Indian Institute of Technology Kanpur

Multi-objective evolutionary algorithms (MOEAs) that use non-dominated sorting and sharing have been criticized mainly for: (1) their O(MN/sup 3/) computational complexity (where M is the number of objectives and N is the population size); (2) their non-elitism approach; and (3) the need to specify a sharing parameter. In this paper, we suggest a non-dominated sorting-based MOEA, called NSGA-II (Non-dominated Sorting Genetic Algorithm II), which alleviates all of the above three difficulties. Specifically, a fast non-dominated sorting approach with O(MN/sup 2/) computational complexity is presented. Also, a selection operator is presented that creates a mating pool by combining the parent and offspring populations and selecting the best N solutions (with respect to fitness and spread). Simulation results on difficult test problems show that NSGA-II is able, for most problems, to find a much better spread of solutions and better convergence near the true Pareto-optimal front compared to the Pareto-archived evolution strategy and the strength-Pareto evolutionary algorithm - two other elitist MOEAs that pay special attention to creating a diverse Pareto-optimal front. Moreover, we modify the definition of dominance in order to solve constrained multi-objective problems efficiently. Simulation results of the constrained NSGA-II on a number of test problems, including a five-objective, seven-constraint nonlinear problem, are compared with another constrained multi-objective optimizer, and the much better performance of NSGA-II is observed.

A comprehensive introduction to network flows that brings together the classic and the contemporary aspects of the field, and provides an integrative view of theory, algorithms, and applications. presents in-depth, self-contained treatments of shortest path, maximum flow, and minimum cost flow problems, including descriptions of polynomial-time algorithms for these core models. emphasizes powerful algorithmic strategies and analysis tools such as data scaling, geometric improvement arguments, and potential function arguments. provides an easy-to-understand descriptions of several important data structures, including d-heaps, Fibonacci heaps, and dynamic trees. devotes a special chapter to conducting empirical testing of algorithms. features over 150 applications of network flows to a variety of engineering, management, and scientific domains. contains extensive reference notes and illustrations.

In trying to solve multiobjective optimization problems, many traditional methods scalarize the objective vector into a single objective. In those cases, the obtained solution is highly sensitive to the weight vector used in the scalarization process and demands that the user have knowledge about the underlying problem. Moreover, in solving multiobjective problems, designers may be interested in a set of Pareto-optimal points, instead of a single point. Since genetic algorithms (GAs) work with a population of points, it seems natural to use GAs in multiobjective optimization problems to capture a number of solutions simultaneously. Although a vector evaluated GA (VEGA) has been implemented by Schaffer and has been tried to solve a number of multiobjective problems, the algorithm seems to have bias toward some regions. In this paper, we investigate Goldberg's notion of nondominated sorting in GAs along with a niche and speciation method to find multiple Pareto-optimal points simultaneously. The proof-of-principle results obtained on three problems used by Schaffer and others suggest that the proposed method can be extended to higher dimensional and more difficult multiobjective problems. A number of suggestions for extension and application of the algorithm are also discussed.

In this paper, we provide a systematic comparison of various evolutionary approaches to multiobjective optimization using six carefully chosen test functions. Each test function involves a particular feature that is known to cause difficulty in the evolutionary optimization process, mainly in converging to the Pareto-optimal front (e.g., multimodality and deception). By investigating these different problem features separately, it is possible to predict the kind of problems to which a certain technique is or is not well suited. However, in contrast to what was suspected beforehand, the experimental results indicate a hierarchy of the algorithms under consideration. Furthermore, the emerging effects are evidence that the suggested test functions provide sufficient complexity to compare multiobjective optimizers. Finally, elitism is shown to be an important factor for improving evolutionary multiobjective search.

Effective stiffness properties (D) of nanosized structural elements such as plates and beams differ from those predicted by standard continuum mechanics (Dc). These differences (D-Dc)/Dc depend on the size of the structural element. A simple model is constructed to predict this size dependence of the effective properties. The important length scale in the problem is identified to be the ratio of the surface elastic modulus to the elastic modulus of the bulk. In general, the non-dimensional difference in the elastic properties from continuum predictions (D-Dc)/Dc is found to scale as αS/Eh, where α is a constant which depends on the geometry of the structural element considered, S is a surface elastic constant, E is a bulk elastic modulus and h a length defining the size of the structural element. Thus, the quantity S/E is identified as a material length scale for elasticity of nanosized structures. The model is compared with direct atomistic simulations of nanoscale structures using the embedded atom method for FCC Al and the Stillinger-Weber model of Si. Excellent agreement between the simulations and the model is found.

