细菌觅食优化算法的研究进展

细菌觅食优化算法是近年来发展起来的,基于大肠杆菌觅食行为模型的一种新型智能算法。它具有对初值和参数选择不敏感、鲁棒性强、简单易于实现,以及并行处理和全局搜索等优点。但其在应用过程中存在精度不够高、收敛速度不够快的缺点。文中首先对细菌觅食优化算法的基本原理及操作流程进行介绍,并概述了国内外学者在这一领域的研究现状,接着分析了算法三大主要操作存在的问题,然后探讨了算法的改进和应用,最后分析了算法未来的研究方向。     

基于细菌觅食优化算法的多色彩空间嘴唇分割

结合多色彩空间处理、细菌觅食优化算法(BFOA)与关键点定位算法,提出一种新的嘴唇轮廓提取算法。对嘴唇图像进行色彩空间的转换,在不同的色彩空间对嘴唇图像进行色彩增强及色彩分割处理,利用BFOA结合Kapur最大熵阈值法对嘴唇区域图像求取最佳阈值,实现唇部精确分割。提取嘴唇的边缘关键点,并以提取到的关键点为输入,运用最小二乘曲线拟合的方法拟合嘴唇边缘轮廓,准确提取嘴唇的外部轮廓。实验结果表明,该方法能够完成对嘴唇的精确分割,并获得较高的分割准确率。     

Bacterial Foraging Optimization Approach to Portfolio Optimization

In this paper we propose a heuristic approach based on bacterial foraging optimization (BFO) in order to find the efficient frontier associated with the portfolio optimization (PO) problem. The PO model with cardinality and bounding constraints is a mixed quadratic and integer programming problem for which no exact algorithms can solve in an efficient way. Consequently, various heuristic algorithms, such as genetic algorithms and particle swarm optimization, have been proposed in the past. This paper aims to examine the potential of a BFO algorithm in solving the PO problem. BFO is a new swarm intelligence technique that has been successfully applied to several real world problems. Through three operations, chemotaxis, reproduction, and elimination-dispersal, the proposed BFO algorithm can effectively solve a PO problem. The performance of the proposed approach was evaluated in computational tests on five benchmark data sets, and the results were compared to those obtained from existing heuristic algorithms. The proposed BFO algorithm is found to be superior to previous heuristic algorithms in terms of solution quality and time.     

改进的菌群算法研究

为了提高菌群寻优算法（ Bacterial Foraging Optimization, BFO）的搜索能力和解决多峰值复杂适应度函数模型避免过早收敛的问题,文中对原始菌群算法进行改进,提出多峰值菌群算法。将寻优过程分成两个时期,前期和原始菌群算法相同,在菌群收敛的后期,加入峰值数目和区间的判断,将区间编号,保证区间内部单峰值;然后在区间内部迭代运行菌群搜索,独立寻优,在多峰值和较复杂模型的情况下进行研究和评估。实验表明,在收敛速度、收敛稳定性和寻找全局最优方面均优于原始菌群算法。     

Fuzzy assembly line balancing using genetic algorithms

In this paper, we implement genetic algorithms to synthesis fuzzy assembly line balancing problem which is well-known as a NP-hard problem. The genetic operators concerned with the feasibility of chromosomes will be discussed, and its performance will be shown with a numerical example.     

基于改进细菌觅食的协作学习分组算法

针对协作学习中基于学习者特征的分组方式对学习过程的影响,设计一种基于改进细菌觅食的协作学习分组算法。在实现协作学习分组过程中,引入分组调节因子和特征权值,满足不同教学活动对学习者多个特征及分组的要求。为构成有效的分组空间,在细菌种群初始化中,细菌群体以实数编码,并加入随机扰动以增加细菌种群的多样性;在算法后期加入二次变异操作,以避免细菌觅食算法可能出现的早熟收敛现象。仿真实验结果表明,该算法在不同分组形式下,与传统算法相比,具有较优的分组性能和较高的准确率,并且对于不同数据集规模具有良好的稳定性。     

