Optimization Of Complex Systems Research Articles

A Sequential Linear Programming Coordination Algorithm for Analytical Target Cascading

Decomposition-based strategies, such as analytical target cascading (ATC), are often employed in design optimization of complex systems. Achieving convergence and computational efficiency in the coordination strategy that solves the partitioned problem is a key challenge. A new convergent strategy is proposed for ATC that coordinates interactions among subproblems using sequential linearizations. The linearity of subproblems is maintained using infinity norms to measure deviations between targets and responses. A subproblem suspension strategy is used to suspend temporarily inclusion of subproblems that do not need significant redesign, based on trust region and target value step size. An individual subproblem trust region method is introduced for faster convergence. The proposed strategy is intended for use in design optimization problems where sequential linearizations are typically effective, such as problems with extensive monotonicities, a large number of constraints relative to variables, and propagation of probabilities with normal distributions. Experiments with test problems show that, relative to standard ATC coordination, the number of subproblem evaluations is reduced considerably while the solution accuracy depends on the degree of monotonicity and nonlinearity.

Optimisation of complex water supply systems with water quality, hydraulic and treatment plant aspects

A model for optimal operation of a complex water supply system for drinking water and with water quality, hydraulic and desalination treatment plants developed by Cohen and others has been applied to a realistic regional network, in which water quality is defined by salinity, magnesium and sulphur. The model considers the hydraulics of the network, including pump stations, boosters and control valves. Solute transport in the model assumes conservative water quality parameters. Changes in water quality within the network are achieved by changing the removal ratio of reverse osmosis in desalination treatment plans and by control of mixing (dilution) at junction nodes. The decision variables in the model are the operation of treatment plants, pumps and control valves. The model minimises the sum of the costs of water at sources, treatment, energy and loss of agricultural yield due to irrigation with low quality water. Three case studies are presented: (1) a network without treatment plants and water salinity as the only water quality parameter; (2) as for (1), but with treatment plants included; and (3), as for (2), but with the addition of magnesium and sulphur quality parameters. The results demonstrate the ability of the model to handle a regional water supply system having water quality problems, with optimal solutions being found in cases where there is a conflict between hydraulic and water quality requirements.

Reflectance and absorbance of all-dielectric metamaterial composites with fractal boundaries: A numerical investigation

Two-dimensional two-phase heterostructures have been the subject of many investigations including computer simulations. An outstanding issue is the shape analysis of the reflectance for dielectric inclusions of arbitrarily complex geometry. We report on finite element numerical calculations to investigate the effects a fractal-shaped object having an intrinsic complex permittivity with a negative real part embedded in a host matrix with real and positive permittivity can have on reflectance and absorbance of a uniform plane wave impinging on it. The simulations were performed at a wavelength much larger than the length scales of the inhomogeneities. The reflectance and absorbance of the structures are shown to be highly sensitive to the details of the fractal metamaterial environment and to its intrinsic loss. We comment on how these results may be used to provide a quantitative framework for the design, selection, and optimization of artificial metamaterial microwave reflectors and other complex systems yet to be explored.

A new grey decision dynamic model based on cybernetics knowledge for complex system optimization analyzing and its practical application

PurposeThe purpose of this paper is to seek an approach to study decision making and optimization analyzing of enterprises with multi‐factors.Design/methodology/approachIn this paper, a new grey decision dynamic model was set up; it integrates with modified GM model, the transfer function and response characteristic of cybernetics, and other knowledge. The building steps of this integrated model and its application method in a certain enterprise were presented.FindingsUntil recently, there have been many references studying grey decision or grey relational analysis of factors, but it was found that dynamic affecting of multi‐factors for enterprise production and their affecting levels were not studied synthetically in these references, and by this new dynamic model, these useful conclusions can be gotten.Research limitations/implicationsThe built time response equation and dynamic model in this paper can be only used for whole regularity analysis and not suited to daily one‐to‐one analyzing; otherwise the error of the reductive values must be tested.Practical implicationsThis new grey decision dynamic model can be used widely in decision making and optimization analyzing of enterprises with multi‐factors. Practical applying results show that the proposed method can instruct effectively actual production.Originality/valueThis paper offers a new grey decision dynamic model that can be used in decision making and optimization analyzing of enterprises with multi‐factors. By applying this new dynamic model in practice, some useful conclusions are drawn; some dynamic factors affecting production capacity of enterprises and their affecting levels can be found.

