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.

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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...

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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.

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

http://www.eng.uwo.ca/people/aray/Ajay%20Publications%20PDF%20files/A27%20Rajesh%20CES%202001.pdf

Multi-objective optimization of industrial hydrogen plants

Knowledge based decision making method for the selection of mixed refrigerant systems for energy efficient LNG processes

Evaluation of genetic algorithms and simulated annealing for phase equilibrium and stability problems

Tabu search for global optimization of continuous functions with application to phase equilibrium calculations

For reasons both financial and environmental, there is a perpetual need to optimize the design and operating conditions of industrial process systems in order to improve their performance, energy efficiency, profitability, safety and reliability. However, with most chemical engineering application problems having many variables with complex inter-relationships, meeting these optimization objectives can be challenging. This is where Multi-Objective Optimization (MOO) is useful to find the optimal trade-offs among two or more conflicting objectives.

Gade Pandu Rangaiah

Papers

Multi-objective optimization of industrial hydrogen plants

Knowledge based decision making method for the selection of mixed refrigerant systems for energy efficient LNG processes

Evaluation of genetic algorithms and simulated annealing for phase equilibrium and stability problems

Tabu search for global optimization of continuous functions with application to phase equilibrium calculations

Multi-objective optimization in chemical engineering : developments and applications