Improved moth flame optimization algorithm based on opposition-based learning and Lévy flight distribution for parameter estimation of solar module

Sharma, Abhishek; Sharma, Abhinav; Averbukh, Moshe; Rajput, Shailendra; Jately, Vibhu; Choudhury, Sushabhan; Azzopardi, Brian

doi:10.1016/j.egyr.2022.05.011

Cited by 32 publications

(12 citation statements)

References 49 publications

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“…Table 4 presents the controller gains of link 1, link 2, and fitness function value. To access the performance of these metaheuristic optimization algorithms, a non-parametric statistical test called Friedman's test (Sharma et al, 2022b) has been performed. Each of these techniques has been assigned a Friedman ranking based on that final ranking has been given.…”

Section: Resultsmentioning

confidence: 99%

Trajectory Control of Robotic Manipulator using Metaheuristic Algorithms

Rawat

Gupta

Sharma

2023

Int. j. math. eng. manag. sci.

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Robotic manipulators are extremely nonlinear complex and, uncertain systems. They have multi-input multi-output (MIMO) dynamics, which makes controlling manipulators difficult. Robotic manipulators have wide applications in many industries like processes, medicine, and space. Effective control of these manipulators is extremely important to perform these industrial tasks. Researchers are working on the control of robotic manipulators using conventional and intelligent control methods. Conventional control methods are proportional integral and derivative (PID), Fractional order proportional integral and derivative (FOPID), sliding mode control (SMC), and optimal & robust control while intelligent control method includes Artificial Neural network (ANN), Fuzzy logic control (FLC) and metaheuristic optimization algorithms based control schemes. This paper presents the trajectory control of a robotic manipulator using a PID controller. Four different meta-heuristic algorithms namely Sooty tern optimization (STO), Spotted Hyena optimizer (SHO), Atom Search optimization (ASO), and Arithmetic Optimization algorithm (AOA) are used to optimize the gains of PID controller for trajectory control of a two-link robotic manipulator and a novel hybrid sooty tern and particle swarm optimization (STOPSO) has been designed. These optimization techniques are nature-inspired algorithms that give the optimal gain values while minimizing the performance indices. A performance index comprising Integral time absolute error (ITAE) having weights for both links has been considered to achieve the desired trajectory. These optimization techniques are stochastic in nature so statistical analysis and Freidman’s ranking test has been performed to evaluate the effectiveness of these algorithms. The proposed hybrid STOPSO provided a fitness value of 0.04541 and showed a standard deviation of 0.0002. A comparative study of these optimization techniques is presented and as a result, hybrid STOPSO provides the best results with minimum fitness value followed by STO, AOA, ASO, and SHO algorithms.

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Section: Resultsmentioning

confidence: 99%

Trajectory Control of Robotic Manipulator using Metaheuristic Algorithms

Rawat

Gupta

Sharma

2023

Int. j. math. eng. manag. sci.

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“…This subsection experiments on the proposed Es-MFO algorithm using CEC 17 functions. It is then compared to six recent algorithms, which are Fire Hawk Optimization (FHO) by Azizi et al (2023) , Arithmetic Optimization Algorithm (AOA) by Abualigah et al (2021) , Artificial Gorilla Troops Optimizer (GTO) by Abdollahzadeh et al (2021) , Multi Population-Based Adaptive Sine Cosine Algorithm (MAMSCA) by Saha (2022), Quantum Mutation Based Backtracking Search Algorithm (gQR-BSA) by Nama et al (2022) , and mLBOA by Sharma et al (2022) . The results of all algorithms are presented in Table 9 , displaying the mean and standard deviation.…”

Section: Simulation Study and Discussionmentioning

confidence: 99%

“…MFO is a versatile algorithm with minimal algorithm-specific parameters, making it suitable for real-world problems. For example, it has been applied successfully to tasks such as parameter estimation for solar modules ( Sharma et al, 2022 ), flexible operation modeling ( Hou et al, 2022 ), intelligent route planning for multiple UAVs ( Ma et al, 2022 ), deep learning ( Khan et al, 2022 ), machine scheduling problems ( Mohd Rose and Nik Mohamed, 2022 ), neural network optimization ( Ramachandran et al, 2021 ), and many more. The MFO is a promising new population-based optimization method.…”

Section: Introductionmentioning

confidence: 99%

Self-adaptive moth flame optimizer combined with crossover operator and Fibonacci search strategy for COVID-19 CT image segmentation

Sahoo¹,

Houssein²,

Premkumar³

et al. 2023

Expert Systems with Applications

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“…This objective function solely applies to thermal power generation, which emits greenhouse gases SOx, NOx, and CO 2 out into the air. The Equation 11shows some ways to reduce emissions [37]: 11) In this case αi, βi, γi, ωi and μi represent the exhalation factor for the i-th generator, including the valve loading impact lowering the power generation and pollution. Putting in place a carbon price to decrease emissions of greenhouse gases is the third objective that needs to be investigated.…”

Section: 5generation Of Electricity and Emission Reductionsmentioning

confidence: 99%

Economic power dispatch solutions incorporating stochastic wind power generators by moth flow optimizer

2023

IJATEE

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Numerous generating units, load locations, and tens of thousands of buses contribute to the vastness and integration of today's electrical networks. Many different solutions have been put up to deal with issues relating to optimal power flow (OPF). In a typical power flow, the values of the control variables are given. A set goal can only be enhanced (minimized or maximized) in the event that all or some of the control variables in an OPF are understood. *Author for correspondenceThe OPF computation has numerous applications, including power systems, real-time control, operational planning, and problems [1].OPF is used by a lot of modern energy management systems (EMS). With increasing power system scale and interconnection complexity, OPF is becoming more important [2] in a neural network. The OPF should, for instance, help with deregulation transactions or make suggestions regarding the type of reinforcement that is required. An improvement in the utilization of an existing asset (such as generating or transmitting) or the provision of a less expensive substitute for the construction of new facilities is

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Improved moth flame optimization algorithm based on opposition-based learning and Lévy flight distribution for parameter estimation of solar module

Cited by 32 publications

References 49 publications

Trajectory Control of Robotic Manipulator using Metaheuristic Algorithms

Trajectory Control of Robotic Manipulator using Metaheuristic Algorithms

Self-adaptive moth flame optimizer combined with crossover operator and Fibonacci search strategy for COVID-19 CT image segmentation

Economic power dispatch solutions incorporating stochastic wind power generators by moth flow optimizer

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