An improved ant colony optimization algorithm and its applications to limit analysis of frame structures

This article presents a modified ant colony systems approach, which allows reduction of the computational effort needed to converge to the optimal solution of a given engineering problem. The proposed algorithm is applied to the evaluation of the plastic load and failure modes of planar frames. The approach is based on the generation of elementary collapse mechanisms and on their linear combination, aimed at minimizing the collapse load factor. Many applications have been performed on frames of different sizes subjected to seismic load scenarios, i.e. permanent vertically distributed and variable concentrated forces at each floor. The values of the collapse load obtained with the proposed algorithm have been compared with those obtained by means of other optimization algorithms and nonlinear pushover analysis. An extended performance comparison between the proposed procedure and standard ant colony system algorithms shows the expected improvements in terms of a great reduction in the computational effort.     

Geographic profiling applied to testing models of bumble-bee foraging

Geographic profiling (GP) was originally developed as a statistical tool to help police forces prioritize lists of suspects in investigations of serial crimes. GP uses the location of related crime sites to make inferences about where the offender is most likely to live, and has been extremely successful in criminology. Here, we show how GP is applicable to experimental studies of animal foraging, using the bumble-bee Bombus terrestris. GP techniques enable us to simplify complex patterns of spatial data down to a small number of parameters (2–3) for rigorous hypothesis testing. Combining computer model simulations and experimental observation of foraging bumble-bees, we demonstrate that GP can be used to discriminate between foraging patterns resulting from (i) different hypothetical foraging algorithms and (ii) different food item (flower) densities. We also demonstrate that combining experimental and simulated data can be used to elucidate animal foraging strategies: specifically that the foraging patterns of real bumble-bees can be reliably discriminated from three out of nine hypothetical foraging algorithms. We suggest that experimental systems, like foraging bees, could be used to test and refine GP model predictions, and that GP offers a useful technique to analyse spatial animal behaviour data in both the laboratory and field.     

A framework to engineer production control strategies and its application in electronics manufacturing

One of the most challenging tasks of companies with complex and discrete production systems is to determine an appropriate production control strategy (PCS). The PCS is crucial, as it influences inventory, costs, and service. We propose a framework to address the most important questions of PCS engineering: limiting work in process, positioning the order penetration point (OPP), and coping with demand uncertainty upstream the OPP. Thereby, uncertain advance demand information in the form of forecasts is considered and desirable characteristics of push and pull strategies are combined. A serial manufacturing system with multiple products, variable processing times, and uncertain advance demand information is examined. The model is formulated as a queuing network model and solved numerically by discrete-event simulation. The framework and a resulting hybrid control strategy are applied to a case study from electronics manufacturing, where a significant improvement potential is identified.     

How structurally stable are global socioeconomic systems?

The stability analysis of socioeconomic systems has been centred on answering whether small perturbations when a system is in a given quantitative state will push the system permanently to a different quantitative state. However, typically the quantitative state of socioeconomic systems is subject to constant change. Therefore, a key stability question that has been under-investigated is how strongly the conditions of a system itself can change before the system moves to a qualitatively different behaviour, i.e. how structurally stable the systems is. Here, we introduce a framework to investigate the structural stability of socioeconomic systems formed by a network of interactions among agents competing for resources. We measure the structural stability of the system as the range of conditions in the distribution and availability of resources compatible with the qualitative behaviour in which all the constituent agents can be self-sustained across time. To illustrate our framework, we study an empirical representation of the global socioeconomic system formed by countries sharing and competing for multinational companies used as proxy for resources. We demonstrate that the structural stability of the system is inversely associated with the level of competition and the level of heterogeneity in the distribution of resources. Importantly, we show that the qualitative behaviour of the observed global socioeconomic system is highly sensitive to changes in the distribution of resources. We believe that this work provides a methodological basis to develop sustainable strategies for socioeconomic systems subject to constantly changing conditions.     

