A bacteria foraging algorithm based cell formation considering operation time

The cellular manufacturing system (CMS) is considered as an efficient production strategy for batch type production. The CMS relies on the principle of grouping machines into machine cells and grouping machine parts into part families based on pertinent similarity measures. The bacteria foraging algorithm (BFA) is a new in development computation technique extracted from the social foraging behavior of Escherichia coli (E. coli) bacteria. Ever since Kevin M. Passino invented the BFA, one of the main challenges has been employment of the algorithm to problem areas other than those for which the algorithm was proposed. This research work inquires the first applications of this emerging novel optimization algorithm to the cell formation (CF) problem. In addition, a newly developed BFA-based optimization algorithm for CF is discussed. In this paper, an attempt is made to solve the cell formation problem meanwhile taking into consideration number of voids in cells and a number of exceptional elements based on operational time of the parts required for processing in the machines. The BFA is suggested to create machine cells and part families. The performance of the proposed algorithm is compared with a number of algorithms that are most commonly used and reported in the corresponding scientific literature such as similarity coefficients methods (SCM), rank order clustering (ROC), ZODIAC, GRAFICS, MST, GATSP, GP, K-harmonic clustering (KHM), K-means clustering, C-link clustering, modified ART1, GA (genetic algorithm), evolutionary algorithm (EA), and simulated annealing (SA) using defined performance measures known as modified grouping efficiency and grouping efficacy. The results lie in favor of better performance of the proposed algorithm.     

Development of bacteria foraging optimization algorithm for cell formation in cellular manufacturing system considering cell load variations

The cellular manufacturing system (CMS) is considered as an efficient production strategy for batch type production. The CMS relies on the principle of grouping machines into machine cells and grouping machine parts into part families on the basis of pertinent similarity measures. The bacteria foraging optimization (BFO) algorithm is a modern evolutionary computation technique derived from the social foraging behavior of Escherichia coli bacteria. Ever since Kevin M. Passino invented the BFO, one of the main challenges has been the employment of the algorithm to problem areas other than those of which the algorithm was proposed. This paper investigates the first applications of this emerging novel optimization algorithm to the cell formation (CF) problem. In addition, for this purpose matrix-based bacteria foraging optimization algorithm traced constraints handling (MBATCH) is developed. In this paper, an attempt is made to solve the cell formation problem while considering cell load variations and a number of exceptional elements. The BFO algorithm is used to create machine cells and part families. The performance of the proposed algorithm is compared with a number of algorithms that are most commonly used and reported in the corresponding scientific literature such as K-means clustering, the C-link clustering and genetic algorithm using a well-known performance measure that combined cell load variations and a number of exceptional elements. The results lie in favor of better performance of the proposed algorithm.     

离散型细菌觅食算法求解TSP

旅行商问题(TSP)是组合优化问题的典型代表,针对TSP的求解提出一种离散型细菌觅食(DBFO)算法.该算法通过结合2-opt算法设计了一种适合处理离散型变量的趋化算子,将细菌觅食算法推广到了离散情形.同时,结合TSP的特点,在迁徙算子中引入基因库的思想来指导新个体的生成,提高了算法的搜索效率.通过对TSPLIB标准库中22个实例进行仿真实验.实验结果表明,该算法能够有效求解城市规模500以下的TSP,与混合蚁群算法和离散型萤火虫群算法相比,具有更好的全局收敛性和稳定性.     

改进细菌觅食算法求解车间作业调度问题*

针对细菌觅食算法（BFOA）求解高维优化问题时容易陷入局部最优和早熟的问题,引入自适应步长及差分进化算子,并将改进算法用于车间作业调度问题（JSP）中。求解时,设计了一种编码转换方案,从而无须修改BFOA运算规则即可实现对JSP的寻优;同时,采用空闲时间片段优化策略降低了调度问题的复杂性。仿真实验表明,该算法能够跳出局部最优,避免了早熟的问题,调度结果优于原始细菌觅食算法和离散粒子群算法。     

A fast projection algorithm for sequence data searching

In real life, data often appear in the form of sequences and this form of data is called sequence data. In this paper, a new definition on sequence similarity and a novel algorithm, Projection Algorithm, for sequence data searching are proposed. This algorithm is not required to access every datum in a sequence database. However, it guarantees that no qualified subsequence is falsely rejected. Moreover, the projection algorithm can be extended to match subsequences with different scales. With careful selection of parameters, most of the similar subsequences with different scales can be retrieved. We also show by experiments that the proposed algorithm can outperform the traditional sequential searching algorithm up to 96 times in terms of speed up.     

