An improved flower pollination algorithm with chaos theory for function optimization

2017 ◽  
Author(s):  
Ong Pauline ◽  
Ong Kok Meng ◽  
Sia Chee Kiong
Keyword(s):  
Chaos Theory ◽  
Function Optimization ◽  
Flower Pollination Algorithm ◽  
Flower Pollination

Optimization of the Angle of Twist of Propeller Using Modified Flower Pollination Algorithm

2018 ◽  
pp. 299-327
Author(s):  
Kok Meng Ong ◽  
Pauline Ong ◽  
Chee Kiong Sia
Keyword(s):  
Mathematical Model ◽  
Chaos Theory ◽  
Flower Pollination Algorithm ◽  
Optimal Solutions ◽  
Box Behnken Design ◽  
Inertia Weight ◽  
Flower Pollination ◽  
Engineering Design Problem ◽  
The Mathematical Model ◽  
The Relationship

In this chapter, a real-world mechanical engineering design problem (i.e., determining the angle of twist of propeller in order to maximize its thrust) was solved using modified flower pollination algorithm (MFPA). First, the relationship between angle of twist and the propeller thrust is explained. Then, the amount of experiments needed for this problem is determined using Box-Behnken design, whereas the mathematical model of this problem is developed using response surface methodology. After that, the overview of flower pollination algorithm (FPA) and its limitation is explained in detail. A proposed MFPA based on chaos theory, frog leaping local search, and inertia weight is explained next. The procedure of solving this problem is explained and the result of this problem is shown later with validation. The optimal solutions obtained from MFPA are 89°, 50.9730°, 48.4565°, 44.6729°, and 64.2286° for five angles of twist start from hub until the tip of the blade.

scholarly journals Tailored Flower Pollination (TFP) Algorithm for Diminution of Real Power Loss

2019 ◽  
Vol 8 (2) ◽  
pp. 94
Author(s):  
Dr.Lenin Kanagasabai
Keyword(s):  
Chaos Theory ◽  
Power Loss ◽  
Reactive Power ◽  
Flower Pollination Algorithm ◽  
Real Power ◽  
Flower Pollination ◽  
Test Systems ◽  
Shuffled Frog Leaping ◽  
Simulation Results ◽  
Power Problem

In this paper, Tailored Flower Pollination (TFP) algorithm is proposed to solve the optimal reactive power problem. Comprising of the elements of chaos theory, Shuffled frog leaping search and Levy Flight, the performance of the flower pollination algorithm has been improved. Proposed TFP algorithm has been tested in standard IEEE 118 & practical 191 bus test systems and simulation results show clearly the better performance of the proposed algorithm in reducing the real power loss.

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 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 A Novel Crow Search Algorithm Based on Improved Flower Pollination

2021 ◽  
Vol 2021 ◽  
pp. 1-26
Author(s):  
Qian Cheng ◽  
Huajuan Huang ◽  
Minbo Chen
Keyword(s):  
Search Algorithm ◽  
Function Optimization ◽  
Flower Pollination Algorithm ◽  
Benchmark Problems ◽  
Local Optimum ◽  
Location Update ◽  
Flower Pollination ◽  
Cauchy Mutation ◽  
Speed Reducer ◽  
Swarm Intelligence Optimization

Crow search algorithm (CSA) is a new type of swarm intelligence optimization algorithm proposed by simulating the crows’ intelligent behavior of hiding and retrieving food. The algorithm has the characteristics of simple structure, few control parameters, and easy implementation. Like most optimization algorithms, the crow search algorithm also has the disadvantage of slow convergence and easy fall into local optimum. Therefore, a crow search algorithm based on improved flower pollination algorithm (IFCSA) is proposed to solve these problems. First, the search ability of the algorithm is balanced by the reasonable change of awareness probability, and then the convergence speed of the algorithm is improved. Second, when the leader finds himself followed, the cross-pollination strategy with Cauchy mutation is introduced to avoid the blindness of individual location update, thus improving the accuracy of the algorithm. Experiments on twenty benchmark problems and speed reducer design were conducted to compare the performance of IFCSA with that of other algorithms. The results show that IFCSA has better performance in function optimization and speed reducer design problem.

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