Metabolic buffer analysis reveals the simultaneous,independent control of ATP and adenylate energy ratios

Determining the underlying principles behind biological regulation is important for understanding the principles of life, treating complex diseases and creating de novo synthetic biology. Buffering—the use of reservoirs of molecules to maintain molecular concentrations—is a widespread and important mechanism for biological regulation. However, a lack of theory has limited our understanding of its roles and quantified effects. Here, we study buffering in energy metabolism using control theory and novel buffer analysis. We find that buffering can enable the simultaneous, independent control of multiple coupled outputs. In metabolism, adenylate kinase and AMP deaminase enable simultaneous control of ATP and adenylate energy ratios, while feedback on metabolic pathways is fundamentally limited to controlling one of these outputs. We also quantify the regulatory effects of the phosphagen system—the above buffers and creatine kinase—revealing which mechanisms regulate which outputs. The results are supported by human muscle and mouse adipocyte data. Together, these results illustrate the synergy of feedback and buffering in molecular biology to simultaneously control multiple outputs.     

Optimal homeostasis necessitates bistable control

Bistability is a fundamental phenomenon in nature. In biology, a number of fine properties of bistability have been identified. However, these properties are only consequences of bistability at the physiological level, which do not explain why it had to emerge during evolution. Using optimal homeostasis as the first principle, I find that bistability emerges as an indispensable control mechanism. It is the only solution to a dilemma in glucose homeostasis: high insulin efficiency is required to confer rapidness in plasma glucose clearance, whereas an insulin sparing state is required to guarantee the brain''s safety during fasting. The optimality consideration renders a clear correspondence between the molecular and physiological levels. This new perspective can illuminate studies on the twin epidemics of obesity and diabetes and the corresponding intervening strategies. For example, overnutrition and sedentary lifestyle may represent sudden environmental changes that cause the lose of optimality, which may contribute to the marked rise of obesity and diabetes in our generation. Because this bistability result is independent of the parameters of the mathematical model (for which the result is quite general), some other biological systems may also use bistability to control homeostasis.     

Modeling and simulation of auction-based shop-floor control using parallel computing

The high level of complexity and cost involved in the development, testing, and implementation of software for traditional, hierarchical shop-floor control of automated manufacturing systems has motivated considerable research in recent years on the distributed shop-floor control paradigm. In this paper, we describe a methodology for modeling and simulation of an auction-based shop-floor control scheme in a parallel and distributed computing environment using the Parallel Virtual Machine software library. Compared to traditional discrete-event simulation, this approach provides a more accurate means for modeling and evaluation of the shop-floor behavior under distributed control, and enables rapid prototyping of the actual control software. We discuss the challenges and highlight research opportunities associated with modeling and simulation of distributed shop-floor control systems using parallel and distributed computing.     

The effect of individual variation on the structure and function of interaction networks in harvester ants

Social insects exhibit coordinated behaviour without central control. Local interactions among individuals determine their behaviour and regulate the activity of the colony. Harvester ants are recruited for outside work, using networks of brief antennal contacts, in the nest chamber closest to the nest exit: the entrance chamber. Here, we combine empirical observations, image analysis and computer simulations to investigate the structure and function of the interaction network in the entrance chamber. Ant interactions were distributed heterogeneously in the chamber, with an interaction hot-spot at the entrance leading further into the nest. The distribution of the total interactions per ant followed a right-skewed distribution, indicating the presence of highly connected individuals. Numbers of ant encounters observed positively correlated with the duration of observation. Individuals varied in interaction frequency, even after accounting for the duration of observation. An ant''s interaction frequency was explained by its path shape and location within the entrance chamber. Computer simulations demonstrate that variation among individuals in connectivity accelerates information flow to an extent equivalent to an increase in the total number of interactions. Individual variation in connectivity, arising from variation among ants in location and spatial behaviour, creates interaction centres, which may expedite information flow.     

