scholarly journals A Survey on Flower pollination algorithm

2021 ◽  
pp. 05-11
Author(s):  
Safaa .. ◽  
◽  
◽  
Ibrahim Elhenawy
Keyword(s):  
Optimization Problems ◽  
Parameter Tuning ◽  
Function Optimization ◽  
Flower Pollination Algorithm ◽  
Complex Nature ◽  
Computer Gaming ◽  
Flower Pollination ◽  
Reproduction Strategy ◽  
Power And Energy ◽  
Energy Signal

Flower pollination algorithm (FPA) is a metaheuristic algorithm that proceeds its representation from flowers' proliferation role in plants. The optimal plant reproduction strategy involves the survival of the fittest as well as the optimal reproduction of plants in terms of numbers. These factors represent the fundamentals of the FPA and are optimization-oriented. Yang developed the FPA in 2012, which has since shown superiority to other metaheuristic algorithms in solving various real-world problems, such as power and energy, signal and image processing, communications, structural design, clustering and feature selection, global function optimization, computer gaming, and wireless sensor networking. Recently, many variants of FPA have been developed by modification, hybridization, and parameter-tuning to cope with the complex nature of optimization problems this paper provides a survey of FPA and its applications.

scholarly journals Flower Pollination Algorithm with Dimension by Dimension Improvement

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Rui Wang ◽  
Yongquan Zhou
Keyword(s):  
Optimization Problems ◽  
Convergence Speed ◽  
Function Optimization ◽  
Neighborhood Search ◽  
Flower Pollination Algorithm ◽  
Evaluation Strategy ◽  
Flower Pollination ◽  
Searching Ability ◽  
Improved Algorithm

Flower pollination algorithm (FPA) is a new nature-inspired intelligent algorithm which uses the whole update and evaluation strategy on solutions. For solving multidimension function optimization problems, this strategy may deteriorate the convergence speed and the quality of solution of algorithm due to interference phenomena among dimensions. To overcome this shortage, in this paper a dimension by dimension improvement based flower pollination algorithm is proposed. In the progress of iteration of improved algorithm, a dimension by dimension based update and evaluation strategy on solutions is used. And, in order to enhance the local searching ability, local neighborhood search strategy is also applied in this improved algorithm. The simulation experiments show that the proposed strategies can improve the convergence speed and the quality of solutions effectively.

scholarly journals Experimental Design for Optimization of Orthogonal Projection Pursuit Models

2020 ◽  
Vol 34 (06) ◽  
pp. 10235-10242
Author(s):  
Mojmir Mutny ◽  
Johannes Kirschner ◽  
Andreas Krause
Keyword(s):  
Experimental Design ◽  
Orthogonal Projection ◽  
Optimization Problems ◽  
Additive Model ◽  
Projection Pursuit ◽  
Parameter Tuning ◽  
Additive Models ◽  
Function Optimization ◽  
Bayesian Optimization ◽  
Additive Structure

Bayesian optimization and kernelized bandit algorithms are widely used techniques for sequential black box function optimization with applications in parameter tuning, control, robotics among many others. To be effective in high dimensional settings, previous approaches make additional assumptions, for example on low-dimensional subspaces or an additive structure. In this work, we go beyond the additivity assumption and use an orthogonal projection pursuit regression model, which strictly generalizes additive models. We present a two-stage algorithm motivated by experimental design to first decorrelate the additive components. Subsequently, the bandit optimization benefits from the statistically efficient additive model. Our method provably decorrelates the fully additive model and achieves optimal sublinear simple regret in terms of the number of function evaluations. To prove the rotation recovery, we derive novel concentration inequalities for linear regression on subspaces. In addition, we specifically address the issue of acquisition function optimization and present two domain dependent efficient algorithms. We validate the algorithm numerically on synthetic as well as real-world optimization problems.

scholarly journals Optimal Power Flow Using Flower Pollination Algorithm: A Case Study of 500 kV Java-Bali Power System

2017 ◽  
Vol 1 (2) ◽  
Author(s):  
Fredi Prima Sakti ◽  
Sarjiya Sarjiya ◽  
Sasongko Pramono Hadi
Keyword(s):  
Power System ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Optimization Problems ◽  
Flower Pollination Algorithm ◽  
Flower Pollination ◽  
Optimal Power ◽  
Metaheuristic Methods ◽  
Owned Company

Flower Pollination Algorithm (FPA) is one of metaheuristic methods that is widely used in optimization problems. This method was inspired by the nature of flower pollination. In this research, FPA is applied to solve Optimal Power Flow (OPF) problems with case study of 500 kV Java-Bali power system in Indonesia. The system consists of 25 bus with 30 lines and 8 generating units. Control variables are generation of active power and voltage magnitude at PV bus and swing bus under several power system constraints. The results show that FPA method is capable of solving OPF problem. This method decreased the generator fuel cost of PT. PLN (Persero), the state-owned company in charge of providing electricity in Indonesia, up to 13.15%.

