Applications of Metaheuristics in Process Engineering
08 Aug 2014-
TL;DR: This book explains cutting-edge research techniques in related computational intelligence domains and their applications in real-world process engineering will be of interest to industrial practitioners and research academics.
Abstract: Metaheuristics exhibit desirable properties like simplicity, easy parallelizability and ready applicability to different types of optimization problems such as real parameter optimization, combinatorial optimization and mixed integer optimization. They are thus beginning to play a key role in different industrially important process engineering applications, among them the synthesis of heat and mass exchange equipment, synthesis of distillation columns and static and dynamic optimization of chemical and bioreactors. This book explains cutting-edge research techniques in related computational intelligence domains and their applications in real-world process engineering. It will be of interest to industrial practitioners and research academics.
Citations
More filters
TL;DR: In this article, the authors present Management Models and Industrial Applications of Linear Programming (MAMLP), a model for industrial applications of linear programming with a focus on management models and industrial applications.
Abstract: (1962). Management Models and Industrial Applications of Linear Programming. Journal of the Operational Research Society: Vol. 13, No. 3, pp. 274-275.
335 citations
01 Jan 2016
TL;DR: The separation process principles is universally compatible with any devices to read and is available in the book collection an online access to it is set as public so you can get it instantly.
Abstract: Thank you very much for reading separation process principles. Maybe you have knowledge that, people have look numerous times for their favorite readings like this separation process principles, but end up in harmful downloads. Rather than enjoying a good book with a cup of coffee in the afternoon, instead they juggled with some harmful bugs inside their computer. separation process principles is available in our book collection an online access to it is set as public so you can get it instantly. Our books collection hosts in multiple countries, allowing you to get the most less latency time to download any of our books like this one. Kindly say, the separation process principles is universally compatible with any devices to read.
125 citations
01 Jan 2016
TL;DR: An introduction to chemoinformatics, which is universally compatible with any devices to read, is available in the book collection an online access to it is set as public so you can get it instantly.
Abstract: Thank you for reading an introduction to chemoinformatics. Maybe you have knowledge that, people have search hundreds times for their chosen readings like this an introduction to chemoinformatics, but end up in malicious downloads. Rather than enjoying a good book with a cup of coffee in the afternoon, instead they cope with some harmful bugs inside their laptop. an introduction to chemoinformatics is available in our book collection an online access to it is set as public so you can get it instantly. Our books collection saves in multiple countries, allowing you to get the most less latency time to download any of our books like this one. Merely said, the an introduction to chemoinformatics is universally compatible with any devices to read.
76 citations
TL;DR: Multi-objective optimization (MOO) provides many Pareto-optimal solutions, quantitative trade-off between objectives, optimal values of decision variables and their trends.
Abstract: Process optimization for two or more objectives is increasing Since objectives are often conflicting, multi-objective optimization (MOO) provides many Pareto-optimal solutions, quantitative trade-off between objectives, optimal values of decision variables and their trends These results give greater insight about the process and are useful for selecting one of the optimal solutions In this paper, MOO is introduced and compared with single objective optimization, and then MOO studies on design of energy efficient processes, published from January 2013 to February 2015, are reviewed MOO applications in 65 papers of interest to chemical engineers, are classified into four groups: energy efficient processes, biofuels, power generation/CO2 capture, and fuel cell/hydrogen production Main features of these studies are summarized in four tables Finally, concluding remarks are given for future studies on MOO applications
58 citations
References
More filters
Book•
01 Sep 1988
TL;DR: In this article, the authors present the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields, including computer programming and mathematics.
Abstract: From the Publisher:
This book brings together - in an informal and tutorial fashion - the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields
Major concepts are illustrated with running examples, and major algorithms are illustrated by Pascal computer programs No prior knowledge of GAs or genetics is assumed, and only a minimum of computer programming and mathematics background is required
52,797 citations
TL;DR: There is a deep and useful connection between statistical mechanics and multivariate or combinatorial optimization (finding the minimum of a given function depending on many parameters), and a detailed analogy with annealing in solids provides a framework for optimization of very large and complex systems.
Abstract: There is a deep and useful connection between statistical mechanics (the behavior of systems with many degrees of freedom in thermal equilibrium at a finite temperature) and multivariate or combinatorial optimization (finding the minimum of a given function depending on many parameters). A detailed analogy with annealing in solids provides a framework for optimization of the properties of very large and complex systems. This connection to statistical mechanics exposes new information and provides an unfamiliar perspective on traditional optimization problems and methods.
41,772 citations
TL;DR: This paper suggests a non-dominated sorting-based MOEA, called NSGA-II (Non-dominated Sorting Genetic Algorithm II), which alleviates all of the above three difficulties, and modify the definition of dominance in order to solve constrained multi-objective problems efficiently.
Abstract: 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.
37,111 citations
TL;DR: In this article, a new heuristic approach for minimizing possibly nonlinear and non-differentiable continuous space functions is presented, which requires few control variables, is robust, easy to use, and lends itself very well to parallel computation.
Abstract: A new heuristic approach for minimizing possibly nonlinear and non-differentiable continuous space functions is presented. By means of an extensive testbed it is demonstrated that the new method converges faster and with more certainty than many other acclaimed global optimization methods. The new method requires few control variables, is robust, easy to use, and lends itself very well to parallel computation.
24,053 citations
04 Oct 1995
TL;DR: The optimization of nonlinear functions using particle swarm methodology is described and implementations of two paradigms are discussed and compared, including a recently developed locally oriented paradigm.
Abstract: The optimization of nonlinear functions using particle swarm methodology is described. Implementations of two paradigms are discussed and compared, including a recently developed locally oriented paradigm. Benchmark testing of both paradigms is described, and applications, including neural network training and robot task learning, are proposed. Relationships between particle swarm optimization and both artificial life and evolutionary computation are reviewed.
14,477 citations
"Applications of Metaheuristics in P..." refers background or methods in this paper
...Particle swarm optimization was developed [4] as a stochastic optimization algorithm based on social simulation models....
[...]
...These include NSGA-II-aJG (adapted JG: the JG size is of a fixed, user-defined, length, another computational parameter [4, 5]), NSGA-II-mJG (modified JG: replaces a chunk of binaries with a new chunk of binaries comprising either all 1s or all 0s [22]), NSGA-II-sJG (specific JG: replaces the chunk of binaries describing a specific decision variable by a randomly generated new chunk of binaries [1, 2]), and saJG (specific-adapted JG: replaces a chunk of binaries of length, lstr, by a randomly generated new chunk of binaries [1, 2])....
[...]
...Alberton [4] proposed a multiobjective approach based on PSO for model-based sequential experimental designs for discrimination of rival models and/or estimation of precise model parameters....
[...]
...Particle swarm optimization (PSO) was developed [4] as a stochastic optimization algorithm based on social simulation models....
[...]
...The usage of supported liquid membranes in separating metal ions of commercial value is well documented [4]....
[...]