Nested autoinhibitory feedbacks alter the resistance of homeostatic adaptive biochemical networks

Negative feedback control is a ubiquitous feature of biochemical systems, as is time delay between a signal and its response. Negative feedback in conjunction with time delay can lead to oscillations. In a cellular context, it might be beneficial to mitigate oscillatory behaviour to avoid recurring stress situations. This can be achieved by increasing the distance between the parameters of the system and certain thresholds, beyond which oscillations occur. This distance has been termed resistance. Here, we prove that in a generic three-dimensional negative feedback system the resistance of the system is modified by nested autoinhibitory feedbacks. Our system features negative feedbacks through both input-inhibition as well as output-activation, a signalling component with mass conservation and perfect adaptation. We show that these features render the system applicable to biological data, exemplified by the high osmolarity glycerol system in yeast and the mammalian p53 system. Output-activation is better supported by data than input-inhibition and also shows distinguished properties with respect to the system''s stimulus. Our general approach might be useful in designing synthetic systems in which oscillations can be tuned by synthetic autoinhibitory feedbacks.     

Dynamic selective pressure using hybrid evolutionary and ant system strategies for structural optimization

Genetic algorithms have already been applied to various fields of engineering problems as a general optimization tool in charge of expensive sampling of the coded design space. In order to reduce such a computational cost in practice, application of evolutionary strategies is growing rapidly in the adaptive use of problem‐specific information. This paper proposes a hybrid strategy to utilize a cooperative dynamic memory of more competitive solutions combining indirect information share in ant systems with direct constructive genetic search. Some proper coding techniques are employed to enable testing the method with various sets of control parameters. As a challenging field of interest, its application to structural layout optimization is considered while an example of a traveling salesman problem is also treated as a combinatorial benchmark. Copyright © 2007 John Wiley & Sons, Ltd.     

Dynamic control in automated manufacturing: a knowledge integrated approach†

Flexibly automated facilities permit a wider variety of products as well as objectives for making those products—thus requiring manufacturing control strategies to face an environment of ever present change. To operate in this environment, a system composed of hard automation, flexible automation and humans, which can be responsive to product and process requirements, machine breakdowns and delays, engineering changes and improvement opportunities, is needed. Such a system does not fall into the realm of any current manufacturing solution techniques. Something more than exact optimization, heuristic algorithms or stochastic estimates must be utilized. The research discussed herein describes a dynamic solution strategy to operate in this changing environment with adaptive self-improving characteristics. The proposed methodology for optimizing the control of an automated manufacturing facility is an integrated approach utilizing real-time feedback from the operating facility, direct feedback from a simulation of the facility and guidance from a historical knowledge base. This system is being implemented in a knowledge based environment called CAYENE. CAYENE is a hybrid artificial intelligence system, written in Lisp, based on the idea of using object oriented programming as a unifying principle for functional, frame and rule-based programming.     

THE ROLE OF EVOLUTIONARY AND ADAPTIVE SEARCH DURING WHOLE SYSTEM,CONSTRAINED AND DETAILED DESIGN OPTIMIZATION

The paper introduces various strategies which incorporate evolutionary and adaptive search techniques. These strategies incorporate genetic algorithms (GA) and ant colony models combined within co-operating frameworks that provide a capability for decision support and optimization during whole system design and constraint satisfaction/ constrained optimization during the engineering design process. The objective during whole system design is to determine an optimum initial configuration for large engineering systems. Strategies for the efficient integration of evolutionary techniques with detailed design are also introduced. Each of these areas presents specific problems to the evolutionary/adaptive search processes and the overall objective here is to identify the main areas of difficulty and provide solutions that will lead to successful integration. The paper illustrates the flexibility and utility of the various techniques when applied across the various stages of the design process, i.e. from providing decision support during the high-risk stages of preliminary design to the identification of definitive optimal solutions during the more deterministic stages of detailed design.     