Dielectric polymer nanocomposites are rapidly emerging as novel materials for a number of advanced engineering applications. In this Review, we present a comprehensive review of the use of ferroelectric polymers, especially PVDF and PVDF-based copolymers/blends as potential components in dielectric nanocomposite materials for high energy density capacitor applications. Various parameters like dielectric constant, dielectric loss, breakdown strength, energy density, and flexibility of the polymer nanocomposites have been thoroughly investigated. Fillers with different shapes have been found to cause significant variation in the physical and electrical properties. Generally, one-dimensional and two-dimensional nanofillers with large aspect ratios provide enhanced flexibility versus zero-dimensional fillers. Surface modification of nanomaterials as well as polymers adds flavor to the dielectric properties of the resulting nanocomposites. Nowadays, three-phase nanocomposites with either combination of fillers or polymer matrix help in further improving the dielectric properties as compared to two-phase nanocomposites. Recent research has been focused on altering the dielectric properties of different materials while also maintaining their superior flexibility. Flexible polymer nanocomposites are the best candidates for application in various fields. However, certain challenges still present, which can be solved only by extensive research in this field.

After adequately demonstrating the ability to solve different two-objective optimization problems, multi-objective evolutionary algorithms (MOEAs) must show their efficacy in handling problems having more than two objectives. In this paper, we suggest three different approaches for systematically designing test problems for this purpose. The simplicity of construction, scalability to any number of decision variables and objectives, knowledge of exact shape and location of the resulting Pareto-optimal front, and ability to control difficulties in both converging to the true Pareto-optimal front and maintaining a widely distributed set of solutions are the main features of the suggested test problems. Because of these features, they should be useful in various research activities on MOEAs, such as testing the performance of a new MOEA, comparing different MOEAs, and having a better understanding of the working principles of MOEAs.

Over the past few years, the research on evolutionary algorithms has demonstrated their niche in solving multiobjective optimization problems, where the goal is to find a number of Pareto-optimal solutions in a single simulation run. Many studies have depicted different ways evolutionary algorithms can progress towards the Pareto-optimal set with a widely spread distribution of solutions. However, none of the multiobjective evolutionary algorithms (MOEAs) has a proof of convergence to the true Pareto-optimal solutions with a wide diversity among the solutions. In this paper, we discuss why a number of earlier MOEAs do not have such properties. Based on the concept of epsilon-dominance, new archiving strategies are proposed that overcome this fundamental problem and provably lead to MOEAs that have both the desired convergence and distribution properties. A number of modifications to the baseline algorithm are also suggested. The concept of epsilon-dominance introduced in this paper is practical and should make the proposed algorithms useful to researchers and practitioners alike.

In this paper, we study the problem features that may cause a multi-objective genetic algorithm (GA) difficulty in converging to the true Pareto-optimal front. Identification of such features helps us develop difficult test problems for multi-objective optimization. Multi-objective test problems are constructed from single-objective optimization problems, thereby allowing known difficult features of single-objective problems (such as multi-modality, isolation, or deception) to be directly transferred to the corresponding multi-objective problem. In addition, test problems having features specific to multi-objective optimization are also constructed. More importantly, these difficult test problems will enable researchers to test their algorithms for specific aspects of multi-objective optimization.

The recent progress of oil/water separation technologies using various materials that possess surface superwetting properties is summarized.

tutorial Evolutionary multi-criterion optimization Share on Author: Kalyanmoy Deb Indian Institute of Technology Kanpur, Kanpur, India Indian Institute of Technology Kanpur, Kanpur, IndiaView Profile Authors Info & Claims GECCO '10: Proceedings of the 12th annual conference companion on Genetic and evolutionary computationJuly 2010 Pages 2577–2602https://doi.org/10.1145/1830761.1830909Online:07 July 2010Publication History 1citation389DownloadsMetricsTotal Citations1Total Downloads389Last 12 Months24Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

The effects of couple stresses in fluids are considered. Linearized constitutive equations are proposed for force and couple stresses. A series of boundary-value problems are solved to indicate the effects of couple stresses as well as for experiments measuring the various material constants. It is found that a size effect comes in which is not present in the nonpolar case (couple stresses absent).

