Comparative study of bat &amp; flower pollination optimization algorithms in highly stressed large power system

Pandya, Kartik S.; Dabhi, Dharmesh; Joshi, S. K.

doi:10.1109/psc.2015.7101677

Cited by 9 publications

(4 citation statements)

References 11 publications

(10 reference statements)

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“…The termination criterion was set to the maximum number of iterations. The proposed WDFPA was compared with the original pollination algorithm and several improved versions: the elite dualitybased FPA (EOFPA) [31]- [34], dimensional evolution FPA (MFPA) [22], [35], quantum coding FPA (QFPA) [36]- [38], and bee FPA (BPFPA) [39].…”

Section: A Comparison Of Each Algorithm's Performancementioning

confidence: 99%

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Enhanced Metaheuristic Optimization: Wind-Driven Flower Pollination Algorithm

2019

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The flower pollination algorithm is a new metaheuristic optimization technique that simulates the pollination behavior of flowers in nature. The global and local search processes of the algorithm are performed by simulating the self-pollination and cross-pollination of flowers. However, the conventional flower pollination algorithm has several limitations. To overcome the problem of slow convergence and prevent the algorithm from becoming stuck around local optimum, this paper describes an enhanced metaheuristic wind-driven flower pollination algorithm (WDFPA). Experiments are conducted using 29 benchmark test functions and two engineering design problems, and the proposed WDFPA is compared against other metaheuristic optimization algorithms and several classical optimization approaches. The results show that WDFPA achieves better performance than the conventional flower pollination algorithm, especially in highdimensional optimization problems. The convergence speed and accuracy of WDFPA exhibit significant improvements over other metaheuristic algorithms in many of the test cases. Additionally, WDFPA produces optimal results for engineering design problems involving a welded beam and a spring structure.INDEX TERMS Enhanced metaheuristic optimization, flower pollination algorithm, wind driven, winddriven flower pollination algorithm.

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Section: A Comparison Of Each Algorithm's Performancementioning

confidence: 99%

“…where M and J (X ) represent the moment of inertia and the polarity, respectively. The remaining parameters are given by: 10 6 psi, E = 30 × 10 6 psi, P = 6000lb, L = 14in(22) …”

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confidence: 99%

Enhanced Metaheuristic Optimization: Wind-Driven Flower Pollination Algorithm

2019

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“…This natural world inspired technique is developed from the distinctiveness of flowering plants with fascinating characteristics that lend a hand to travel around the viable groom in the neighbourhood and globally. In the recent times, it has gained increased attention to solve the OPF problem [21][22] to discover the most select settings of the controllable variables [23].…”

Section: Introductionmentioning

confidence: 99%

Adaptive FPA Algorithm based OPF with Unified Power Flow Controller

Immanuel¹,

Babu²,

Sudheer³

et al. 2022

EAI Endorsed Trans Energy Web

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In this work a novel modified flower pollination algorithm has been developed to solve the problem of single and multi-objective Optimal Power Flow operations for Unified power Flow Controller in Flexible Alternating Current Transmission Systems. In the proposed Adaptive Flower Pollination Algorithm the best initial solution can be chosen from the fittest and also the weights are adaptively adjusted to get better convergence characteristics. The nature of the objective functions is non-linear and difficult to get best possible solutions within the boundary conditions of total power demand. The weak nodes are determined in the system to locate the UPFC with Fuzzy approach considering input parameters as L-Index and voltage magnitudes. The projected method is validated using IEEE-30 and IEEE-57 bus systems for three objective functions, namely, system real power loss minimization, fuel cost minimization and the combination of total generating cost and system real power loss. Results of Fuzzy- Adaptive Flower Pollination Algorithm based OPF optimization for UPFC produced optimum results for the considered objectives of total fuel cost, real power loss and for the multiobjective.

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“…This algorithm, as stimulated from nature, simulates the features of flowering plants and the important aspects that lead to find the global and the local feasible space. As for these features, it has been used to solve the optimal power flow problem [31][32][33][34].…”

Section: Introductionmentioning

confidence: 99%

Modified Flower Pollination Algorithm Constrained Optimal Power Flow

Hocine¹,

Salem²

2019

ISI

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In power systems, the power flow must satisfy a set of operational and safety requirements. It is a highly nonlinear problem to optimize the power flow. In this paper, the flower pollination algorithm (FPA) is modified and applied to optimize the power flow. Firstly, the author set up a mathematical model for power flow optimization problem, and formulated all the constraints. Next, the FPA was introduced in details, including the self-pollination, cross-pollination and other operations. After that, the local search strategy of the FPA was modified to replace the worst individuals in the current population with the best individuals in the previous population. Finally, the modified FPA (MFPA) was proved more efficient and better in convergence than standard algorithms like the firefly algorithm.

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Comparative study of bat & flower pollination optimization algorithms in highly stressed large power system

Cited by 9 publications

References 11 publications

Enhanced Metaheuristic Optimization: Wind-Driven Flower Pollination Algorithm

Enhanced Metaheuristic Optimization: Wind-Driven Flower Pollination Algorithm

Adaptive FPA Algorithm based OPF with Unified Power Flow Controller

Modified Flower Pollination Algorithm Constrained Optimal Power Flow

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