Differential evolution (DE) is arguably one of the most powerful stochastic real-parameter optimization algorithms in current use. DE operates through similar computational steps as employed by a standard evolutionary algorithm (EA). However, unlike traditional EAs, the DE-variants perturb the current-generation population members with the scaled differences of randomly selected and distinct population members. Therefore, no separate probability distribution has to be used for generating the offspring. Since its inception in 1995, DE has drawn the attention of many researchers all over the world resulting in a lot of variants of the basic algorithm with improved performance. This paper presents a detailed review of the basic concepts of DE and a survey of its major variants, its application to multiobjective, constrained, large scale, and uncertain optimization problems, and the theoretical studies conducted on DE so far. Also, it provides an overview of the significant engineering applications that have benefited from the powerful nature of DE.

/pdf/differential-evolution-a-survey-of-the-state-of-the-art-3lz840pz2t.pdf

Differential Evolution: A Survey of the State-of-the-Art

Numerical Initial Value Problems in Ordinary Differential Equations

In this review, we summarize the knowledge of the production and properties of charcoal that has been accumulated over the past 38 millenia. The manipulation of pressure, moisture content, and gas flow enables biomass carbonization with fixed-carbon yields that approachor attainthe theoretical limit after reaction times of a few tens of minutes. Much of the heat needed to carbonize the feed is released by vigorous, exothermic secondary reactions that reduce the formation of unwanted tars by augmenting the charcoal yield in a well-designed carbonizer. As a renewable fuel, charcoal has many attractive features:  it contains virtually no sulfur or mercury and is low in nitrogen and ash; it is highly reactive yet easy to store and handle. Carbonized charcoal can be a good adsorbent with a large surface area and a semimetal with an electrical resistivity comparable to that of graphite. Recent advances in knowledge about the production and properties of charcoal presage its expanded use as a renewable fuel, red...

/pdf/the-art-science-and-technology-of-charcoal-production-2yxzbuvb7k.pdf

The Art, Science, and Technology of Charcoal Production†

Differential Evolution (DE) is arguably one of the most powerful and versatile evolutionary optimizers for the continuous parameter spaces in recent times. Almost 5 years have passed since the first comprehensive survey article was published on DE by Das and Suganthan in 2011. Several developments have been reported on various aspects of the algorithm in these 5 years and the research on and with DE have now reached an impressive state. Considering the huge progress of research with DE and its applications in diverse domains of science and technology, we find that it is a high time to provide a critical review of the latest literatures published and also to point out some important future avenues of research. The purpose of this paper is to summarize and organize the information on these current developments on DE. Beginning with a comprehensive foundation of the basic DE family of algorithms, we proceed through the recent proposals on parameter adaptation of DE, DE-based single-objective global optimizers, DE adopted for various optimization scenarios including constrained, large-scale, multi-objective, multi-modal and dynamic optimization, hybridization of DE with other optimizers, and also the multi-faceted literature on applications of DE. The paper also presents a dozen of interesting open problems and future research issues on DE.

Recent advances in differential evolution – An updated survey

A nonlinear model predictive controller has been developed to maintain normoglycemia in subjects with type 1 diabetes during fasting conditions such as during overnight fast. The controller employs a compartment model, which represents the glucoregulatory system and includes submodels representing absorption of subcutaneously administered short-acting insulin Lispro and gut absorption. The controller uses Bayesian parameter estimation to determine time-varying model parameters. Moving target trajectory facilitates slow, controlled normalization of elevated glucose levels and faster normalization of low glucose values. The predictive capabilities of the model have been evaluated using data from 15 clinical experiments in subjects with type 1 diabetes. The experiments employed intravenous glucose sampling (every 15 min) and subcutaneous infusion of insulin Lispro by insulin pump (modified also every 15 min). The model gave glucose predictions with a mean square error proportionally related to the prediction horizon with the value of 0.2 mmol L(-1) per 15 min. The assessment of clinical utility of model-based glucose predictions using Clarke error grid analysis gave 95% of values in zone A and the remaining 5% of values in zone B for glucose predictions up to 60 min (n = 1674). In conclusion, adaptive nonlinear model predictive control is promising for the control of glucose concentration during fasting conditions in subjects with type 1 diabetes.