Method to assess and optimise dependability of complex macro-systems: Application to a railway signalling system

Achieving a high degree of dependability in complex macro-systems is challenging. Because of the large number of components and numerous independent teams involved, an overview of the global system performance is usually lacking to support both design and operation adequately. A functional failure mode, effects and criticality analysis (FMECA) approach is proposed to address the dependability optimisation of large and complex systems. The basic inductive model FMECA has been enriched to include considerations such as operational procedures, alarm systems, environmental and human factors, as well as operation in degraded mode. Its implementation on a commercial software tool allows an active linking between the functional layers of the system and facilitates data processing and retrieval, which enables to contribute actively to the system optimisation. The proposed methodology has been applied to optimise dependability in a railway signalling system. Signalling systems are typical example of large complex systems made of multiple hierarchical layers. The proposed approach appears appropriate to assess the global risk- and availability-level of the system as well as to identify its vulnerabilities. This enriched-FMECA approach enables to overcome some of the limitations and pitfalls previously reported with classical FMECA approaches.

Extension of VIKOR method for decision making problem with interval numbers

The VIKOR method was developed for multi-criteria optimization of complex systems. It determines the compromise ranking list and the compromise solution obtained with the initial (given) weights. This method focuses on ranking and selecting from a set of alternatives in the presence of conflicting criteria. It introduces the multi-criteria ranking index based on the particular measure of “closeness” to the “ideal” solution. The aim of this paper is to extend the VIKOR method for decision making problems with interval number. The extended VIKOR method’s ranking is obtained through comparison of interval numbers and for doing the comparisons between intervals, we introduce α as optimism level of decision maker. Finally, a numerical example illustrates and clarifies the main results developed in this paper.

A cross entropy based algorithm for reliability problems

The Cross Entropy method has recently been applied to combinatorial optimization problems with promising results. This paper proposes a Cross Entropy based algorithm for reliability optimization of complex systems, where one wants to maximize the reliability of a system through optimal allocation of redundant components while respecting a set of budget constraints. We illustrate the effectiveness of the proposed algorithm on two classes of problems, software system reliability optimization and complex network reliability optimization, by testing it on instances from the literature as well as on randomly generated large scale instances. Furthermore, we show how a Cross Entropy-based algorithm can be fine-tuned by using a training scheme based upon the Response Surface Methodology. Computational results show the effectiveness as well as the robustness of the algorithm on different classes of problems.

On the interaction between stratification and control variates, with illustrations in a call centre simulation

Variance reduction techniques (VRTs) are often essential to make simulation quick and accurate enough to be useful. A case in point is simulation-based optimization of complex systems. An obvious idea to push the improvement one step further is to combine several VRTs for a given simulation. But such combinations often give rise to new issues. This paper studies the combination of stratification with control variates. We detail and compare several ways of doing the combination. Nontrivial synergies between the two methods are exhibited. We illustrate this with a telephone call centre simulation, where we combine a control variate with stratification with respect to one of the uniform random variates that drive the simulation. It turns out that using more information in the control variate degrades the performance (significantly) in our example. This seemingly paradoxical behaviour is not rare and our theoretical analysis explains why.

Artificial Intelligence technique for modelling and forecasting of solar radiation data: a review

Artificial Intelligence (AI) has been used and applied in different sectors, such as engineering, economic, medicine, military, marine, etc. AI has also been applied for modelling, identification, optimisation, prediction, forecasting, and control of complex systems. The main objective of this paper is to present an overview of AI techniques for modelling, prediction and forecasting of solar radiation data. Published literature works presented in this paper show the potential of AI as a design tool for prediction and forecasting of solar radiation data; additionally, they present the advantages of using AI-based prediction solar radiation data in isolated areas where there no instrument for the measurement of this data, especially the parameters related to photovoltaic (PV) systems. Solar radiation plays a very important factor in PV-system performance and sizing.

