmentioning

confidence: 99%

“…The routing of the moth comprises of straight as well as the spiral motion depending upon whether the moon(destination) is far or near from the current position of moth. This phenomenon of routing of the moth towards the moon can be used to in sensor network to transfer the data from source to the destination in an optimized manner [1]. This section describes the moth-flame optimization algorithm as follow In the MFOA, n moths are travelling towards the moon in d dimensions then the position matrix can be given shown as eq.…”

Section: Moth -Flame Optimization (Mfo) Algorithmmentioning

confidence: 99%

“…Where the total number of iterations and the current iteration number is given by the respectively [1]. This process is used attain the global optima.…”

Section: Moth -Flame Optimization (Mfo) Algorithmmentioning

confidence: 99%

“…The Moth-flame optimization algorithm (MFOA) [1] depicts the nature of butterfly to fly towards moon. This algorithm has been proved better than various existing state of art techniques [1].…”

Section: Moth Flame Based Routing Algorithm In Wireless Sensor Networmentioning

confidence: 99%

See 3 more Smart Citations

A survey on natural computing techniques

2017

IJLTET

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“…This algorithm has been proved better than various existing state of art techniques [1]. The MFOA can be applied for the routing in the wireless sensor network (WSN).…”

Section: Moth Flame Based Routing Algorithm In Wireless Sensor Networmentioning

confidence: 99%

Section: Moth -Flame Optimization (Mfo) Algorithmmentioning

confidence: 99%

“…Where the total number of iterations and the current iteration number is given by the respectively [1]. This process is used attain the global optima.…”

Section: Moth -Flame Optimization (Mfo) Algorithmmentioning

confidence: 99%

Section: Moth Flame Based Routing Algorithm In Wireless Sensor Networmentioning

confidence: 99%

See 2 more Smart Citations

A survey on natural computing techniques

2017

IJLTET

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“…One of them is a metaheuristic (MH). The term metaheuristics can cover nature-inspired optimisers [1][2][3][4][5][6][7][8][9][10], swarm intelligent algorithms [11][12][13][14][15][16][17][18][19][20], and evolutionary algorithms [21][22][23][24]. Most of them are based on using a set of design solutions, often called a population, for searching an optimum.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Distribution Algorithm Using Correlation between Binary Elements: A New Binary-Code Metaheuristic

Pholdee

Bureerat

2017

Mathematical Problems in Engineering

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A new metaheuristic called estimation of distribution algorithm using correlation between binary elements (EDACE) is proposed. The method searches for optima using a binary string to represent a design solution. A matrix for correlation between binary elements of a design solution is used to represent a binary population. Optimisation search is achieved by iteratively updating such a matrix. The performance assessment is conducted by comparing the new algorithm with existing binary-code metaheuristics including a genetic algorithm, a univariate marginal distribution algorithm, population-based incremental learning, binary particle swarm optimisation, and binary simulated annealing by using the test problems of CEC2015 competition and one real-world application which is an optimal flight control problem. The comparative results show that the new algorithm is competitive with other established binary-code metaheuristics.

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Grid frequency enhancement using coordinated action of wind unit with redox flow battery in a deregulated electricity market

Dhundhara

Verma

2020

Int Trans Electr Energ Syst

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Summary Growing electricity demand, environmental issues, and challenge to decrease dependence on fossil fuel resources have increased the inception of wind generation into the power system. However, higher penetration of wind‐based generating units into the existing grid can affect the working of the power system. Traditionally, a wind unit does not provide inertia, but collectively, wind units can have a notable impact on the dynamic performance of the power system. However, the battery energy storage systems have the potential to offer flexibility and ancillary supports to the power system. This article evaluates the impact of redox flow battery (RFB) in coordination with a doubly fed induction generator (DFIG)–based wind turbine unit (WTU) to enrich the dynamic performances of a multi‐source interconnected power system in a deregulated electricity market. The modified inertial control scheme is proposed for the DFIG unit that responds in the event of frequency deviation in the grid. The turbine sheds its kinetic energy and provides active power injection, thereby enhancing the frequency response of the system. The recently developed moth‐flame optimization (MFO) algorithm is employed for optimal tuning of the proportional‐integral (PI) controller and the speed regulator of a DFIG‐WTU. The simulation studies have been executed to analyze the impact of the RFB with WTU on the system frequency, tie‐line and different generating units power through a comparative study in terms of the settling time, and peak overshoot/undershoot in a deregulated electricity market. The analysis reveals that the WTU effectively contributes to sustaining the frequency and tie‐line power oscillations during abrupt load disturbances in the proposed power system. Moreover, the inclusion of RFB in coordination with the WTU helps to reduce the stress on a wind turbine during inertial control scheme. It can also reduce wind curtailments by absorbing excess power flowing through transmission lines, reduces wastage of green energy, and gives better dynamic response under different operating conditions of the deregulated electricity market.

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Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm

Cited by 3,306 publications

References 81 publications

A survey on natural computing techniques

A survey on natural computing techniques

Estimation of Distribution Algorithm Using Correlation between Binary Elements: A New Binary-Code Metaheuristic

Grid frequency enhancement using coordinated action of wind unit with redox flow battery in a deregulated electricity market

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