Abstract
Bird swarm algorithm is one of the swarm intelligence algorithms proposed recently. However, the original bird swarm algorithm has some drawbacks, such as easy to fall into local optimum and slow convergence speed. To overcome these short-comings, a dynamic multi-swarm differential learning quantum bird swarm algorithm which combines three hybrid strategies was established. First, establishing a dynamic multi-swarm bird swarm algorithm and the differential evolution strategy was adopted to enhance the randomness of the foraging behavior's movement, which can make the bird swarm algorithm have a stronger global exploration capability. Next, quantum behavior was introduced into the bird swarm algorithm for more efficient search solution space. Then, the improved bird swarm algorithm is used to optimize the number of decision trees and the number of predictor variables on the random forest classification model. In the experiment, the 18 benchmark functions, 30 CEC2014 functions, and the 8 UCI datasets are tested to show that the improved algorithm and model are very competitive and outperform the other algorithms and models. Finally, the effective random forest classification model was applied to actual oil logging prediction. As the experimental results show, the three strategies can significantly boost the performance of the bird swarm algorithm and the proposed learning scheme can guarantee a more stable random forest classification model with higher accuracy and efficiency compared to others.
Highlights
Like many swarm intelligence algorithms, BSA is faced with the problem of being trapped in local optima and slow convergence. ese disadvantages limit the wider application of BSA
In order to validate effectiveness of the proposed method, we have evaluated the performance of DMSDL-QBSA on classical benchmark functions and CEC2014 functions including unimodal and multimodal functions in comparison with the state-of-the-art methods and new popular algorithms. e experimental results have shown that the three improvement strategies are able to significantly boost the performance of BSA
As the experimental results show, the proposed learning scheme can guarantee a more stable random forest (RF) classification model with higher predictive accuracy compared to other counterparts. e rest of the paper is organized as follows: (i) In order to achieve a better balance between efficiency and velocity for BSA, we have studied the effects of four different hybrid strategies of the dynamic multi-swarm method, differential evolution, and quantum behavior on the performance of BSA
Summary
(1) In order to improve the local search capability of foraging behavior on BSA, we put forward equation (8) based on the dynamic multi-swarm method. (2) In order to get the guiding vector to improve the global search capability of foraging behavior on BSA, we put forward equations (9), (11), and (12) based on differential evolution. Variable setting: number of iterations:iter, bird positions: Xi, local optimum: Pi, global optimal position:gbest, global optimum: Pgbest,and fitness of sub-swarm optimal position: Plbest; Input: population size: N, dimension’s size: D, number of function evaluations:iter max, the time interval of each bird flight behavior: FQ, the probability of foraging behavior: P, constant parameter: c1, c2, a1, a2, FL, iter 0, and contraction expansion factor: β; Output: global optimal position: gbest, fitness of global optimal position: Pgbest; (1) Begin (2) Initialize the positions of N birds using equations (16)-(17): Xi(i 1, 2, ..., N);. DE has not got the minimum value of 0
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