细菌觅食优化算法的研究与应用

细菌觅食优化算法是进化算法家族的新成员。首先对细菌觅食优化算法的三大主要操作：趋向性、复制和迁徙操作的基本原理及流程进行介绍,然后对算法求解优化问题的设计步骤进行分析,接着探讨算法的改进和应用,最后指出细菌觅食优化算法的未来研究方向。     

A functional motif discovery algorithm for invertebrate EST sequence data

We have developed a new algorithm for invertebrate expressed sequence tag (EST) analysis, termed as the fmEST algorithm, which consists of a systematic homology search, functional motif scanning, and clustering alignment. This study was undertaken to evaluate the validity of our fmEST algorithm in functional motif discovery for invertebrate EST sequence data. Out of 200 unidentified invertebrate ESTs, including 100 arthropod ESTs and 100 mollusk ESTs, 18 arthropod ESTs and 21 mollusk ESTs were identified as fmESTs that contained functional motifs. The nucleotide lengths of arthropod fmEST and mollusk fmEST sequences were distributed from 388 to 954 bp and from 222 to 742 bp, respectively. This result allowed us to annotate these invertebrate fmESTs as various functional genes, while they showed no significant homology to the gene information recorded in the international DNA databases using the conventional BLAST homology search program. In addition, another 1 arthropod EST and 23 mollusk ESTs were assembled into contigs with any identified fmESTs by clustering alignment. Based on these findings, we have concluded that our fmEST algorithm, involving the functional motif discovery procedure, is a valuable approach, enabling us to break new ground in undeveloped invertebrate EST analysis. This work was presented in part at the 11th International Symposium on Artificial Life and Robotics, Oita, Japan, January 23–25, 2006     

A genetic algorithm with proper parameters for manufacturing cell formation problems

One fundamental problem in cellular manufacturing is the formation of product families and machine cells. Many solution methods have been developed for the cell formation problem. Since efficient grouping is the prerequisite of a successful Cellular Manufacturing installation the research in this area will likely be continued. In this paper, we consider the problem of cell formation in cellular manufacturing systems with the objective of maximizing the grouping efficacy. We propose a Genetic Algorithm (GA) to obtain machine-cells and part-families. Developed GA has three different selection and crossover operators. The proper operators and parameters of the GA were determined by design of experiments. A set of 15 test problems with various sizes drawn from the literature is used to test the performance of the proposed algorithm. The corresponding results are compared to several well-known algorithms published. The comparative study shows that the proposed GA improves the grouping efficacy for 40% of the test problems.     

A spectral clustering algorithm for manufacturing cell formation

A graph clustering algorithm constructs groups of closely related parts and machines separately. After they are matched for the least intercell moves, a refining process runs on the initial cell formation to decrease the number of intercell moves. A simple modification of this main approach can deal with some practical constraints, such as the popular constraint of bounding the maximum number of machines in a cell. Our approach makes a big improvement in the computational time. More importantly, improvement is seen in the number of intercell moves when the computational results were compared with best known solutions from the literature.     

Optimizing supply chain network for perishable products using improved bacteria foraging algorithm

In a supply chain environment, time delay has a significant impact on the success of perishable products. A major concern is therefore aimed at development of a holistic optimized approach in a supply chain environment for perishable products. Thus, integration of production, inventory and, distribution of perishable products in a supply chain environment are the challenging tasks for practitioners and researchers. In general, the standard optimal supply chain model cannot work for perishable products. There is therefore, a need for a holistic model that focuses on the consolidation of the processes. Shorter product shelf-life, temperature control, requirement of strict tractability, large number of product variants, and a large volume of goods handled are the major challenges in a supply chain environment for perishable products. The present work focuses on the development of a holistic model which uses improved bacteria forging algorithm (IBFA) for solving the formulated model. We have proposed and analyzed some general properties of the model and, finally applied it to a three-stage supply chain problem using an IBFA. Two case studies have been considered for support and demonstration of the integrated perishable supply chain network problem. Results obtained from IBFA reveal that the proposed model is more useful for decision makers while considering optimal supply chain network for perishable products. Finally, validation of results has been carried out using bacteria forging algorithm (BFA). The computational performance of the proposed algorithm proves that IBFA is instrumental in effectively handling the proposed approach.     

