Modified Flower Pollination Algorithm for Function Optimization

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.

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A Survey on Flower pollination algorithm

10.54216/jisiot.020101 ◽

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.

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A Novel Crow Search Algorithm Based on Improved Flower Pollination

Mathematical Problems in Engineering ◽

10.1155/2021/1048879 ◽

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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