Knowledge worker scheduling optimization model based on bacterial foraging algorithm

Highlights

• A bacterial foraging algorithm is proposed.

• In this paper, bacterial foraging algorithm is used to build a knowledge-based employee scheduling model.

• It can help bacteria improve the quality of reproduction, that is, improve the quality of the algorithm.

• It is proved that the optimized bacterial foraging algorithm has better accuracy and efficiency.

Abstract

Bacterial foraging algorithm comes from the best survival selection mechanism of animals in nature. As the representative of the heuristic algorithm, the bacterial foraging algorithm has unique advantages in solving the multi difficulty scheduling problem effectively. In order to realize the artificial intelligent management of the enterprise’s staff scheduling, this paper constructs the knowledge staff scheduling model by using a bacterial foraging algorithm and analyzes the implementation principle, advantages, and disadvantages of the algorithm. The influence of the basic parameters in the algorithm model on the algorithm performance is analyzed. In order to optimize the unconventional foraging strategy, the improvement measures of bacterial foraging behavior were proposed. Finally, the performance of the optimized bacterial foraging algorithm is tested and compared with the basic bacterial foraging algorithm, genetic algorithm, and particle swarm optimization algorithm. The experimental results show that the optimized bacterial foraging algorithm can achieve better convergence accuracy and shorter convergence speed for the objective function, and it can solve the scheduling optimization problem of knowledge workers more quickly, accurately, and effectively. The research in this paper shows that the optimization of four aspects of the basic bacterial foraging algorithm improves the performance of the algorithm and provides a theoretical reference for the optimization of the bacterial foraging algorithm.

Introduction

The optimal scheduling problem of enterprise knowledge workers is a typical N-P problem. Combined with bacterial foraging artificial intelligence algorithm, it can provide an efficient and optimal solution. Bacterial foraging algorithm is derived from the best survival selection mechanism of animals in nature. As a representative of heuristic algorithm, this algorithm has unique advantages in effectively solving multi difficult scheduling problems. This paper first introduces the principle, framework and process of bacterial foraging algorithm, and discusses the reasons for the limitations of the algorithm from two aspects of chemotaxis and reproduction. The setting method of basic parameters in the algorithm is discussed. In order to achieve the effect of bacterial foraging under unconventional and conventional conditions, the optimization scheme of bacterial foraging algorithm is proposed, and the equation and process of the algorithm are updated. Through the above research, bacterial foraging algorithm can sense the population, realize the aggregation of the population center and the fast search of the optimal solution, so as to improve the accuracy, speed and probability of solving the optimal solution. This study has important theoretical and practical value

Verma et al. [1] investigated the feasibility of the bacterial foraging algorithm applied to fuzzy edge detection and improves the accuracy of region extraction from the original image by optimizing the relevant parameters of the bacterial foraging algorithm. Chen et al. [2] studied the intelligent population algorithm, explored the mathematical model of flame optimization feature selection using a bacterial foraging algorithm, improving the probability of optimal feature combination search in machine learning. Lu et al. [3] proposed a quantum bacteria optimization algorithm. In the variable solution of the algorithm structure, the invariant angle was introduced to enrich the population diversity, improve the realization probability of the algorithm, and the accuracy of the optimal solution, which has the best algorithm robustness. Hern et al. [4] aimed at how to achieve the best optimization of the menu, uses the bacterial foraging algorithm to establish a mathematical model, introduces the theory of satisfying individual nutritional needs, and optimizes the algorithm to solve the constraint value, which makes the user satisfied in the application of menu planning. In modern production management research using artificial intelligence algorithms, Turanogl et al. [5] discussed the optimization of production indicators by using bacterial foraging algorithm and annealing algorithm and establishes a new heuristic algorithm model. In order to improve wireless communication, Sahu et al. [6] used the optimized bacterial foraging algorithm to solve the optimization problem of channel estimation and allocation, explored the feasibility of bacterial foraging algorithm to establish Gaussian distribution channel, and effectively reduced the ineffective selection of regional antenna in the application. Manikandan et al. [7] employed the hybrid model of bacterial foraging algorithm and genetic algorithm to solve the problem of multi-sequence comparison in protein development. Through the mutual support and supplement of the advantages of the two algorithms, the accuracy and efficiency of the algorithm are improved. Du et al. [8] optimized the basic bacterial foraging algorithm by improving the bacterial similarity, thus providing technical support for constructing artificial intelligence fuzzy system rule base and promoting the system robustness. Nithya et al. [9] proposed the concept of a three-way protocol applied in heterogeneous communication and optimized genetic algorithm by bacterial foraging algorithm to realize the security and reliability of the network routing protocol. Xue et al. [10] proposed two kinds of optimization bacterial foraging algorithms based on self-adaptive. Two different greedy strategies were used to search for the best solution. Experiments show that the algorithm has better performance than the algorithm.