Stability analysis and controller synthesis for hybrid dynamical systems

Wherever continuous and discrete dynamics interact, hybrid systems arise. This is especially the case in many technological systems in which logic decision-making and embedded control actions are combined with continuous physical processes. Also for many mechanical, biological, electrical and economical systems the use of hybrid models is essential to adequately describe their behaviour. To capture the evolution of these systems, mathematical models are needed that combine in one way or another the dynamics of the continuous parts of the system with the dynamics of the logic and discrete parts. These mathematical models come in all kinds of variations, but basically consist of some form of differential or difference equations on the one hand and automata or other discrete-event models on the other hand. The collection of analysis and synthesis techniques based on these models forms the research area of hybrid systems theory, which plays an important role in the multi-disciplinary design of many technological systems that surround us. This paper presents an overview from the perspective of the control community on modelling, analysis and control design for hybrid dynamical systems and surveys the major research lines in this appealing and lively research area.     

Predator confusion is sufficient to evolve swarming behaviour

Swarming behaviours in animals have been extensively studied owing to their implications for the evolution of cooperation, social cognition and predator–prey dynamics. An important goal of these studies is discerning which evolutionary pressures favour the formation of swarms. One hypothesis is that swarms arise because the presence of multiple moving prey in swarms causes confusion for attacking predators, but it remains unclear how important this selective force is. Using an evolutionary model of a predator–prey system, we show that predator confusion provides a sufficient selection pressure to evolve swarming behaviour in prey. Furthermore, we demonstrate that the evolutionary effect of predator confusion on prey could in turn exert pressure on the structure of the predator''s visual field, favouring the frontally oriented, high-resolution visual systems commonly observed in predators that feed on swarming animals. Finally, we provide evidence that when prey evolve swarming in response to predator confusion, there is a change in the shape of the functional response curve describing the predator''s consumption rate as prey density increases. Thus, we show that a relatively simple perceptual constraint—predator confusion—could have pervasive evolutionary effects on prey behaviour, predator sensory mechanisms and the ecological interactions between predators and prey.     

A composite computational model of liver glucose homeostasis. II. Exploring system behaviour

Using a composite model of the glucose homeostasis system, consisting of seven interconnected submodels, we enumerate the possible behaviours of the model in response to variation of liver insulin sensitivity and dietary glucose variability. The model can reproduce published experimental manipulations of the glucose homeostasis system and clearly illustrates several important properties of glucose homeostasis—boundedness in model parameters of the region of efficient homeostasis, existence of an insulin sensitivity that allows effective homeostatic control and the importance of transient and oscillatory behaviour in characterizing homeostatic failure. Bifurcation analysis shows that the appearance of a stable limit cycle can be identified.     

基于细菌觅食和蚁群算法的工艺路线优化

针对工艺路线规划中满足多重约束的最优方案选择问题,提出一种细菌觅食和蚁群优化（bacteria foraging ant colony optimization,BFACO）算法。首先,将工艺路线规划转化为对加工元顺序的优化问题,构造满足多种工艺准则的加工元拓扑优先顺序图,并构建了在缩短加工周期、提高加工质量和降低加工成本目标下的最低加工资源更换成本的目标函数;其次,设计加工元序列与加工资源两个搜索阶段的蚁群搜索,拓扑优先顺序图可弥补加工元序列搜索阶段信息素匮乏的缺点,而在加工资源搜索阶段引入细菌觅食优化算法的复制与趋向操作,可使加工元在多个可选加工资源的情况下获得加工资源更换成本最低的加工序列;最后,基于细菌觅食与蚁群算法的融合优化,完成多个加工元序列的信息素积累并输出最优解,解决蚁群算法局部收敛且计算速度慢的问题。将BFACO算法应用于实例并与其他优化算法的优化结果进行对比,结果显示BFACO算法在工艺路线优化方面较其他优化算法具有较高的计算效率,验证了BFACO算法的可行性与有效性。研究表明,BFACO算法可有效应用于同时考虑工艺约束与加工资源更换成本的工艺规划,为实际生产提供高效且灵活的工艺路线的优化选择。