Bacterial Foraging Algorithm With Potential Field Guidance Mechanism

To address the problems of weak quorum sensing ability and slow convergence speed in bacterial foraging algorithm, a bacterial foraging algorithm with potential field guidance mechanism is proposed. The algorithm combines the sampling guidance mechanism in the artificial potential field algorithm to provide the optimization direction for each bacterium; The original swimming operation of bacterial foraging algorithm is used to realize the local optimization strategy, and the local dimension update is added after swimming, so that the search range of bacteria in chemotaxis operation is wider; In the elimination and dispersal operation of bacterial foraging algorithm, double Gaussian function is introduced to re initialize the location of bacteria, so as to better avoid the algorithm falling into local extremum and improve the optimization ability of the algorithm. The experimental results show that the improved bacterial foraging algorithm has better optimization ability than the basic bacterial foraging algorithm.

Hybrid spiral-bacterial foraging algorithm for a fuzzy control design of a flexible manipulator

A novel hybrid strategy combining a spiral dynamic algorithm (SDA) and a bacterial foraging algorithm (BFA) is presented in this article. A spiral model is incorporated into the chemotaxis of the BFA algorithm to enhance the capability of exploration and exploitation phases of both SDA and BFA with the aim to improve the fitness accuracy for the SDA and the convergence speed as well as the fitness accuracy for BFA. The proposed algorithm is tested with the Congress on Evolutionary Computation 2013 (CEC2013) benchmark functions, and its performance in terms of accuracy is compared with its predecessor algorithms. Consequently, for solving a complex engineering problem, the proposed algorithm is employed to obtain and optimise the fuzzy logic control parameters for the hub angle tracking of a flexible manipulator system. Analysis of the performance test with the benchmark functions shows that the proposed algorithm outperforms its predecessor algorithms with significant improvements and has a competitive performance compared to other well-known algorithms. In the context of solving a real-world problem, it is shown that the proposed algorithm achieves a faster convergence speed and a more accurate solution. Moreover, the time-domain response of the hub angle shows that the controller optimised by the proposed algorithm tracks the desired system response very well.

A Double-Layer Combination Algorithm for Real-Time Information-Sharing Network Design Problem

The accumulation of real-time data has attracted the attention of various industries because valuable information can be extracted from the effective model and method design. This paper designs a low-carbon model and focuses on the real-time information-sharing network in order to get sustainable strategies promptly and exactly. The design problem is concerned with determining optimal integration strategies on a series of multilocation, multipath, and multiwarehouse freight provided by an information-sharing network to find an effective balance between the total costs and carbon emission. Firstly, the biobjective information-sharing network model is established to describe real-time problem with total cost and carbon emission factor. Secondly, a double-layer bacterial foraging algorithm is divided into inner and outer layers to solve the model, in which the inner section solves the transportation and inventory problems, and the outer section solves the supplier location problem. The double-layer bacterial foraging algorithm realizes the optimization of multisource e-commerce information-sharing model through nesting inside and outside layers. Finally, double-layer bacterial foraging algorithm can be confirmed to get the global optimal solution rapidly based on test data and the e-business case study of Jingdong, China.