In order to solve the problem of bacterial foraging in the initial stage, Jun et al. [11] introduce the adaptive algorithm to solve the problem of bacterial foraging in the initial stage. Peng et al. [12] improved the shortcomings of the bacterial foraging algorithm, which is easy to enter local optimum by means of uniform mutation propagation, and applied the optimized bacterial foraging algorithm to power distribution network reconfiguration for the performance test. Practice shows that the optimization algorithm has high accuracy and efficiency. Wang et al. [13] optimized the bacterial foraging algorithm by optimizing the bacterial density function factor, wall collision rebound factor, and dynamic step size. After optimization, the algorithm can realize the discovery probability of the best solution, improve the search speed, and have a suitable application prospect. Tang et al. [14] introduced an improved bacterial foraging algorithm to solve the economic operation scheduling problem of the distribution network and discussed the effectiveness and feasibility of the improved measures degree of bacterial foraging algorithm. Experiments show that this study can improve the utilization rate of the distribution network. In order to solve the problem of artificial intelligence control of machine speed and acceleration in manufacturing and processing, Gu et al. [15] used a bacterial foraging algorithm to construct the constraint method of control speed to ensure the smoothness of the processing curve. Chang et al. [16] applied a bacterial foraging algorithm to solve the intelligent scheduling problem of work and multi-angle selection of the same bacterial orientation direction to improve the searchability of bacterial foraging algorithm and established the Western foraging optimization algorithm model under different topological structures. Wang et al. [17] employed the optimized bacterial foraging algorithm to solve the workshop flexible scheduling problem. By improving the adaptive step size of the algorithm, the premature and local optimization problems of the algorithm are avoided, and the convergence of the bacterial foraging algorithm is effectively improved. In the experiment, it is proved that it has optimal optimization accuracy. In the optimization of the automatic focus system of the optical imaging system, Lu et al. [18] introduced the bacterial foraging artificial intelligence algorithm, solved the computational instability of the coding system through dynamic adaptive search, and helped the itemset system expand the depth of field. Aiming at the problem of the long operation cycle of bacterial foraging algorithm, Ma et al. [19] proposed a method to adjust the bacterial step size of the algorithm and introduced an immune algorithm to optimize the replication operation of bacterial foraging algorithm, which effectively improved the optimization efficiency and accuracy of bacterial foraging algorithm. Wu et al. [20] adopted a hybrid model of bacterial foraging algorithm and genetic algorithm in reactive power optimization of the power system. By increasing population diversity, it is found that the hybrid model combines the advantages of the two algorithms and has better performance. Liu et al. [21] introduced a new multi-objective optimization bacterial foraging algorithm hybrid multi-objective optimization bacterial foraging algorithm. Cai et al. [22] suggested a heuristic hybrid bacterial foraging algorithm to search the optimal pose of Blu, which avoids the time-consuming exhaustive search. Zheng et al. [23] proposed a reliability classification method of rock mass based on least squares support vector machine (LSSVM) optimized by BFOA, and proposed a novel response surface function for reliability evaluation to constrain the model. Reddy et al. Reddy et al. [24] introduced a hybrid evolutionary algorithm called hybrid bacterial foraging particle swarm optimization (hbfpso). Hbfpso combines the speed and position update strategy of particle swarm optimization (PSO) and the propagation and diffusion elimination strategy of bacterial foraging algorithm (BFA). KORA et al. [25] introduced an abnormal heartbeat detection algorithm based on BFO and particle swarm optimization (PSO), improved BFO, and combined it with wavelet transform, machine learning, support vector machine and other transformation technologies. Hakimuddin et al. [26] proposed an AGC controller design method based on bacterial foraging algorithm, and compared the influence of BFA tuned AGC controller and genetic algorithm tuned AGC controller on the dynamic response of power system. Hu et al. [27] optimized and improved the algorithm. Compared with genetic algorithm, the improved BFO algorithm is superior to genetic algorithm in speed and quality. Gao et al. [28] proposed decreasing composite function and gradient migration behavior to solve the problems of bacterial foraging algorithm, and introduced 80 / 20 rule to improve the convergence speed. Secondly, the update speed of particles was introduced, another compound function was proposed, and the biological characteristics of Escherichia coli were introduced, which realized the screening of excellent individuals. Xu et al. [29] proposed a virtual network embedding method based on bacterial foraging optimization (BFO) algorithm, aiming to make more effective and reasonable use of the underlying resources. Chen et al. [30] proposed a new bacterial foraging optimization (BFO) swarm intelligence algorithm. Pei et al. [31] proposed a hybrid algorithm combining bacterial foraging (BF) and variable neighborhood search (VNS), and verified the performance of the hybrid algorithm and other famous algorithms. Hans et al. [32] combined sine cosine algorithm (SCA) with ant colony optimization algorithm (alo) to form a hybrid sine cosine ant colony optimization algorithm (scalo). Mohammad et al. [33] proposed a hybrid bacterial foraging sine cosine algorithm for global optimization problem. Long et al. [34] proposed a new BFO algorithm based on grid partition to solve this problem. As-bfo has advantages in USV global path planning. Lenin et al. [35] proposed an improved bacterial foraging optimization algorithm (obfo) to solve the optimal reactive power problem. Chen et al. [36] proposed an enhanced BFO algorithm based on chaotic chemotaxis step size, Gaussian mutation and chaotic local search, which overcomes the shortcomings of slow convergence speed, unable to jump out of the local optimal solution and fixed step size. Pan et al. [37] proposed a bacterial foraging optimization algorithm for cell image segmentation and processing. Bfed algorithm can recognize boundaries more effectively and provide more accurate cell image segmentation. Pare et al. [38] compared the application of Rhododendron search algorithm and bacterial foraging algorithm in multi-level threshold segmentation technology, which makes multi-level minimum cross entropy more practical and reduces the complexity. Compared with other methods, this method can select the best threshold more effectively and accurately, and obtain high-quality segmentation image. Panda et al. [39] proposed an improved bacterial foraging optimization algorithm, namely adaptive crossover bacterial foraging optimization algorithm, which combines adaptive chemotaxis and inherits the crossover mechanism of genetic algorithm. Acbfoa algorithm is used to find the optimal principal component of dimension reduction. The effectiveness of the proposed acbfo Fisher algorithm is verified by comparing with the results of existing methods. Hooshmand et al. [40] developed a method to solve device congestion based on mixed bacterial foraging and Nelder Mead algorithm.

