Bacteria foraging optimization in antenna engineering: An application to array fault finding

Finding fault elements in linear antenna arrays using bacteria foraging optimization (BFO) is presented. One of the better options of array diagnosis is to perform it by measuring the radiated field, because in this case, removal of the array from its working site is not required and thereby not interrupting its normal operation. This task of fault finding from far‐field data is designed as an optimization problem where the difference between the far‐field power pattern obtained for a given configuration of failed element(s) and the measured one is minimized w. r. t. the excitations of the array elements. This set of excitations on comparison with the excitations of the original array gives the idea of the fault position and their type, such as either complete fault or partial fault. BFO being relatively new to microwave community when compared with other soft‐computing techniques, its performance was observed w. r. t. time of computation and convergence of the iterative process. Possibility of finding the faults from random sample points and use of minimum number of sample points for array fault finding are the novelties of the present work. © 2012 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2013.     

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.     

Solving cell formation and task scheduling in cellular manufacturing system by discrete bacteria foraging algorithm

We consider a joint decision model of cell formation and task scheduling in cellular manufacturing system under dual-resource constrained (DRC) setting. On one hand, machines and workers are multi-functional and/or multi-skilled, and they are grouped into workstations and cells. On the other hand, there is a processing sequence among operations of the parts which needs to be dispatched to the desirable workstations for processing. Inter-cell movements of parts can reduce the processing times and the makespan but will increase the inter-cell material handling costs. The objective of the problem is to minimise the material handling costs as well as the fixed and operating costs of machines and workers. Due to the NP-hardness of the problem, we propose an efficient discrete bacteria foraging algorithm (DBFA) with elaborately designed solution representation and bacteria evolution operators to solve the proposed problem. We tested our algorithm using randomly generated instances with different sizes and settings by comparing with the original bacteria foraging algorithm and a genetic algorithm. Our results show that the proposed DBFA has better performance than the two compared algorithms with the same running time.     

Communal Foraging Behavior and Recruitment Communication in Gloveria sp.

The caterpillars of Gloveria sp. mark trails with a pheromone they deposit by dragging the ventral surface of the tip of the abdomen along branch pathways as they move between their communal nest and distance feeding sites. The threshold sensitivity of the caterpillar for an extract prepared from the secretory site was approximately 0.5 × 10^–3 caterpillar equivalents/cm of trail. Bioassays show that Gloveria follows neither authentic trails of Malacosoma americanum nor artificial trails prepared from 5-cholestane-3-one, a chemical previously reported to elicit trail following from other social caterpillars. Although our observations show that fed caterpillars mark heavily as they return to their nest, we found no evidence that individual caterpillars are able to recruit hungry nestmates to new food finds. In this species, recruitment to food occurs only after many caterpillars have reinforced a trail to a newly discovered food source. In contrast, hungry caterpillars of the confamilial species M. americanum, tested under identical conditions, responded strongly to the postprandial trails of individual caterpillars and rapidly abandoned depleted sites in favor of new food finds. We postulate that the difference in the efficiency with which these two species recruit nestmates to food evolved in response to differences in the spatial distribution of their food supplies.     

基于GSO-BFA的采煤机双滚筒同步调高控制

为研究采煤机滚筒在煤岩混合复杂工况下的调高连续性,依据液压机构的工作原理建立了滚筒调高机构的动力学模型.基于自适应模糊微分积分滑模(AFDI-SMC)鲁棒性强的优点,采用了萤火虫-细菌觅食(GSO-BFA)算法优化滑模控制器参数条件下的滚筒调高控制方案.为保证双滚筒工作时举升高度的一致性,引入偏差-环形耦合同步控制策略补偿位置偏差,同时采用融合算法(GSO-BFA)对外闭环(滚筒高度-电压)控制器参数进行优化,并分析了双滚筒同步调高性能,且与采用遗传算法(GA)优化的系统同步调高精度相比较.仿真结果表明,采用融合算法优化且结合自适应模糊微分积分滑模的滚筒同步调高系统具有良好的鲁棒性及较高的同步精度.     

