基于BFOA算法的配电网DG选址定容方法

发展分布式能源系统对于实现的“碳达峰”和“碳中和”,提升可再生能源的开发利用具有重要意义。提出一种基于细菌觅食优化算法（BFOA）的配电网分布式电源（DG）选址定容方法。建立以配电网的功率损耗指数、电压偏差以及安装分布式电源所降低的净运行成本最小为目标的数学模型及约束条件,提出损耗敏感系数（LSF）来确定DG安装位置,并引用BFOA算法求解DG的最佳容量。仿真表明,相对于传统优化算法,BFOA算法在模型求解时间和收敛速度上具有明显优势,所提规划方法能够最大限度地降低功率损耗和运行成本,并提高系统的电压稳定性。     

Research on optimal schedule strategy for active distribution network using particle swarm optimization combined with bacterial foraging algorithm

Comparing with the traditional distribution network, a significant feature of the active distribution network (ADN) is that the performance of distributed generation (DG) units, energy storage units and micro-grid (MG) in the network is controllable for the distribution network operator. Considering the characteristics of the distributed power supply and micro-grid, and giving full play to the advantages of distributed generation technology in the economic, environmental and energy aspects, this paper highlights an environmental protection and energy saving optimal schedule model for ADN. The scheduling model focuses on the minimum network loss, minimum voltage deviation and minimum difference between peak and valley load. In addition, the two stage algorithm is presented to solve the proposed multi-objective scheduling model of ADN. First, a set of Pareto solutions are obtained by using the proposed particle swarm optimization combined with bacterial foraging algorithm (PSO-BFO) to solve multi-objective optimization problems, then the optimal schedule strategy of ADN is gained through evaluating the Pareto solutions with entropy weight decision-making method. To avoid the search falling into local optimal solution, the two-value crossover operator is introduced to exchange the information among subpopulations and update the position of related particles. Meanwhile, the adaptive adjusting inertia constant strategy is used to improve the algorithm convergence speed. Finally, the case study results demonstrate the rationality of the proposed optimal schedule model and the validity of its solution algorithm for ADN.     

Optimal SSSC design for damping power systems oscillations via Gravitational Search Algorithm

In recent years, various heuristic optimization methods have been developed. Many of these methods are inspired by swarm behaviors in nature. In this paper, a new optimization algorithm namely Gravitational Search Algorithm (GSA) based on the law of gravity and mass interactions is illustrated for designing Static Synchronous Series Compensator (SSSC) for single and multimachine power systems. In the proposed algorithm, the searcher agents are a collection of masses which interact with each other based on the Newtonian gravity and the laws of motion. The proposed method has been compared with some well-known heuristic search methods. The obtained results confirm the high performance of the proposed method in tuning SSSC compared with Bacteria Foraging (BF) and Genetic Algorithm (GA). Moreover, the results are presented to demonstrate the effectiveness of the proposed controller to improve the power systems stability over a wide range of loading conditions.     

群智能算法的理论及应用综述

群智能是由自然或人造的分散自组织系统所表现出来的集体智能.群智能包含一组简单的个体,其中个体与个体、个体与环境之间存在局部交互行为.虽然个体遵循非常简单的规则,但是微观的交互最终还是导致了宏观的智能行为.在本文中,我们对典型群智能方法的起源、发展、理论、技术、应用等做了深入的研究,包括了蚁群优化、粒子群优化、人工蜂群、细菌觅食优化、萤火虫共五类算法.文末提出群智能发展的六个方向.     

Responses of White-Faced Capuchins (Cebus capucinus) to Naturalistic and Experimentally Presented Food-Associated Calls.

In the context of foraging, many animal species produce specific calls that attract others. Researchers hypothesize that these vocalizations function to inform others about food; however, few studies have investigated whether food-associated calls alone are sufficient to cause individuals to respond as if they have been informed about food. Playback experiments on white-faced capuchins (Cebus capucinus) investigated whether listeners could infer the presence of food merely by hearing food-associated calls. Recipients looked significantly longer toward the call source and approached the speaker more often after hearing food-associated calls, as compared with control calls. Because these responses are not indicative of feeding, it is unclear whether listeners associated the calls with food. Nonetheless, these responses, specific to food-associated calls, may increase individuals' chances of finding food. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Information Foraging with a Proximity-Based Browsing Tool

