New chaotic flower pollination algorithm for unconstrained non-linear optimization functions

Dynamic h“economic load dispatch (DELD) aims to obtain optimum generation schedule of the committed generating units’ output over ha certain timing horizon”, sustaining practical restrictions and power demands in each period.”Valve-point effect, the ramp up/down limits, prohibited operation zones (POZs), and power losses form the hDELD has ha complex, non-linear liable’ problem. The Flower Pollination Algorithm (FPA) his therefore anticipated in this paper to solve such a complex issue. The practicality of the proposed FPA method is assessed by conducting simulations at different load patterns hon standard 5-unit and 10-unit systems for ha 24-hour schedule. The FPA's simulation results hare related to other previously published biography techniques. These results clearly show that the skill hand robustness hof the proposed FPA method to solve the non h- linear DELD problem has been restricted.

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Optimal Reactive Power Dispatch untuk Meminimalkan Rugi Daya Menggunakan Flower Pollination Algorithm

Jurnal Teknik Elektro ◽

10.15294/jte.v11i2.21680 ◽

2019 ◽

Vol 11 (2) ◽

pp. 36-43

Author(s):

Fredi Prima Sakti ◽

Jimmy Trio Putra

Keyword(s):

Power Loss ◽

Reactive Power ◽

Linear Optimization ◽

Electric Power System ◽

Flower Pollination Algorithm ◽

Flower Pollination ◽

Optimal Reactive Power Dispatch ◽

Power Dispatch ◽

Reactive Power Dispatch ◽

Bus Voltage

This paper presents the Flower Pollination Algorithm (FPA) metaheuristic used to solve the Optimal Reactive Power Dispatch (ORPD) problem. ORPD is a non-linear optimization problem in the electric power system that regulates the generation of reactive power at the generator to minimize the real power loss on the transmission line while maintaining all parameters at the allowable value. In this case the FPA algorithm is used to find the minimum power loss by adjusting the voltage magnitude value of the generator, the transformer tap settings, and the reactive power compensator value in the system while maintaining the magnitude of the bus voltage, active and reactive power at the generator, and the channel capacity remains at its safe limit. ORPD is applied to the IEEE-30 Bus system test consisting of 8 generating units, 4 transformers, 9 reactive power compensators and 41 channels. The system has a load of 283.4 MW and 126.2 MVAR. The results after being optimized using FPA shows the power loss in the channel is reduced to 4,895 MW or reduced by 15.89%. The results of optimization using FPA showed better results compared to Genetic Algorithm and Particle Swarm Optimization.

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Flower Pollination Algorithm for the Inversion of Schlumberger Sounding Curve

IOP Conference Series Earth and Environmental Science ◽

10.1088/1755-1315/873/1/012018 ◽

2021 ◽

Vol 873 (1) ◽

pp. 012018

Author(s):

F Raflesia ◽

W Widodo

Keyword(s):

Synthetic Data ◽

Least Square ◽

Flower Pollination Algorithm ◽

Grey Wolf Optimizer ◽

Local Optimum ◽

Initial Model ◽

Linear Inversion ◽

Flower Pollination ◽

Non Linear ◽

Schlumberger Sounding

Abstract Inversion of schlumberger sounding curve is non-linear, and multi-minimum. All linear inversion strategies can produce local optimum, and depend on the initial model. Meanwhile, the non-linear bionic method for inversion problems does not require an initial model, simple, flexible, derivation-free mechanism and can avoid local optimum. One of the new algorithm of the non-linear bionic method for geophysical inversion problem is the Flower Pollination Algorithm (FPA). The FPA is used for the inversion of schlumberger sounding curve. This algorithm was stimulated by the pollination process for blooming plants. The applicability of the present algorithm was tested on synthetic models A-type and KH-type curve. Numerical tests in MATLAB R2013a for the synthetic data and the observed data show that FPA can find the global minimum. For further study, inverted results using the FPA are contrasted with the damped least-square (DLSQR) inversion program, Particle Swarm Optimization (PSO), and Grey Wolf Optimizer (GWO). The outcomes of the comparison reveal that FPA performs better than the DLSQR inversion program, PSO, and GWO.

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Comparative Analysis of Chaotic Variant of Firefly Algorithm, Flower Pollination Algorithm and Dragonfly Algorithm for High Dimension Non-linear Test Functions

Advances in Intelligent Systems and Computing - Innovations in Bio-Inspired Computing and Applications ◽

10.1007/978-3-030-16681-6_19 ◽

2019 ◽

pp. 192-201

Author(s):

Amrit Pal Singh ◽

Arvinder Kaur

Keyword(s):

Comparative Analysis ◽

High Dimension ◽

Firefly Algorithm ◽

Flower Pollination Algorithm ◽

Test Functions ◽

Dragonfly Algorithm ◽

Flower Pollination ◽

Non Linear

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An Improved Flower Pollination Algorithm for Global and Local Optimization

JOIV International Journal on Informatics Visualization ◽

10.30630/joiv.5.4.738 ◽

2021 ◽

Vol 5 (4) ◽

pp. 461

Author(s):

M. Iqbal Kamboh ◽

Nazri Bin Mohd Nawi ◽

Azizul Azhar Ramli ◽

Fanni Sukma

Keyword(s):

Heuristic Algorithms ◽

Optimization Problems ◽

Linear Optimization ◽

Bat Algorithm ◽

Search Space ◽

Flower Pollination Algorithm ◽

Mathematical Functions ◽

Flower Pollination ◽

Linear Optimization Problems ◽

Global And Local Optimization

Meta-heuristic algorithms have emerged as a powerful optimization tool for handling non-smooth complex optimization problems and also to address engineering and medical issues. However, the traditional methods face difficulty in tackling the multimodal non-linear optimization problems within the vast search space. In this paper, the Flower Pollination Algorithm has been improved using Dynamic switch probability to enhance the balance between exploitation and exploration for increasing its search ability, and the swap operator is used to diversify the population, which will increase the exploitation in getting the optimum solution. The performance of the improved algorithm has investigated on benchmark mathematical functions, and the results have been compared with the Standard Flower pollination Algorithm (SFPA), Genetic Algorithm, Bat Algorithm, Simulated annealing, Firefly Algorithm and Modified flower pollination algorithm. The ranking of the algorithms proves that our proposed algorithm IFPDSO has outperformed the above-discussed nature-inspired heuristic algorithms.

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