scholarly journals An improved flower pollination solution for economic dispatch with valve point effect

2021 ◽  
Vol 22 (2) ◽  
pp. 629
Author(s):  
Muhammad Iqbal Kamboh ◽  
Nazri Mohd Nawi ◽  
Radiah Bt. Mohamad
Keyword(s):  
Power Generation ◽  
Optimization Problems ◽  
Thermal Power ◽  
Economic Dispatch ◽  
Evolutionary Programming ◽  
Flower Pollination Algorithm ◽  
Cost Curve ◽  
Fuel Cost ◽  
Flower Pollination ◽  
Valve Point Effect

<span>The economic dispatch is used to find the best optimal output of power generation at the lowest operating cost of each generator, to fulfill the requirements of the consumer. To get a practical solution, several constraints have to be considered, like transmission losses, the valve point effect, prohibited operating region, and emissions. In this research, the valve point effect is to be considered which increases the complexity of the problem due to its ripple effect on the fuel cost curve. Economic load dispatch problems are well-known optimization problems. Many classical and meta-heuristic techniques have been used to get better solutions.  However, there is still room for improvement to get an optimal solution for the economic dispatch problem. In this paper, an Improved Flower Pollination Algorithm with dynamic switch probability and crossover operator is proposed to solve these complex optimization problems.  The performance of our proposed technique is analyzed against fast evolutionary programming (FEP), modified fast evolutionary programming (MFEP), improved fast evolutionary programming (IFEP), artificial bee colony algorithm (ABC), modified particle swarm optimization (MPSO) and standard flower pollination algorithm (SFPA) using three generator units and thirteen thermal power generation units, by including the effects of valve point loading unit and without adding it. The proposed technique has outperformed other methods in terms of the lowest operating fuel cost.</span>

scholarly journals Biological Flower Pollination Algorithm with Orthogonal Learning Strategy and Catfish Effect Mechanism for Global Optimization Problems

2018 ◽  
Vol 2018 ◽  
pp. 1-16 ◽  
Author(s):  
Weijia Cui ◽  
Yuzhu He
Keyword(s):  
Learning Strategy ◽  
Optimization Problems ◽  
Orthogonal Experiment ◽  
Optimization Technique ◽  
Iteration Process ◽  
Population Diversity ◽  
Flower Pollination Algorithm ◽  
Flower Pollination ◽  
Effect Mechanism ◽  
Orthogonal Learning

The flower pollination algorithm (FPA) is a novel optimization technique derived from the pollination behavior of flowers. However, the shortcomings of the FPA, such as a tendency towards premature convergence and poor exploitation ability, confine its application in engineering problems. To further strengthen FPA optimization performance, an orthogonal learning (OL) strategy based on orthogonal experiment design (OED) is embedded into the local pollination operator. OED can predict the optimal factor level combination by constructing a smaller but representative test set based on an orthogonal array. Using this characteristic of OED, the OL strategy can extract a promising solution from various sources of experience information, which leads the population to a potentially reasonable search direction. Moreover, the catfish effect mechanism is introduced to focus on the worst individuals during the iteration process. This mechanism explores new valuable information and maintains superior population diversity. The experimental results on benchmark functions show that our proposed algorithm significantly enhances the performance of the basic FPA and offers stronger competitiveness than several state-of-the-art algorithms.

scholarly journals Flower Pollination Algorithm Combined with Multiple Strategies of Opposition–Based Learning

2018 ◽  
Author(s):  
Cácio L. N. A. Bezerra ◽  
Cácio L. N. A. Bezerra ◽  
Fábio G. B. C. Costa ◽  
Lucas V. Bazante ◽  
Pedro V. M. Carvalho ◽  
...  
Keyword(s):  
Optimization Problems ◽  
Flower Pollination Algorithm ◽  
Local Optimum ◽  
Multiple Strategies ◽  
Flower Pollination ◽  
Opposition Based Learning ◽  
New Variant

Flower Pollination Algorithm (FPA) has been widely used to solve optimization problems. However, it faces the problem of stagnation in local optimum. Several approaches have been proposed to deal with this problem. To improve the performance of the FPA, this paper presents a new variant that combines FPA and two variants of the Opposition Based Learning (OBL), such as Quasi OBL (QOBL) and Elite OBL (EOBL). To evaluate this proposal, 10 benchmark functions were used. In addition, the proposed algorithm was compared with original FPA and three variants such as FA–EOBL, SBFPA and DE–FPA. The proposal presented significant results.

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