Algorithm fairness has started to attract the attention of researchers in AI, Software Engineering and Law communities, with more than twenty different notions of fairness proposed in the last few years. Yet, there is no clear agreement on which definition to apply in each situation. Moreover, the detailed differences between multiple definitions are difficult to grasp. To address this issue, this paper collects the most prominent definitions of fairness for the algorithmic classification problem, explains the rationale behind these definitions, and demonstrates each of them on a single unifying case-study. Our analysis intuitively explains why the same case can be considered fair according to some definitions and unfair according to others.

A first-principles order-parameter theory of the fluid-solid transition is presented in this paper. The thermodynamic potential $\ensuremath{\Omega}$ of the system is computed as a function of order parameters ${\ensuremath{\lambda}}_{i}(={\ensuremath{\lambda}}_{{\stackrel{\ensuremath{\rightarrow}}{\mathrm{k}}}_{i}})$ proportional to the lattice periodic components of the one-particle density $\ensuremath{\rho}(\stackrel{\ensuremath{\rightarrow}}{\mathrm{r}})$, ${\stackrel{\ensuremath{\rightarrow}}{\mathrm{K}}}_{i}$'s being the reciprocal-lattice vectors (RLV) of the crystal. Computation of $\ensuremath{\Omega}({{\ensuremath{\lambda}}_{i}})$ is shown to require knowing $\ensuremath{\Omega}$ for a fluid placed in lattice periodic potentials with amplitudes depending on ${\ensuremath{\lambda}}_{i}$. Using systematic nonperturbative functional methods for calculating the response of the fluid to such potentials, we find $\ensuremath{\Omega}({{\ensuremath{\lambda}}_{i}})$. The fluid properties (response functions) determining it are the Fourier coefficients ${c}_{i}(={c}_{{\stackrel{\ensuremath{\rightarrow}}{\mathrm{K}}}_{i}})$ and ${c}_{0}(={c}_{\stackrel{\ensuremath{\rightarrow}}{\mathrm{q}}=0})$ of the direct correlation function $c(\stackrel{\ensuremath{\rightarrow}}{\mathrm{r}})$. The system freezes when at constant chemical potential $\ensuremath{\mu}$ and pressure $P$, locally stable fluid and solid phases [i.e., minima of $\ensuremath{\Omega}({{\ensuremath{\lambda}}_{i}})$ with ${{\ensuremath{\lambda}}_{i}}=0$ and ${{\ensuremath{\lambda}}_{i}}\ensuremath{\ne}0$, respectively] have the same $\ensuremath{\Omega}$. The order-parameter mode most effective in reducing $\ensuremath{\Omega}({{\ensuremath{\lambda}}_{i}})$ corresponds to ${\stackrel{\ensuremath{\rightarrow}}{\mathrm{K}}}_{j}$ being of the smallest-length RLV set (${\mathrm{c}}_{\stackrel{\ensuremath{\rightarrow}}{\mathrm{q}}}$ is largest for $|\stackrel{\ensuremath{\rightarrow}}{\mathrm{q}}|\ensuremath{\simeq}|{\stackrel{\ensuremath{\rightarrow}}{\mathrm{K}}}_{j}|$). In some cases one has to consider a second order parameter ${\ensuremath{\lambda}}_{n}$ with a RLV ${\stackrel{\ensuremath{\rightarrow}}{\mathrm{K}}}_{n}$ lying near the second peak in ${c}_{\stackrel{\ensuremath{\rightarrow}}{\mathrm{q}}}$. The effect of further order-parameter modes on $\ensuremath{\Omega}$ is shown to be small. The theory can be viewed as one of a strongly first-order density-wave phase transition in a dense classical system. The transition is a purely structural one, occurring when the fluid-phase structural correlations (measured by ${c}_{j}$, etc.) are strong enough. This fact has been brought out clearly by computer experiments but had not been theoretically understood so far. Calculations are presented for freezing into some simple crystal structures, i.e., fcc, bcc, and two-dimensional hcp. The input information is only the crystal structure and the fluid compressibility (related to ${c}_{0}$). We obtain as output the freezing criterion stated as a condition on ${c}_{j}$ or as a relation between ${c}_{j}$ and ${c}_{n}$, the volume change $V$, the entropy change $\ensuremath{\Delta}s$, and the Debye-Waller factor at freezing for various RLV values. The numbers are all in very good agreement with those available experimentally.