Therefore, the main contributions of this paper are:

• Apply the bacterial foraging algorithm to the enterprise knowledge worker scheduling problem and apply it to solve the multi-scenario and multi-dimensional scheduling problem.

• Based on the optimal foraging theory, the bacterial foraging algorithm is optimized in many aspects, and a more effective foraging strategy is proposed to improve the control accuracy of the step size, and the speed of the algorithm is improved by introducing an adaptive mechanism.

• The performance simulation experiment of the knowledge worker sample scheduling in the studied enterprise proves that our method has better algorithm accuracy and efficiency.

The structure of the paper is divided into the following parts: the first part analyzes the advantages of bacterial foraging algorithm in solving the scheduling optimization problem of knowledge workers, and discusses the feasibility of bacterial foraging algorithm in solving the scheduling optimization problem of knowledge workers quickly and accurately, and it is very effective. The second part analyzes the mathematical principle and implementation process of bacterial foraging algorithm. The third part provides the optimization strategy for the application of bacterial foraging algorithm to solve the conventional and unconventional foraging problems, and studies the accuracy, speed and probability of bacterial foraging algorithm to solve the N-P problem. The main contents of this paper are as follows: the second part introduces the principle and advantages of bacterial foraging algorithm, and puts forward the original intention of this paper. The third part gives the implementation process and analysis type of bacterial foraging algorithm. The fourth part puts forward the optimization strategy of bacterial foraging algorithm, and introduces the implementation method of the optimization strategy. The fifth part summarizes the research content of bacterial foraging algorithm, summarizes the performance test of bacterial foraging algorithm and the comparison results with other algorithms. Task optimization bacterial foraging algorithm has an important application effect in the construction of knowledge worker scheduling optimization model.