Multiobjective memetic algorithms for time and space assembly line balancing

This paper presents three proposals of multiobjective memetic algorithms to solve a more realistic extension of a classical industrial problem: time and space assembly line balancing. These three proposals are, respectively, based on evolutionary computation, ant colony optimisation, and greedy randomised search procedure. Different variants of these memetic algorithms have been developed and compared in order to determine the most suitable intensification–diversification trade-off for the memetic search process. Once a preliminary study on nine well-known problem instances is accomplished with a very good performance, the proposed memetic algorithms are applied considering real-world data from a Nissan plant in Barcelona (Spain). Outstanding approximations to the pseudo-optimal non-dominated solution set were achieved for this industrial case study.     

A simulated annealing algorithm for multi-manned assembly line balancing problem

Assembly line balancing problems with multi-manned workstations usually occur in plants producing high volume products (e.g. automotive industry) in which the size of the product is reasonably large to utilize the multi-manned assembly line configuration. In these kinds of assembly lines, usually there are multi-manned workstations where a group of workers simultaneously performs different operations on the same individual product. However, owing to the high computational complexity, it is quite difficult to achieve an optimal solution to the balancing problem of multi-manned assembly lines with traditional optimization approaches. In this study, a simulated annealing heuristic is proposed for solving assembly line balancing problems with multi-manned workstations. The line efficiency, line length and the smoothness index are considered as the performance criteria. The proposed algorithm is illustrated with a numerical example problem, and its performance is tested on a set of test problems taken from literature. The performance of the proposed algorithm is compared to the existing approaches. Results show that the proposed algorithm performs well.     

A new heuristic approach to assembly line balancing

The main feature of the method suggested in this paper is the assignment of priority to elements and priority elements are preferred to non-priority elements when assigning elements to stations. It gives the minimum number of stations under a predetermined cycle time. The work element time is considered to be invariant. This method has been tested by solving nearly all the problems available in the pertinent literature. This method yields better or similar results as available in the literature. A computer program incorporating the new heuristic method is presented in the paper.     

基于粒子群优化的细菌觅食优化算法

细菌觅食优化算法(BFOA)具有全局搜索能力强的优点,但存在收敛速度慢的缺陷.为了解决以上问题,结合收敛速度快的粒子群优化算法,提出一种基于粒子群优化的细菌觅食优化算法(BF-PSO),该改进的优化算法具有可操作性和优越性.选用测试函数和对PID控制参数整定的实例进行Matlab仿真,结果进一步显示了BF-PSO的优化能力优于BFOA,收敛速度快,且具有较好的鲁棒性.     

An efficient approach for type II robotic assembly line balancing problems

In the past decades, robots have been extensively applied in assembly systems as called robotic assembly lines. When changes in the production process of a product take place, the line needs to be reconfigured in order to improve its productivity. This study presents a type II robotic assembly line balancing (rALB-II) problem, in which the assembly tasks have to be assigned to workstations, and each workstation needs to select one of the available robots to process the assigned tasks with the objective of minimum cycle time. An innovative genetic algorithm (GA) hybridized with local search is proposed for the problem. The genetic algorithm uses a partial representation technique, where only part of the decision information about a candidate solution is expressed in the chromosome and the rest is computed via a heuristic method. Based on different neighborhood structures, five local search procedures are developed to enhance the search ability of GA. The coordination between these procedures is well considered in order to escape from local optima and to reduce computation time. The performance of the hybrid genetic algorithm (hGA) is tested on 32 rALB-II problems and the obtained results are compared with those by other methods.     

A solution procedure for type E simple assembly line balancing problem

This paper presents a type E simple assembly line balancing problem (SALBP-E) that combines models SALBP-1 and SALBP-2. Furthermore, this study develops a solution procedure for the proposed model. The proposed model provides a better understanding of management practice that optimizes assembly line efficiency while simultaneously minimizing total idle time. Computational results indicated that, under the given upper bound of cycle time (ct_max), the proposed model can solve problems optimally with minimal variables, constraints, and computing time.     