Developing scaffolds that mimic the architecture of tissue at the nanoscale is one of the major challenges in the field of tissue engineering. The development of nanofibers has greatly enhanced the scope for fabricating scaffolds that can potentially meet this challenge. Currently, there are three techniques available for the synthesis of nanofibers: electrospinning, self-assembly, and phase separation. Of these techniques, electrospinning is the most widely studied technique and has also demonstrated the most promising results in terms of tissue engineering applications. The availability of a wide range of natural and synthetic biomaterials has broadened the scope for development of nanofibrous scaffolds, especially using the electrospinning technique. The three dimensional synthetic biodegradable scaffolds designed using nanofibers serve as an excellent framework for cell adhesion, proliferation, and differentiation. Therefore, nanofibers, irrespective of their method of synthesis, have been used as scaffolds for musculoskeletal tissue engineering (including bone, cartilage, ligament, and skeletal muscle), skin tissue engineering, vascular tissue engineering, neural tissue engineering, and as carriers for the controlled delivery of drugs, proteins, and DNA. This review summarizes the currently available techniques for nanofiber synthesis and discusses the use of nanofibers in tissue engineering and drug delivery applications.

We present an unconditional deterministic polynomial-time algorithm that determines whether an input number is prime or composite.

In response to the 2013 Update of the European Strategy for Particle Physics, the Future Circular Collider (FCC) study was launched, as an international collaboration hosted by CERN. This study covers a highest-luminosity high-energy lepton collider (FCC-ee) and an energy-frontier hadron collider (FCC-hh), which could, successively, be installed in the same 100 km tunnel. The scientific capabilities of the integrated FCC programme would serve the worldwide community throughout the 21st century. The FCC study also investigates an LHC energy upgrade, using FCC-hh technology. This document constitutes the second volume of the FCC Conceptual Design Report, devoted to the electron-positron collider FCC-ee. After summarizing the physics discovery opportunities, it presents the accelerator design, performance reach, a staged operation scenario, the underlying technologies, civil engineering, technical infrastructure, and an implementation plan. FCC-ee can be built with today's technology. Most of the FCC-ee infrastructure could be reused for FCC-hh. Combining concepts from past and present lepton colliders and adding a few novel elements, the FCC-ee design promises outstandingly high luminosity. This will make the FCC-ee a unique precision instrument to study the heaviest known particles (Z, W and H bosons and the top quark), offering great direct and indirect sensitivity to new physics.

In the past few years, new developments in structured electromagnetic materials have given rise to negative refractive index materials which have both negative dielectric permittivity and negative magnetic permeability in some frequency ranges. The idea of a negative refractive index opens up new conceptual frontiers in photonics. One much-debated example is the concept of a perfect lens that enables imaging with sub-wavelength image resolution. Here we review the fundamental concepts and ideas of negative refractive index materials.

ADVERTISEMENT RETURN TO ISSUEPREVArticleNEXTPalladium-Catalyzed Reactions of Allenes†Reinhold Zimmer, Chimmanamada U. Dinesh, Erathodiyil Nandanan, and Faiz Ahmed KhanView Author Information Institut für Organische Chemie, Technische Universität Dresden, Mommsenstrasse 13, D-01062 Dresden, Germany, and Department of Chemistry, Indian Institute of Technology, Kanpur 208016, India Cite this: Chem. Rev. 2000, 100, 8, 3067–3126Publication Date (Web):July 21, 2000Publication History Received22 November 1999Published online21 July 2000Published inissue 1 August 2000https://pubs.acs.org/doi/10.1021/cr9902796https://doi.org/10.1021/cr9902796research-articleACS PublicationsCopyright © 2000 American Chemical SocietyRequest reuse permissionsArticle Views9580Altmetric-Citations799LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InRedditEmail Other access optionsGet e-Alertsclose SUBJECTS:Allenes,Catalysts,Cyclization,Palladium,Substitution reactions Get e-Alerts

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