Improved Flower Pollination Algorithm for Large Scale Optimization Problems

Abstract

Flower pollination algorithm (FPA) has a novel optimization structure and good optimization ability, but it is easy to fall into “dimension disaster” when solving high-dimensional optimization problems. In order to improve the performance of FPA in solving large-scale optimization problems, an improved flower pollination algorithm (IFPA) is proposed. The opposition-based learning strategy is adopted to increase the diversity of population and search the solution space sufficiently to improve the quality of the initial population. In the stage of self-pollination, the traction effect of contemporary optimal position is exerted to reduce the iterative cost of the algorithm and the search efficiency is improved. And this paper proposes a new method to avoid interference phenomena among dimensions to improve the local iteration quality. The pollen individual is updated with the strategy of dimension-by-dimension random disturbance, and the better solution is accepted after the overall evaluation. IFPA only needs 3 to 5 population individuals to achieve satisfactory optimization effect. The simulation results of 15 test functions at the dimensions of 100, 1000 and 5000 show that the solution accuracy of IFPA is greatly improved, the convergence speed is obviously accelerated, and the robustness is strong. Meanwhile, the results also reveal the proposed algorithm is competitive for different types of large scale optimization problems compared with FPA, PSO and BA.

Keywords

• flower pollination algorithm
• opposition-based learning
• dimension-by-dimension random disturbance
• interference among dimensions
• large scale optimization