A novel bacterial foraging technique for edge detection

A new approach for edge detection using a combination of bacterial foraging algorithm (BFA) and probabilistic derivative technique derived from Ant Colony Systems, is presented in this paper. The foraging behavior of some species of bacteria like Escherichia coli can be hypothetically modeled as an optimization process. A group of bacteria search for nutrients in a way that maximizes the energy obtained per unit time spent during the foraging. The proposed approach aims at driving the bacteria through the edge pixels. The direction of movement of the bacteria is found using a direction probability matrix, computed using derivatives along the possible directions. Rules defining the derivatives are devised to ensure that the variation of intensity due to noise is discarded. Quantitative analysis of the feasibility of the proposed approach and its comparison with other standard edge detection operators in terms of kappa and entropy are given. The effect of initial values of parameters of BFA on the edge detection is discussed.     

Semiochemicals for use with parasitoids: Status and future

Allelochemicals are known to serve important roles at all steps in the host-searching sequence of parasitoids. We discuss the various roles of these allelochemics and the type of information needed to develop their use in pest control, which to date has been very limited. Rapid advancements are being made with respect to airborne chemicals and longer-range foraging behavior. Moreover, recent discoveries have shown that genetic diversity in parasitoid populations and phenotypic plasticity of individuals, together with their physiological state, often result in substantial variations in the response to chemical cues. Successful application of semiochemical-parasitoid systems will require management of these intrinsic parasitoid variables as well as management of the foraging environment. We illustrate emerging technology for such an application. For the immediate future, the development of this technology will allow us to: (1) define the genetic and phenotypic foraging profiles important to consistent and efficient parasitoid foraging, and (2) establish the proper propagation and release procedures and monitoring bioassays necessary to ensure appropriate behavioral and physiological qualities of released organisms. For the long term, we envision technology for comprehensively manipulating the pest/crop environment in ways that would provide foraging stimuli and other needs important to retention and efficiency of parasitoids.     

基于自适应细菌觅食算法的灰度图像增强方法

为提高图像增强的自适应性,首先将细菌的自适应趋向因子引入到细菌觅食算法中,然后将提升的细菌觅食算法和非完全Beta函数结合而去获得最佳的灰度变换参数,最终实现对降质图像的最大程度的自适应增强.仿真实验结果表明,提升的优化算法可以更好的优化Beta函数的参数,因而能更有效地提高图像整体对比度和视觉效果.     

Bacteria foraging optimization for protein sequence analysis on the grid

Scientific applications such as protein sequence analysis require a coordination of resources. This is due to hundreds and hundreds of protein sequences being deposited into data banks by the research community which results in an extensive database search when one wants to find a similar protein sequence. This search becomes easier and the time taken is reduced when it is conducted in a grid environment implemented using the Globus tool kit. This paper proposes the use of Bacteria Foraging Optimization (BFO) for finding similar protein sequences in the existing databases. Usage of BFO further reduces the time taken by a resource to execute the user’s requests. Also, the resources utilized in the proposed method are better balanced compared to the existing scheduling algorithms. Also, it is found that the number of tasks executed is more compared to the existing algorithms even though there is a fall in the execution of tasks as the number of resources increases which might be due to network failure etc. The proposed BFO has been compared with the existing First Come First Serve (FCFS) and Minimum Execution Time (MET) scheduling algorithms and it has been found that the proposed BFO performs well compared to the existing algorithms in terms of makespan, resource utilization and minimization in the case of non-execution of client requests.     

群体机器人觅食任务Eulerian建模与分析

为了提高觅食任务性能,根据蚂蚁觅食原理引入了觅食概率的概念,机器人按照该概率决定是否离开Home区域,通过改变该概率值来调整环境中机器人的数量。为了研究觅食概率对系统性能的影响,利用Eulerian数学建模方法建立了觅食任务模型,并通过仿真实验分析了觅食概率对系统整体性能的影响。实验结果表明:觅食概率越大,环境中机器人数量越增加,机器人之间的干扰也增加,从而降低系统性能。最后通过实验分析给出了觅食概率与环境中任务数量以及机器人数之间的大致关系。