Sizing of photovoltaic systems: a review

Artificial intelligence (AI) techniques are becoming useful as alternate approaches to conventional techniques or as components of integrated systems. They have been used to solve complicated practical problems in various areas and are becoming popular more and more nowadays. AI techniques have the following features: can learn from examples; are fault tolerant in the sense that they are able to handle noise and incomplete data; are able to deal with non-linear problems; and once trained can perform prediction and generalization at high speed. AI-based systems are being developed and deployed worldwide in a myriad of applications, mainly because of their symbolic reasoning, flexibility and explanation capabilities. AI have been used and applied in different sectors, such as engineering, economic, medicine, military, marine, etc. They have also been applied for modelling, identification, optimization, prediction, forecasting, and control of complex systems. The main objective of this paper is to present an overview of the alternative approach and AI techniques for sizing of photovoltaic (PV) systems: stand-alone PV, grid-connected PV system, PV-wind hybrid system, etc). Published literature works presented in this paper show the potential of AI as a design tool in the optimal sizing of PV systems. Additionally the advantage of using an AIbased sizing of PV systems is that it provides good optimisation, especially in isolated areas, where the weather data are not always available.

Stochastic design optimization: Application to reacting flows

One concern in the optimization of realistic complex systems is to ensure that the optimal model response is reliable and robust with respect to the inherent uncertainties associated with the design variables, constraints and the objective function. Traditional optimization techniques together with uncertainty analysis are computationally expensive and time consuming when it comes to identify what drives the response variability. A stochastic optimization framework combining stochastic surrogate model representation and optimization algorithm is proposed. A generalized Polynomial Chaos stochastic representation is used as the surrogate model. This representation is obtained from a high-order stochastic collocation method. The surrogate model can then be put to use within an appropriate optimization algorithm and provides fast approximations at new design points. This approach allows both sensitivity and optimization analysis. The stochastic optimization method is applied to a multi-layer reacting flow device. The geometric configuration is assumed to be uncertain. The structure design is optimized to maximize the energy transfer between the reacting flow and the device moving parts. Gradient descent and simulated annealing optimization techniques are successfully tested on the kinetic energy surrogate model of the device inner parts.

Collaborative optimization of complex systems: a multidisciplinary approach

Engineering design of complex systems is a decision making process that aims at choosing from among a set of options that implies an irrevocable allocation of resources. It is inherently a multidisciplinary and multi- objective process; nowadays, the designer has to face the continuous growing complexity of engineering problems, but also, the increasing economic competition that have led to a specialization and distribution of knowledge, expertise, tools and work sites. Products become more and more complex and their design is usually of large scale, characterized by an important number of design variables, parameters, requirements, constraints and objectives. Consequently, multi- objective optimization (MOO) and multidisciplinary design optimization (MDO) are more and more used to provide one optimal solution—by the use of a “a priori” or interactive preferences modelling—or a set of optimal solutions—in which the designer will have to choose “a posteriori” the one to be developed. The objective of this paper is to present an original and efficient Collaborative Optimization Strategy for Multi-Objective Systems (COSMOS) designed to find the sub-set of the design space that contains the best solutions – Pareto frontier – of the global system.

Multi-objective approach for robust design optimization problems

This article demonstrates the main capabilities of IOSO (Indirect Optimization based on Self-organization) technology algorithms, tools, and software, which can be used for the optimization of complex systems and objects. IOSO algorithms have higher efficiency, provide a wider range of capabilities, and are practically insensitive with respect to the types of objective function and constraints. They could be smooth, non-differentiable, and stochastic, with multiple optima, with the portions of the design space where objective function and constraints could not be evaluated at all, with the objective function and constraints dependent on mixed variables, etc. The capabilities of IOSO software are demonstrated using examples of solving complex multi-objective (up to 8 simultaneous objectives) problems, which are solved in deterministic and robust design optimization statements. The results of this study show the Pareto set probability statement, which decreases technical risks when developing modern objects and systems with the highest level of efficiency.

On Selecting Single-Level Formulations for Complex System Design Optimization

Design of complex products with several interacting subsystems or disciplinary analyses poses substantive challenges to both analysis and optimization, necessitating specialized solution techniques. A product or system may qualify as complex due to large scale or due to strong interactions. Single-level strategies for complex system optimization centralize decision-making authority, while multilevel strategies distribute the decision-making process. This article studies important differences between two popular single-level formulations: multidisciplinary feasible (MDF) and individual disciplinary feasible (IDF). Results presented aim at aiding practitioners in selecting between formulations. Specifically, while IDF incurs some computational overhead, it may find optima hidden to MDF and is more efficient computationally for strongly coupled problems; further, MDF is sensitive to variations in coupling strength, while IDF is not. Conditions that lead to failure of MDF are described. Two new reproducible design examples are introduced to illustrate these findings and to provide test problems for other investigations.

