Flower Pollination Algorithm with Dimension by Dimension Improvement

Abstract

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.

A Comparative Study on Recently-Introduced Nature-Based Global Optimization Methods in Complex Mechanical System Design

“…Henawy et al [67] introduced a hybrid of FPA combined with harmony search (FPCHS) to increase the accuracy of FPA. Wang and Zhou [68] presented a new search strategy called a Dimension by Dimension Improvement of FPA (DDIFPA) to enhance the convergence speed and solution quality of the original FPA. Kanagasabai et al [69] integrated FPA with PSO (FPAPSO) to deal with reactive power dispatch problems.…”

On the efficiency of the binary flower pollination algorithm: Application on the antenna positioning problem

“…The authors in [66] used the canonical flower pollination algorithm for solving the sudoku puzzles problem. In [79], the authors proposed a novel FPA based on dimension by dimension improvement. A comparative study between the FPA and the bat algorithm was conducted in [64].…”

On the performances of the flower pollination algorithm – Qualitative and quantitative analyses

“…We categorise them into 4 classes: (1) The works proposed by Yang and his co-authors [18,[22][23][24][25], (2) the works just applying the FPA for solving optimisation problems [27][28][29][30], (3) FPA extensions [31][32][33][34][35] and (4) the works analysing the FPA performances [36][37][38]. These contributions are analysed with greater detail in the next section.…”