/pdf/nonlinear-model-predictive-control-of-glucose-concentration-2l5wkss7wi.pdf

Nonlinear model predictive control of glucose concentration in subjects with type 1 diabetes

Enhanced IMC for Glucose Control in Type I Diabetic Patients

Gamma distribution model ― a limiting form of gamma distributed time delay model

It is increasingly recognized that rapid model development or the refining of currently available models for any system (chemical, biological, medical, environmental etc.) is greatly facilitated by experimental design and particularly by Model Based Optimal Experiment Design (MBOED). MBOED approaches are primarily used in the context of precise parameter estimation and model discrimination. However, the presence of parameter correlation often reduces the confidence in creating practical applications based on the identified model. To overcome this problem, a novel design criterion comprising two conflicting objectives: (1) maximizing the information content (which is the usual objective in traditional OED), and (2) minimizing the correlation between estimated parameters, is proposed. The proposed approach is implemented to suggest the optimal sampling times for estimating the model parameters of toxin kinetics model for patients on maintenance hemodialysis, developed recently by Maheshwari et al., 2011. The resulting parameter estimates can be used for prescribing patient specific dialysis sessions. The proposed multi-objective MBOED criterion is also relevant for use in other biomedical, chemical, and environmental systems.

A novel optimal experiment design technique based on multi-objective optimization and its application for toxin kinetics model of hemodialysis patients

The advent of tighter environmental norms to curb pollution and simultaneous increase in crude prices has resulted in the need for production of bulk chemicals from bio-based feedstock. Lactic acid (LA) is a major example of bio-based products and is an important, commodity organic acid used in bio-plastics, pharmaceuticals, food, preservatives, cosmetics, and fertilizers. LA is mainly produced by the microbial fermentation of renewable feedstock. Its separation from dilute fermentation broth and further concentration are energy and cost intensive tasks. Reactive separation involving reactive distillation (RD) and hybrid reactive stripper-membrane (RSM) technology are potential alternatives to recover the desired grades of LA. The present work analyzes the effectiveness of the proposed RSM technology for the hydrolysis of methyl lactate (MLA) to obtain industrial grade LA (88 wt%), and compares its performance with RD. Obtained results are encouraging as MLA conversion increase of 6% and energy savings of nearly 17% can be achieved by RSM compared to RD.

Modeling and Analysis of Intensified Processes for Economic Recovery of High-Grade Lactic Acid

Stochastic search methods can thoroughly explore the search space for the global optimum, but once the search approaches the optimum, they become inefficient to find the optimal solution precisely. Conversely, deterministic local methods are more efficient in finding the optimum precisely, particularly if a good initial estimate is given. In this work, the stochastic and local search methods are combined together for improving the search efficiency without losing reliability for solving multiobjective optimization problems. An improved multiobjective differential evolution (I-MODE) is used for the global search; it includes a termination criterion to decide the switching from the stochastic search to the local search. The normalized normal constraint method is employed to refine the non-dominated solutions obtained by the I-MODE search. The proposed hybrid approach is tested on several unconstrained and constrained biobjective optimization problems. It is then applied to the alkylation process, three-stage fermentation process integrated with extraction, and three-stage fermentation process integrated with pervaporation.

Gade Pandu Rangaiah

Papers

Enhanced IMC for Glucose Control in Type I Diabetic Patients

Gamma distribution model ― a limiting form of gamma distributed time delay model

A novel optimal experiment design technique based on multi-objective optimization and its application for toxin kinetics model of hemodialysis patients

Modeling and Analysis of Intensified Processes for Economic Recovery of High-Grade Lactic Acid

Hybrid Approach for Multiobjective Optimization and Its Application to Process Engineering Problems