This paper describes a user friendly, powerful information foraging tool. Document sets are presented through combinations of traditional ranked lists and 2-dimensional proximity-based visualisations, created by uniting graph-theoretic clustering and force-directed layout techniques, where article positions are determined by inter-document similarities. By using Gestalt principles and information encoding, the simple layout improves search efficiency by leveraging human cognitive strengths that have generally been under-utilised in commercial GUI development. In this paper, design and realisation of the layout technique are described in the context of an article browsing framework. Results of an indicative comparative laboratory study, which evaluates the client application – and in particular Graph-Theoretic Force-Directed (GTFD) visualisations against traditional search engine interfaces – are then presented. This study demonstrates the advantage of graphical presentations when browsing an article collection. Finally, potential improvements identified during the study are discussed, as are future directions for this approach to collection browsing     

Integrating Insect Life History and Food Plant Phenology: Flexible Maternal Choice Is Adaptive

Experience of insect herbivores and their natural enemies in the natal habitat is considered to affect their likelihood of accepting a similar habitat or plant/host during dispersal. Growing phenology of food plants and the number of generations in the insects further determines lability of insect behavioural responses at eclosion. We studied the effect of rearing history on oviposition preference in a multivoltine herbivore (Pieris brassicae), and foraging behaviour in the endoparasitoid wasp (Cotesia glomerata) a specialist enemy of P. brassicae. Different generations of the insects are obligatorily associated with different plants in the Brassicaceae, e.g., Brassica rapa, Brassica nigra and Sinapis arvensis, exhibiting different seasonal phenologies in The Netherlands. Food plant preference of adults was examined when the insects had been reared on each of the three plant species for one generation. Rearing history only marginally affected oviposition preference of P. brassicae butterflies, but they never preferred the plant on which they had been reared. C. glomerata had a clear preference for host-infested B. rapa plants, irrespective of rearing history. Higher levels of the glucosinolate breakdown product 3-butenyl isothiocyanate in the headspace of B. rapa plants could explain enhanced attractiveness. Our results reveal the potential importance of flexible plant choice for female multivoltine insects in nature.     

Development of skilled detection and extraction of embedded prey by wild brown capuchin monkeys (cebus apella apella).

Brown capuchin monkeys (Cebus apella apella) in Suriname forage on larvae enclosed within healthy bamboo stalks. We applied the searching versus handling dichotomy of optimal foraging models to highlight developmental processes contributing to foraging on embedded prey. Larvae acquisition begins with search; selecting an appropriate stalk and locating the embedded larvae; followed by handling, ripping the stalk open, and extracting the larvae. Although extraction behaviors were present at low rates in infant capuchins' repertoire, we found that the acquisition of adequate detection techniques was fully completed in subadults, aged around 6. Selecting appropriate foraging substrates, requiring accurate assessment of the physical properties of bamboo stalks, gradually increased with age and experience. We showed that both components of searching required extended practice beyond that required to master the handling components. We conclude that the developmental sequence of skill acquisition goes counter to the preceding logical sequence of foraging process and suggest that searching components present greater challenges than handling components in extractive foraging. Specifying the searching components of foraging more precisely will enhance understanding of species variation in the developmental schedule of foraging skills. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Bacteria foraging optimization algorithm based load frequency controller for interconnected power system

Social foraging behavior of Escherichia coli bacteria has recently been explored to develop a novel algorithm for distributed optimization and control. The Bacterial Foraging Optimization Algorithm (BFOA), as it is called now, is currently gaining popularity in the community of researchers, for its effectiveness in solving certain difficult real world optimization problems. This paper proposes BFOA based Load Frequency Control (LFC) for the suppression of oscillations in power system. A two area non-reheat thermal system is considered to be equipped with proportional plus integral (PI) controllers. BFOA is employed to search for optimal controller parameters by minimizing the time domain objective function. The performance of the proposed controller has been evaluated with the performance of the conventional PI controller and PI controller tuned by genetic algorithm (GA) in order to demonstrate the superior efficiency of the proposed BFOA in tuning PI controller. Simulation results emphasis on the better performance of the optimized PI controller based on BFOA in compare to optimized PI controller based on GA and conventional one over wide range of operating conditions, and system parameters variations.     

基于集合的细菌群优化算法

针对细菌觅食优化算法求解高维优化问题时不易跳出局部最优解的问题,引入趋向方向余弦向量和随时间变化的加速系数,控制细菌觅食优化算法的收敛精度和收敛速度,并将改进算法用于求解组合优化问题。依据细菌种群密度计算原则,设计了一种离散空间和连续空间之间相互转换的规则,同时用集合对细菌觅食优化算法中的算术运算符形式化描述。仿真试验结果表明:基于集合的细菌群优化算法避免了早熟现象,寻优结果优于蚁群算法且接近基于集合的粒子群算法。