Enriched Biogeography-Based Optimization Algorithm to Solve Economic Power Dispatch Problem

Raviprabhakaran, Vijay; Ravichandran, C.S.

doi:10.1007/978-981-10-0451-3_78

Cited by 7 publications

(5 citation statements)

References 20 publications

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“…From the above discussions, the proposed algorithm produces quality results in less time for finding the optimal location for placing a solar PV system. (Zhang et al, 2014) 2.5260 100.9800 BBO (Raviprabhakaran and Ravichandran, 2016) 2.4793 97.3084 ACO (Raviprabakaran and Subramanian, 2018) 2.4580 97.2849 BSO (Vijay, 2018) 2 (Zhang et al, 2014) 1.9682 108.7760 BBO (Raviprabhakaran and Ravichandran, 2016) 1.9167 109.1232 ACO (Raviprabakaran and Subramanian, 2018) 1.8670 106.3196 BSO (Vijay, 2018) 1.7586 99.1821 CSOA (prop. )…”

Section: Results Of Distributed Generation Placementmentioning

confidence: 99%

“…Mainly the probability dissemination deviates by iteration, and the computational interval to convergence is unreliable. In Biogeography-based optimization (BBO) (Raviprabhakaran and Ravichandran, 2016) the deprived solution admits some new features from worthy ones, this progresses the superiority of problem solutions. Comparably, this is another distinctive component of the BBO technique, when associated with alternative methods.…”

Section: Introductionmentioning

confidence: 99%

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Performance enrichment in optimal location and sizing of wind and solar PV centered distributed generation by communal spider optimization algorithm

Raviprabhakaran¹

2022

COMPEL

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Purpose The distributed generation (DG) proper placement is an extremely rebellious concern for attaining their extreme potential profits. This paper aims to propose the application of the communal spider optimization algorithm (CSOA) to the performance model of the wind turbine unit (WTU) and photovoltaic (PV) array locating method. It also involves the power loss reduction and voltage stability improvement of the ring main distribution system (DS). Design/methodology/approach This paper replicates the efficiency of WTU and PV array enactment models in the placement of DG. The effectiveness of the voltage stability factor considered in computing the voltage stability levels of buses in the DS is studied. Findings The voltage stability levels are augmented, and total losses are diminished for the taken bus system. The accomplished outcomes exposed the number of PV arrays accompanied by the optimal bus location for various penetration situations. Practical implications The optimal placement and sizing of wind- and solar-based DGs are tested on the 15- and 69-test bus system. Originality/value Moreover, the projected CSOA algorithm outperforms the PSOA, IAPSOA, BBO, ACO and BSO optimization techniques.

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Section: Results Of Distributed Generation Placementmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Performance enrichment in optimal location and sizing of wind and solar PV centered distributed generation by communal spider optimization algorithm

Raviprabhakaran¹

2022

COMPEL

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“…X as given in ( 14) otherwise update k u X as given in (13). If the new solution is better than the old solution, then, the new solution will be accepted in the population and the old solution will be rejected, otherwise the new solution will be rejected and old solution will be kept in the population.…”

Section: B Student Phasementioning

confidence: 99%

“…An optimization method based on Simulated Annealing (SA) algorithm has been implemented in [11] in this regard, the cost function has been approximated by a cubic function and the problem has been converted into monoobjective problem by using price penalty factors. Within this context, other metaheuristic techniques, such as Gravitational Search Algorithm (GSA) [12], Biogeography-Based Optimization (BBO) [13], Bacterial Foraging Algorithm (BFA) [14], and Harmony Search (HS) algorithm [15] have been developed and implemented for various complex dispatch problems. The main advantage of the aforementioned techniques is that they expand the entire search space for the optimal solution to avoid getting trapped in a local optimal.…”

Section: Introductionmentioning

confidence: 99%

Teaching-Learning-Based Optimization Algorithm for the Combined Dynamic Economic Environmental Dispatch Problem

Alshammari

2020

Eng. Technol. Appl. Sci. Res.

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The Dynamic Economic Environmental Dispatch Problem (DEEDP) is a major issue in power system control. It aims to find the optimum schedule of the power output of thermal units in order to meet the required load at the lowest cost and emission of harmful gases. Several constraints, such as generation limits, valve point loading effects, prohibited operating zones, and ramp rate limits, can be considered. In this paper, a method based on Teaching-Learning-Based Optimization (TLBO) is proposed for dealing with the DEEDP problem where all aforementioned constraints are considered. To investigate the effectiveness of the proposed method for solving this discontinuous and nonlinear problem, the ten-unit system under four cases is used. The obtained results are compared with those obtained by other metaheuristic techniques. The comparison of the simulation results shows that the proposed technique has good performance.

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“…The current horde optimization methods embrace particle swarm optimization (PSO), [1][2][3][4][5][6] differential evolution, [7][8][9][10] ant cluster optimization (ACO), [11][12][13][14][15][16] simulated bee constellation (SBC), 17,18 biogeography built optimization (BBO), [19][20][21][22][23][24][25][26][27] bat motivated optimization, 28 communal spider optimization, 29,30 multisurrogate assisted metaheuristics optimization, 31 seagull optimization, 32 grasshopper optimization, 33,34 salp swarm optimization, 35,36 levy flight optimization, 37,38 slime mold optimization, 39 sine-cosine optimization, 40 Henry gas solubility optimization, 41 bacterial horde optimization (BHO), 42,43 and so forth applied to real-world practical applications. Some limitations of considered optimization techniques are as follows PSO is computationally economical consequently it expends slight reminiscence and central administering unit speed.…”

Section: Introductionmentioning

confidence: 99%

Quorum sensing centered bacterial horde algorithm for global optimization

Raviprabhakaran¹

2023

Concurrency and Computation

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This article proposes an intellectual quorum sensing-centered bacterial horde optimization (QBHO) exert for assessing and authenticating the optimization problems.Quorum detection realizes the finest place of the bacteria by apprehending the most awful point within the hunt area. This mechanism is utilized in this study for problem-solving. With this bacterial behavior and location perceptive, the superlative optimum result is obtained. In this anticipated QBHO algorithm, the competence of bacteria is enhanced to be beneficial so that results can be enhanced.The fundamental group-built optimization procedures for instance, particle swarm, ant cluster, biogeography-built algorithm, simulated bee constellation procedure and typical bacterial herd optimization through the distinctive considerations remain simulated and then associated through the intended practice for its ratification of results with QBHO. The projected QBHO practice is exploited to ensue with the seven-typical benchmark with unimodal and multimodal trial functions intended to prove its practicability and ideality. The realized findings imply that the suggested QBHO approach surpasses the deemed optimization procedures for complex standard functions. Furthermore, the intended procedure might be appropriate to several engineering challenges, notably intended for intricate concurrent optimization problems.

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Enriched Biogeography-Based Optimization Algorithm to Solve Economic Power Dispatch Problem

Cited by 7 publications

References 20 publications

Performance enrichment in optimal location and sizing of wind and solar PV centered distributed generation by communal spider optimization algorithm

Performance enrichment in optimal location and sizing of wind and solar PV centered distributed generation by communal spider optimization algorithm

Teaching-Learning-Based Optimization Algorithm for the Combined Dynamic Economic Environmental Dispatch Problem

Quorum sensing centered bacterial horde algorithm for global optimization

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