Equilibrium optimizer: A novel optimization algorithm

Faramarzi, Afshin; Heidarinejad, Mohammad; Stephens, Brent; Mirjalili, Seyedali

doi:10.1016/j.knosys.2019.105190

Cited by 1,397 publications

(689 citation statements)

References 34 publications

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“…In this paper, we have made an attempt to address the FS problem using a recently proposed optimization algorithm known as Equilibrium Optimizer (EO) [23]. It mimics the process of control volume mass balance used to predict equilibrium and dynamic phases where the equilibrium state is treated as the optimal solution to the optimization problem.…”

Section: One Of the Most Sought Areas After Dimension Reduction Procementioning

confidence: 99%

“…EO is one of the most recent algorithms in the domain of optimization. Faramarzi et al have proposed EO in [44]. EO uses the concept of control volume mass balance where each particle represents a solution and its concentration represents the position.…”

Section: Literature Studymentioning

confidence: 99%

“…Equillibrium Optimizer (EO), first proposed in [44], is a physics based optimization algorithm inspired by dynamic source and sink models for estimating equilibrium states. This method is based upon simple well mixed dynamic mass balance on a control volume, where a mass balance equation is used to determine the concentration of a nonreactive constituent in a control volume as a function of its various source and sink mechanisms.…”

Section: Equillibrium Optimization: An Overviewmentioning

confidence: 99%

“…sgn function indicates the direction of the search process. As per [44], we have set a 1 = 2 and a 2 = 1. By substituting Equation 9…”

Section: Equillibrium Optimization: An Overviewmentioning

confidence: 99%

“…where that indicates the probability that informs the number of particles using the last term in Equation 4 to update their states. GP = 0.5 is set balancing the exploitation and exploration [44]. So, finally the concentration updating rule in this algorithm is given by:…”

Section: Generation Rate Gmentioning

confidence: 99%

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BEO: Binary Equilibrium Optimizer Combined with Simulated Annealing for Feature Selection

Ghosh

Guha

Bera

et al. 2020

Preprint

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This work proposed a binary variant of the recently-proposed Equilibrium Optimizer (EO) to solve binary problems. A v-shaped transfer function is used to map continuous values created in EO to binary. To improve the exploitation of the Binary Equilibrium Optimizer (BEO), the Simulated Annealing is used as one of the most popular local search methods. The proposed BEO algorithm is applied to 18 UCI datasets and compared to a wide range of algorithms. The results demonstrate the superiority and merits of EO when solving feature selection problems.

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Section: One Of the Most Sought Areas After Dimension Reduction Procementioning

confidence: 99%

Section: Literature Studymentioning

confidence: 99%

Section: Equillibrium Optimization: An Overviewmentioning

confidence: 99%

“…sgn function indicates the direction of the search process. As per [44], we have set a 1 = 2 and a 2 = 1. By substituting Equation 9…”

Section: Equillibrium Optimization: An Overviewmentioning

confidence: 99%

Section: Generation Rate Gmentioning

confidence: 99%

See 3 more Smart Citations

BEO: Binary Equilibrium Optimizer Combined with Simulated Annealing for Feature Selection

Ghosh

Guha

Bera

et al. 2020

Preprint

Self Cite

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Equilibrium optimizer‐tuned cascade fractional‐order 3DOF‐PID controller in load frequency control of power system having renewable energy resource integrated

Guha

Roy

Banerjee

2020

Int Trans Electr Energ Syst

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PID‐ / FOPID ‐based frequency control of zero‐carbon multisources‐based interconnected power systems underderegulated scenarios

Ebrahim

Becherif

Abdelaziz

2020

Int Trans Electr Energ Syst

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Summary Zero‐carbon multienergy sources (ZCESs) integration into a power system becomes mandatory to reduce energy production costs and fuel‐burning emissions. Although, the energy mix of ZCESs with high penetration levels arises at moderated frequency as well as voltage response deterioration of the interconnected power systems. These response deteriorations stem from the fluctuation nature of ZCESs. Therefore, the necessity for overcoming these performance degradations is not an option for power system planners and operators. Thanks to fractional order (FO) mathematics in conjunction with metaheuristic optimization algorithms (MOAs) as they can be employed to enhance the damping of ZCESs oscillations under energy mix uncertainties. Three‐area solar thermal‐wind‐hydro (STWH) power system, including system's uncertainties such as data telemetry delays and governor dead bandsand generation rate constraints, is considered to accomplish this study. The performance of STWH system equipped with FOPID‐based load frequency controllers tuned by various modern MOAs (FOPID‐based LFCs) has been compared to the classical PID‐LFC controllers governed by the same MOAs, to demonstrate the superior efficiency of the FOPID‐based LFCs. The efficacy of FOPID‐based LFCs and their effectiveness compared to the predecessors are verified during simulation results.

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Equilibrium optimizer: A novel optimization algorithm

Cited by 1,397 publications

References 34 publications

BEO: Binary Equilibrium Optimizer Combined with Simulated Annealing for Feature Selection

BEO: Binary Equilibrium Optimizer Combined with Simulated Annealing for Feature Selection

Equilibrium optimizer‐tuned cascade fractional‐order 3DOF‐PID controller in load frequency control of power system having renewable energy resource integrated

PID‐ / FOPID ‐based frequency control of zero‐carbon multisources‐based interconnected power systems underderegulated scenarios

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