一种采用改进细菌觅食优化算法的图像增强方法

为了解决现有图像增强技术在细节处理方面的不足以及变换后图像直方图分布偏移的情况,提出一种采用改进细菌觅食优化算法的灰度图像增强方法。针对细菌觅食算法在优化高维函数时性能不佳、易陷入早熟收敛的缺陷,将变高维的灰度图像增强问题转化为固定2维的非完全Beta函数的参数最优化问题。仿真实验结果表明了所提出方法的有效性,与其他方法相比,增强后的图像细节表现更自然,直方图分布更均匀,明暗区域分配更合理。     

Evolutionary state-based novel multi-objective periodic bacterial foraging optimization algorithm for data clustering

Clustering divides objects into groups based on similarity. However, traditional clustering approaches are plagued by their difficulty in dealing with data with complex structure and high dimensionality, as well as their inability in solving multi-objective data clustering problems. To address these issues, an evolutionary state-based novel multi-objective periodic bacterial foraging optimization algorithm (ES-NMPBFO) is proposed in this article. The algorithm is designed to alleviate the high-computing complexity of the standard bacterial foraging optimization (BFO) algorithm by introducing periodic BFO. Moreover, two learning strategies, global best individual (gbest) and personal historical best individual (pbest), are used in the chemotaxis operation to enhance the convergence speed and guide the bacteria to the optimum position. Two elimination-dispersal operations are also proposed to prevent falling into local optima and improve the diversity of solutions. The proposed algorithm is compared with five other algorithms on six validity indexes in two data clustering cases comprising nine general benchmark datasets and four credit risk assessment datasets. The experimental results suggest that the proposed algorithm significantly outperforms the competing approaches. To further examine the effectiveness of the proposed strategies, two variants of ES-NMPBFO were designed, and all three forms of ES-NMPBFO were tested. The experimental results show that all of the proposed strategies are conducive to the improvement of solution quality, diversity and convergence.     

A multiple-objective grouping genetic algorithm for the cell formation problem with alternative routings

This paper addresses the cell formation problem with alternative part routings, considering machine capacity constraints. Given processes, machine capacities and quantities of parts to produce, the problem consists in defining the preferential routing for each part optimising the grouping of machines into manufacturing cells. The main objective is to minimise the inter-cellular traffic, while respecting machine capacity constraints. To solve this problem, the authors propose an integrated approach based on a multiple-objective grouping genetic algorithm for the preferential routing selection of each part (by solving an associated resource planning problem) and an integrated heuristic for the cell formation problem.     

A hybrid grouping genetic algorithm for the cell formation problem

The machine-part cell formation problem consists of constructing a set of machine cells and their corresponding product families with the objective of minimizing the inter-cell movement of the products while maximizing machine utilization. This paper presents a hybrid grouping genetic algorithm for the cell formation problem that combines a local search with a standard grouping genetic algorithm to form machine-part cells. Computational results using the grouping efficacy measure for a set of cell formation problems from the literature are presented. The hybrid grouping genetic algorithm is shown to outperform the standard grouping genetic algorithm by exceeding the solution quality on all test problems and by reducing the variability among the solutions found. The algorithm developed performs well on all test problems, exceeding or matching the solution quality of the results presented in previous literature for most problems.     

A novel bacterial foraging optimization algorithm for feature selection

Bacterial foraging optimization (BFO) algorithm is a new swarming intelligent method, which has a satisfactory performance in solving the continuous optimization problem based on the chemotaxis, swarming, reproduction and elimination-dispersal steps. However, BFO algorithm is rarely used to deal with feature selection problem. In this paper, we propose two novel BFO algorithms, which are named as adaptive chemotaxis bacterial foraging optimization algorithm (ACBFO) and improved swarming and elimination-dispersal bacterial foraging optimization algorithm (ISEDBFO) respectively. Two improvements are presented in ACBFO. On the one hand, in order to solve the discrete problem, data structure of each bacterium is redefined to establish the mapping relationship between the bacterium and the feature subset. On the other hand, an adaptive method for evaluating the importance of features is designed. Therefore the primary features in feature subset are preserved. ISEDBFO is proposed based on ACBFO. ISEDBFO algorithm also includes two modifications. First, with the aim of describing the nature of cell to cell attraction-repulsion relationship more accurately, swarming representation is improved by means of introducing the hyperbolic tangent function. Second, in order to retain the primary features of eliminated bacteria, roulette technique is applied to the elimination-dispersal phase.In this study, ACBFO and ISEDBFO are tested with 10 public data sets of UCI. The performance of the proposed methods is compared with particle swarm optimization based, genetic algorithm based, simulated annealing based, ant lion optimization based, binary bat algorithm based and cuckoo search based approaches. The experimental results demonstrate that the average classification accuracy of the proposed algorithms is nearly 3 percentage points higher than other tested methods. Furthermore, the improved algorithms reduce the length of the feature subset by almost 3 in comparison to other methods. In addition, the modified methods achieve excellent performance on wilcoxon signed-rank test and sensitivity-specificity test. In conclusion, the novel BFO algorithms can provide important support for the expert and intelligent systems.     