PROBLEMS IN COMPLEX ENGINEERING SYSTEM OPTIMIZATION DESIGN AND ALTERNATIVE SOLUTION

Problems of low efficiency, low accuracy and complex information exchange in the application of conventional optimization approach to complex engineering system optimization design are pointed out. Reasons for the presence of these problems are explained in detail by analyzing the calculation process of complex system optimization. The response surface method is introduced to solve these problems, based on which an iterative method of series response surface is presented. In this method, complex system analysis is replaced by response surface model analysis and a database of the complex system is constructed. At each iteration, the database is expanded through new added data and then the response surface model is refined according to the expanded database, which makes the response surface model more and more accurate until convergence is achieved. Moreover, to decrease the amount of computation while retaining high accuracy, this method provides a systematic way of choosing the new added data. In the last section, a large-scale transporter wing design problem is solved successfully by this series response surface method, which shows the validity of the method.

A functional approach to geometry optimization of complex systems

A quadratically convergent procedure is presented for the geometry optimization of complex systems, such as biomolecules and molecular complexes. The costly evaluation of the exact Hessian is avoided by expanding the density functional to second order in both nuclear and electronic variables, and then searching for the minimum of the quadratic functional. The dependence of the functional on the choice of nuclear coordinate system is described, and illustrative geometry optimizations using Cartesian and internal coordinates are presented for Taxol™.

Heuristic Design of Cancer Chemotherapies

A methodology using heuristic search methods is proposed for optimizing cancer chemotherapies with drugs acting on a specific phase of the cell cycle. Specifically, two evolutionary algorithms, and a simulated annealing method are considered. The methodology relies on an underlying mathematical model for tumor growth that includes cycle phase specificity, and multiple applications of a single cytotoxic agent. The goal is to determine effective protocols for administering the agent, so that the tumor is eradicated, while the immune system remains above a given threshold. Results confirm that modern heuristic methods are a good choice for optimizing complex systems. The three algorithms considered produced effective solutions, and provided drug schedules suitable for practice, although some methods excelled others in performance. A discussion of comparative results is presented.

Queueing networks and the topological design of supply chain systems

Manufacturing firms are linking their internal processes to external suppliers and customers. The resulting supply chain is often a very large network of activities and resources. Modeling and optimization of such complex systems is a new area of research for which exact analytical results are obviously very difficult if not impossible. We develop a methodology based on analytical queueing networks coupled with nonlinear optimization to design supply chain topologies and evaluate various performance measures. The results obtained from small network configurations as well as from the case study discussed in the paper demonstrate that our approach is a very useful tool to analyze congestion problems and to evaluate the performance of the network topologies.

Parametric optimization of complex systems using a multi-domain FRF-based substructuring method

A general procedure for a parametric sensitivity analysis of a complex system has been presented using a multi-domain FRF-based substructuring formulation. The direct differentiation of reaction forces on the interface boundaries with respect to the design vector is used to obtain a design sensitivity formula of responses in any substructure. A simple example for a ladder structure is introduced to verify the formulation numerically. The proposed method is applied to an optimization problem of an engine mount system to minimize the interior sound over the concerned rpm range. The optimized interior noise in a passenger car shows that the proposed design sensitivity formulation is efficient and accurate.

Co-evolution of Hetero Multiagent Systems using Genetic Network Programming.

Recently, many methods of evolutionary computation such as Genetic Algorithm(GA) and Genetic Programming(GP) have been developed as a basic tool for modeling and optimizing complex systems. GA has the genome of string structure, while the genome in GP is of tree structure. In this paper, a new evolutionary method named Genetic Network Programming(GNP), whose genome has network structure is applied to multiagent sysytems. Hetero Multiagent Sysytems with GNP are studied, where each agent of the same group has its own GNP program in order to build the adaptive agents against changing environments. Specifically, the comparison between Hetero Multiagent Systems and conventional Homo Multiagent Sysytems is carried out in simulations on ants behaviors.