Hybrid PSO-ACO algorithm to solve economic load dispatch problem with transmission loss for small scale power system

Santra, Dipankar; Mukherjee, Anirban; Sarker, Krishna; Chatterjee, Debasish

doi:10.1109/icicpi.2016.7859707

Cited by 15 publications

(3 citation statements)

References 23 publications

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“…It can be applied for the form error evaluation, which contributes significantly to the production of mechanical components [29]. PSO can also be hybridized by combining with adaptive crossover and mutation rates [30] or by combining PSO with ant colony optimization (ACO) for convex and non-convex economic load dispatch (ELD) problem of a small scale thermal power system [31]. Also, a cooperative PSO (CPSO) is applied for solving function approximation and classification problems with improved accuracy [32].…”

Section: Introductionmentioning

confidence: 99%

A new hybrid teaching–learning particle swarm optimization algorithm for synthesis of linkages to generate path

2017

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This paper proposes a novel hybrid teaching-learning particle swarm optimization (HTLPSO) algorithm, which merges two established nature-inspired algorithms, namely, optimization based on teachinglearning (TLBO) and particle swarm optimization (PSO). The HTLPSO merges the best half of population obtained after the teacher phase in TLBO with the best half of the population obtained after PSO. The population so obtained is used subsequently in learner phase of TLBO. To validate the proposed algorithm, five constrained benchmark functions are considered to prove its robustness and efficiency. The proposed algorithm is applied to synthesize four-bar linkage for prescribed path. It is found that the HTLPSO performs better than other single nature-inspired algorithms for path synthesis problem in mechanism theory. Hence, HTLPSO may prove to be an important tool for mechanism design to follow the prescribed path.

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Section: Introductionmentioning

confidence: 99%

A new hybrid teaching–learning particle swarm optimization algorithm for synthesis of linkages to generate path

2017

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“…For example, local optima problem in PSO is avoided using ABC algorithm for effort estimation of software projects, 41 the searching strategy of Cuckoo Search is combined with PSO for solving engineering optimization problems, 42 local optima problem in PSO is avoided using ACO algorithm for solving load dispatch problem in small scale power system. 43 The advantage of having hyper-parameters with more decimal places can be recalled from Section 5.5. To incorporate this advantage, PSO-based high precision hyperparameter updating method is proposed.…”

Section: Pso As High-precision Hyperparameter Updating Methodsmentioning

confidence: 99%

DementiaMRIimage classification using transformation technique based on elephant herding optimization with Randomized Adam method for updating the hyper‐parameters

Bharanidharan

Rajaguru²

2020

Int J Imaging Syst Tech

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The primary objective of this research work is to build a binary classifier for categorizing the input brain magnetic resonanceimaging (MRI) images as either demented or nondemented with high accuracy. A novel hyperparameter updating method called Randomized Adam (RanAdam) is proposed for enhancing the dementia classification accuracy of elephant herding optimization algorithm and other swarm intelligence (SI) algorithms. Usually, Adam method is widely used in deep learning neural networks for hyper-parameters updating, and it is ingenious to use Adam and its modified version called RanAdam as hyper-parameters updating method for SI algorithms. The proposed RanAdam algorithm tries to find actual optimal values for hyperparameters near the optimal values given by Adam method through the Controlled Randomness procedure. This research work also compares dementia MRI image classification performance of elephant herding optimization-based transformation technique with the standard clustering approaches and other transformation approaches. In this research work, 117 subjects (65 nondementia and 52 dementia subjects) acquired from the Open Access Series of Imaging Studies (OASIS) database is used. Two cases are analyzed in all the techniques: with and without statistical features. The highest accuracy of 90.6% is achieved by elephant herding optimization (EHO)-based transformation technique combined with RanAdam for updating hyper-parameters for the case without statistical features. To verify the efficiency of the proposed technique, a popular Pima diabetic dataset is considered in addition to the OASIS dementia dataset and 88% accuracy is earned for EHO-based transformation technique combined with RanAdam.

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“…The proposed NBA has given a higher quality performance when it was applied for small scale power systems [3,4] . The NBA performance can be achieved by making comparison with other well-known optimization methods that they proved their efficiency and reliability in solving the EPD problem, such as Genetic Algorithm (GA) [5][6][7], Particle Swarm Optimization (PSO) [8][9][10][11] and Quadratic Programing (QP) [12][13][14], and the performance of NBA will be studied by comparing the obtained simulation results of it with the other optimization algorithms which they mentioned above.…”

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confidence: 99%

A New Bat Optimization Algorithm to Solve EPD Problem Solving with Transmission Loss

Rahebi

Al-jumaili

2020

Balkan Journal of Electrical and Computer Engineering

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While, researchers work to make the systems operate economically and reduce operational cost, in this study, we work to reduce the fuel costs to make the power station running economically as much as possible by utilizing the Economic Power Dispatch (EPD) and the optimization algorithms. The economic power dispatch (EPD) is an integral part of the power system, The major roles and the purpose of its use are for achieving a reliable and efficient operation out of power system generation networks, and this operation should be obtained by minimizing the generator fuel cost. Getting optimal solutions to EPD problem requires efficient optimization algorithms. Novel Bat Algorithm (NBA) is one of the most recent methods and it has already proven its efficiency and reliability for solving the EPD problem. This paper proposes Novel Bat Algorithm (NBA) in order to solve the EPD problem based on the large scale power system. The NBA has proved its efficiency and it gave a perfect performance for small-scale systems compared with the original Bat algorithm (BA), because of considering the Doppler Effect and assumed that bat can move between various habitats. The study of the EPD for large-scale power systems is more important than study it for small ones, because all power systems in countries are considered large networks. This is why all modern studies focus on the study of EPD for large systems. To test the performance of NBA during small and large scale power system, we have applied it to many systems, including 3thermal units, 6-thermal units, 31-Iraqi thermal units and IEEE 40-thermal units respectively, with transmission losses and generator limits, and we have made a comparison of the obtained results of NBA with other optimization methods. The Iraqi thermal units have been utilized as a new data.Index Terms-Novel Bat Algorithm (NBA), Economic Power Dispatch (EPD).

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Hybrid PSO-ACO algorithm to solve economic load dispatch problem with transmission loss for small scale power system

Cited by 15 publications

References 23 publications

A new hybrid teaching–learning particle swarm optimization algorithm for synthesis of linkages to generate path

A new hybrid teaching–learning particle swarm optimization algorithm for synthesis of linkages to generate path

DementiaMRIimage classification using transformation technique based on elephant herding optimization with Randomized Adam method for updating the hyper‐parameters

A New Bat Optimization Algorithm to Solve EPD Problem Solving with Transmission Loss

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