Discrete cuckoo search algorithms for two-sided robotic assembly line balancing problem

Neural Computing and Applications - Robotics are extensively utilized in modern industry to replace human labor and achieve high automation and flexibility. In order to produce large-size products,...     

A new method for assembly line balancing having stochastic work elements

The main feature of the method suggested in this paper is the assignment of priority to elements and priority elements are preferred to non-priority elements when assigning elements to stations. It accepts element times from known or unknown symmetrical distributions, minimizes the variation within a station and allows assignments of elements to stations such that each station time does not exceed the probability confidence levels set by management.     

A Lagrangian relaxation technique for the general assembly line balancing problem

This paper addresses the problem of balancing assembly or fabrication lines. In order to achieve a given production rate or to optimize the use of workstations, one has to tackle the problem of balancing the production lines. It is well known that this problem belongs to the class of NP-hard problems. In this paper the polyhedron of the feasible solutions of the assembly line balancing problem is first studied. Then a Lagrangian relaxation algorithm that incorporates the set of cycle constraints in the objective function is proposed. These constraints are the complicating restrictions in the model. The relaxed problem has the interesting property that its linear programming relaxation always has integer optimal solutions. The subgradient algorithm is then used to maximize the Lagrangian dual. A heuristic is also used to find primal feasible solutions for the original line balancing integer program. These two bounds are then used to reduce the size of the branch-and-bound tree.     

A meta-heuristic algorithm for the fuzzy assembly line balancing type-E problem

Consideration is given to a single-model assembly line balancing problem with fuzzy task processing times. The problem referred to herein as f-SALBP-E consists of finding a combination of the number of workstations and the cycle time as well as a respective line balance such that the efficiency of the line is maximized. f-SALBP-E is an extension of the classical SALBP-E under fuzziness. First, a formulation of the problem is given with the tasks processing times presented by triangular fuzzy membership functions. Then, since the problem is known to be NP-hard, a meta-heuristic based on a Genetic Algorithm (GA) is developed for its solution. The performance of the proposed solution approach is studied and discussed over multiple benchmarks test problems taken from the open literature. The results demonstrate very satisfactory performance for the developed approach in terms of both solution time and quality.     

基于细菌觅食机理改进粒子群算法的研究

粒子群算法与细菌觅食算法在优化问题中均体现了较好的性能,但由于各自特定的进化机制,也都存在缺点。粒子群优化（PSO）算法在优化过程中过快陷入局部极值,为了避免这个缺陷,提出了一种新的混合算法。通过PSO算法完成整个空间的全局搜索,通过细菌觅食算法（BFOA）中的趋向性运动算子完成局部搜索的功能,再通过典型函数进行测试,结果表明新算法可以有效弥补细菌觅食算法速度不快和粒子群算法精度不高的缺陷,同时部分地避免了局部收敛的问题,从而适用于解决复杂函数的优化问题。     

基于细菌觅食算法的分布式电源配电网重构研究

分布式电源由于可以降低网损,在节能减排与可持续发展中发挥着巨大的作用,如今越来越受到人们的重视。而配电网重构已经不再是单一考虑开关的开闭组合的优化,还必须考虑分布式电源的影响。文章根据配电网自身特点对细菌觅食算法进行了改进,提高了其效率,建立了系统网络损耗的目标函数,并进行了仿真验证。     

考虑多专业协同的装配线平衡问题研究

为改善飞机总装线平衡效果,提出考虑多专业协同分配的第一类装配线平衡方法。首先,分析各专业间的关联性,建立基于动态模糊聚类的专业划分方法,获取资源-功能的多专业集合;然后,根据飞机总装环境及所涉及专业的特点,建立了多专业协同分配的平衡模型;再根据专业划分结果及作业分配规则,提出了混合模拟退火-遗传算法(HSAGA),将专业间关联度高的作业分配到工作面(指专业关联度高的作业的集合,也指能完成一项装配任务的作业执行操作空间),实现工作面数的最小化;最后,以某型飞机总装作业为例,验证了平衡方法的有效性。