Hydrothermal scheduling using Modified Flower Pollination Algorithm: A parallel approach

Sutradhar, Suman; Choudhury, Nalin B. Dev; Sinha, Nidul

doi:10.1109/tencon.2016.7848307

Cited by 6 publications

(8 citation statements)

References 18 publications

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“…The paper found that system costs decreased, can confirm FPAs success and efficiency in economic dispatch problems to obtain an optimal solution in a variety of cases in power generation systems. In other literature, the initial population and switching processes was modified to form the MFPA, described in [111], [112], and [113], and was applied by Sarjiya et al [79] on a custom 10-generator system and Regalado et al [13] who conducted the test on a standard IEEE 30-bus system. The results show that in both cases FPA and MFPA were able to reduce the costs of the system.…”

Section: A the Economic Dispatch Problemmentioning

confidence: 99%

“…[81], [80] , [82] [114] [111] Scheduling of Hydrothermal stations -optimised scheduling of Hydrothermal stations in smart/ distribution grids to minimize operational costs.…”

Section: References Problem Solved Optimization Techniquementioning

confidence: 99%

“…Addition of a scaling factor and an additional exploitation phase can greatly enhance the performance of FPA in this sector [111].…”

Section: Fpa Mfpa Ifpamentioning

confidence: 99%

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Applications of Flower Pollination Algorithm in Electrical Power Systems: A Review

et al. 2022

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Section: A the Economic Dispatch Problemmentioning

confidence: 99%

“…[81], [80] , [82] [114] [111] Scheduling of Hydrothermal stations -optimised scheduling of Hydrothermal stations in smart/ distribution grids to minimize operational costs.…”

Section: References Problem Solved Optimization Techniquementioning

confidence: 99%

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Applications of Flower Pollination Algorithm in Electrical Power Systems: A Review

et al. 2022

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“…In the literature, various classical and evolutionary based optimization techniques have been developed for solving the optimal ST-HTS problem. Some of the evolutionary algorithms used for solving the ST-HTS problem include: particle swarm optimization (PSO) [4], fully-informed PSO [5], two-swarm based PSO search strategy [6], couple-based PSO [7], hybrid simulated annealing/genetic algorithm [8], quasi-oppositional teaching learning based optimization [9], improved harmony search algorithm [10], successive approximation approach [11], improved TLBO algorithm [12], accelerated PSO [13], flower pollination algorithm [14], symbiotic organisms search algorithm [15], Grey wolf optimizer [16], recurrent neural network [17], modified flower pollination algorithm [18], etc. Some of the literature is also available for solving the multi-objective based ST-HTS problem by optimizing cost and emission using normal boundary intersection and VIKOR in [19], lexicographic optimization and normal boundary intersection method in [20], enhanced multi-objective bee colony optimization algorithm in [21].…”

Section: Introductionmentioning

confidence: 99%

Optimal short-term hydro-thermal scheduling using multi-function global particle swarm optimization

Salkuti¹

2020

IJEECS

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<p>An optimal short-term hydro-thermal scheduling (ST-HTS) problem is solved in this paper using the multi-function global particle swarm optimization (MF-GPSO). A multi-reservoir cascaded hydro-electric system with a non-linear relationship between water discharge rate, power generation and net head is considered in this paper. The ST-HTS problem determines the optimal power generation of hydro and thermal generators which is aimed to minimize total fuel cost of thermal power plants during a determined time period. Effects of valve point loading and prohibited operating zones in the fuel cost function of the thermal power plants is examined. Power balance, reservoir volume, water balance and operation constraints of hydro and thermal plants are considered. The effectiveness and feasibility of MF-GPSO algorithm is examined on a standard test system, and the simulation results are compared with other algorithms presented in the literature. The results show that the MF-GPSO algorithm appears to be the best in terms of convergence speed and optimal cost compared with other techniques reported in the literature.</p>

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“…In the survey study on metaheuristic algorithms [14] also concluded in their findings that applying a combination of two metaheuristics can improve another metaheuristic algorithm's performance. To the best of our investigation, the majority of the studies presented for solving optimization problems using FPA are applied to engineering and industry-related problems [20][21][22][23]. Despite the enhancements and related works on FPA mentioned in this study, FPA has certain limitations or issues: FPA cannot deal directly with combinatorial problems like UCTP, the exploration ability of FPA is dependent on the levy flight which can be aggressive and can cause large steps and a non-diverse population may lead to local optima and the parameter settings in an FPA may affect also the performance of the algorithm.…”

Section: Introductionmentioning

confidence: 99%

New hybrid flower pollination algorithm with dragonfly algorithm and jaccard index to enhance solving university course timetable problem

Sapul¹,

Setthawong²,

Setthawong³

2020

IJEECS

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University course timetable problem (UCTP) is one of the problems on which many researches have been conducted over the years because of its importance in academic institutions. A nature-inspired metaheuristic optimization algorithm, Flower Pollination Algorithm (FPA) has been adapted, so-called Adapted FPA (AFPA), to cope with UCTP in the previous work. However, AFPA suffers from the stagnation problem because of the non-diversity in the population. To improve the diversity of the population, this work introduces new Hybrid FPA with two variants: JFPA provided the Jaccard index to determine similarities among categorical data and the greedy selection mechanism to improve the selection of the random solution, and DFPA applied the navigational characteristics of the Dragonfly Algorithm (DA) to help in the neighborhood relationship. The results in this study indicate that the proposed algorithms have better exploration ability and fast convergence rate in comparison; JFPA outperforms AFPA in 3 out of 4 datasets for both small and large datasets, and DFPA outperforms AFPA and GA in all datasets while it outperforms PSO in 3 out of 4 small datasets and 2 complex large datasets.

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Hydrothermal scheduling using Modified Flower Pollination Algorithm: A parallel approach

Cited by 6 publications

References 18 publications

Applications of Flower Pollination Algorithm in Electrical Power Systems: A Review

Applications of Flower Pollination Algorithm in Electrical Power Systems: A Review

Optimal short-term hydro-thermal scheduling using multi-function global particle swarm optimization

New hybrid flower pollination algorithm with dragonfly algorithm and jaccard index to enhance solving university course timetable problem

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