基于细菌觅食算法和支持向量机的表情识别

为正确选择应用于人脸表情识别的支持向量机相关参数,提高表情识别准确率,提出一种应用于表情识别的基于细菌觅食算法的支持向量机参数选择方法。利用细菌觅食算法,通过模拟细菌觅食行为的趋向性操作、复制操作和迁移操作对应用于表情识别的支持向量机的参数进行寻优,避免寻优陷入局部最优,实现参数优化。实验结果表明,采用该方法能够使人脸表情识别分类结果具有更高的准确率。     

Optimal foraging algorithm for global optimization

An optimization algorithm, inspired by the animal Behavioral Ecology Theory—Optimal Foraging Theory, named the Optimal Foraging Algorithm (OFA) has been developed. As a new stochastic search algorithm, OFA is used to solve the global optimization problems following the animal foraging behavior. During foraging, animals know how to find the best pitch with abundant prey; in establishing OFA, the basic operator of OFA was constructed following this foraging strategy. During foraging, an individual of the foraging swarms obtained more opportunities to capture prey through recruitment; in OFA the recruitment was adopted to ensure the algorithm has a higher chance to receive the optimal solution. Meanwhile, the precise model of prey choices proposed by Krebs et al. was modified and adopted to establish the optimal solution choosing strategy of OFA. The OFA was tested on the benchmark functions that present difficulties common to many global optimization problems. The performance comparisons among the OFA, real coded genetic algorithms (RCGAs), Differential Evolution (DE), Particle Swarm Optimization (PSO) algorithm, Bees Algorithm (BA), Bacteria Foraging Optimization Algorithm (BFOA) and Shuffled Frog-leaping Algorithm (SFLA) are carried out through experiments. The parameter of OFA and the dimensions of the multi-functions are researched. The results obtained by experiments and Kruskal-Wallis test indicate that the performance of OFA is better than the other six algorithms in terms of the ability to converge to the optimal or the near-optimal solutions, and the performance of OFA is the second-best one from the view of the statistical analysis.     

Multi-colony bacteria foraging optimization with cell-to-cell communication for RFID network planning

In order to obtain accurate and reliable network planning in the Radio Frequency Identification (RFID) communication system, the locations of readers and the associated values for each of the reader parameters have to be determined. All these choices must optimize a set of objectives, such as tag coverage, economic efficiency, load balance, and interference level between readers. This paper proposes a novel optimization algorithm, namely the multi-colony bacteria foraging optimization (MC-BFO), to solve complex RFID network planning problem. The main idea of MC-BFO is to extend the single population bacterial foraging algorithm to the interacting multi-colony model by relating the chemotactic behavior of single bacterial cell to the cell-to-cell communication of bacterial community. With this multi-colony cooperative approach, a suitable diversity in the whole bacterial community can be maintained. At the same time, the cell-to-cell communication mechanism significantly speeds up the bacterial community to converge to the global optimum. Then a mathematical model for planning RFID networks is developed based on the proposed MC-BFO. The performance of MC-BFO is compared to both GA and PSO on RFID network planning problem, demonstrating its superiority.     

一种改进细菌觅食优化算法及其在软测量建模中的应用

针对软测量建模中模型参数的优化需求,在分析细菌觅食优化算法(BFOA)和粒子群优化(PSO)算法的基础上,将二者有机结合,提出了一种新型细菌觅食粒子群混合优化算法(BSOA)。该算法将PSO粒子移动的思想引入BFOA,有效解决了BFOA趋向性操作中细菌位置更新的盲目性。将其分别用于典型函数的寻优与成品油研究法辛烷值最小二乘支持向量机(LSSVM)模型参数的优化,仿真结果表明:该方法有效增强了算法的全局寻优能力与收敛速度,并在一定程度上改善了模型的预测精度与泛化能力。