The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Shengxiang Yang - One of the best experts on this subject based on the ideXlab platform.
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a survey of swarm intelligence for Dynamic Optimization algorithms and applications
Swarm and evolutionary computation, 2017Co-Authors: Michalis Mavrovouniotis, Shengxiang YangAbstract:Swarm intelligence (SI) algorithms, including ant colony Optimization, particle swarm Optimization, bee-inspired algorithms, bacterial foraging Optimization, firefly algorithms, fish swarm Optimization and many more, have been proven to be good methods to address difficult Optimization problems under stationary environments. Most SI algorithms have been developed to address stationary Optimization problems and hence, they can converge on the (near-) optimum solution efficiently. However, many real-world problems have a Dynamic environment that changes over time. For such Dynamic Optimization problems (DOPs), it is difficult for a conventional SI algorithm to track the changing optimum once the algorithm has converged on a solution. In the last two decades, there has been a growing interest of addressing DOPs using SI algorithms due to their adaptation capabilities. This paper presents a broad review on SI Dynamic Optimization (SIDO) focused on several classes of problems, such as discrete, continuous, constrained, multi-objective and classification problems, and real-world applications. In addition, this paper focuses on the enhancement strategies integrated in SI algorithms to address Dynamic changes, the performance measurements and benchmark generators used in SIDO. Finally, some considerations about future directions in the subject are given.
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Benchmark Generator for the IEEE WCCI-2014 Competition on Evolutionary Computation for Dynamic Optimization Problems Dynamic Travelling Salesman Problem Benchmark Generator
2013Co-Authors: Michalis Mavrovouniotis, Shengxiang Yang, Xin YaoAbstract:The field of Dynamic Optimization is related to the applications of nature-inspired al-gorithms [1]. The area is rapidly growing on strategies to enhance the performance of algorithms, but still there is limited theoretical work, due to the complexity of nature-inspired algorithms and the difficulty to analyze them in the Dynamic domain. Therefore, the development of BGs to evaluate the algorithms in Dynamic Optimization problems (DOPs) is appreciated by the evolutionary computation community. Such tools are not only useful to evaluate algorithms but also essential for the development of new algorithms. The exclusive-or (XOR) DOP generator [5] is the only general benchmark for the combinatorial space that constructs a Dynamic environment from any static binary-encoded function f(x(t)), where x(t) ∈ {0, 1}n, by a bitwise XOR operator. XOR DOP shifts the population of individuals into a different location in the fitness landscape. Hence, the global optimum is known during the environmental changes. In the case of permutation-encoded problems, e.g., the travelling salesman problem (TSP) where x(t) is a set of numbers that represent a position in a sequence, the BGs used change th
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evolutionary Dynamic Optimization a survey of the state of the art
Swarm and evolutionary computation, 2012Co-Authors: Trung Thanh Nguyen, Shengxiang Yang, Juergen BrankeAbstract:Optimization in Dynamic environments is a challenging but important task since many real-world Optimization problems are changing over time. Evolutionary computation and swarm intelligence are good tools to address Optimization problems in Dynamic environments due to their inspiration from natural self-organized systems and biological evolution, which have always been subject to changing environments. Evolutionary Optimization in Dynamic environments, or evolutionary Dynamic Optimization (EDO), has attracted a lot of research effort during the last 20 years, and has become one of the most active research areas in the field of evolutionary computation. In this paper we carry out an in-depth survey of the state-of-the-art of academic research in the field of EDO and other meta-heuristics in four areas: benchmark problems/generators, performance measures, algorithmic approaches, and theoretical studies. The purpose is to for the first time (i) provide detailed explanations of how current approaches work; (ii) review the strengths and weaknesses of each approach; (iii) discuss the current assumptions and coverage of existing EDO research; and (iv) identify current gaps, challenges and opportunities in EDO.
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evolution strategies with q gaussian mutation for Dynamic Optimization problems
Brazilian Symposium on Neural Networks, 2010Co-Authors: Renato Tinos, Shengxiang YangAbstract:Evolution strategies with q-Gaussian mutation, which allows the self-adaptation of the mutation distribution shape, is proposed for Dynamic Optimization problems in this paper. In the proposed method, a real parameter q, which allows to smoothly control the shape of the mutation distribution, is encoded in the chromosome of the individuals and is allowed to evolve. In the experimental study, the q-Gaussian mutation is compared to Gaussian and Cauchy mutation on four experiments generated from the simulation of evolutionary robots.
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A clustering particle swarm optimizer for Dynamic Optimization
2009 IEEE Congress on Evolutionary Computation, 2009Co-Authors: Changhe Li, Shengxiang YangAbstract:In the real world, many applications are nonstationary Optimization problems. This requires that Optimization algorithms need to not only find the global optimal solution but also track the trajectory of the changing global best solution in a Dynamic environment. To achieve this, this paper proposes a clustering particle swarm optimizer (CPSO) for Dynamic Optimization problems. The algorithm employs hierarchical clustering method to track multiple peaks based on a nearest neighbor search strategy. A fast local search method is also proposed to find the near optimal solutions in a local promising region in the search space. Six test problems generated from a generalized Dynamic benchmark generator (GDBG) are used to test the performance of the proposed algorithm. The numerical experimental results show the efficiency of the proposed algorithm for locating and tracking multiple optima in Dynamic environments.
Antonio Florestlacuahuac - One of the best experts on this subject based on the ideXlab platform.
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Dynamic Optimization of a cryogenic air separation unit using a derivative free Optimization approach
Computers & Chemical Engineering, 2018Co-Authors: Israel Negrellosortiz, Antonio Florestlacuahuac, Miguel Angel GutierrezlimonAbstract:Abstract Optimal Dynamic product transition is a challenging and important issue in manufacturing plants. When a reliable Dynamic model is available, gradient-based Optimization algorithms can be used to achieve this aim. However, in some cases a first principles Dynamic model may not be available. In this work, we will assume that only input-output information from a Dynamic model embedded in a Dynamic process simulator is available for optimal product transitions between products. We present a derivative-free Optimization trust region approach to deal with the product Dynamic Optimization problem of an air separation unit used in several processing plants to obtain pure oxygen. High-purity oxygen is required in intensive energy applications such as steel plants and in combustion processes. A closed-loop model predictive control strategy is used where the system to be optimized is embedded in the ASPEN Dynamics simulation environment. The results demonstrate that black-box Dynamic models can be Dynamically optimized when model of the Dynamic model and/or its gradient information are not available. We have successfully applied the non-linear model predictive control and derivate-free approach to several oxygen composition and productivity transition issues.
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Dynamic Optimization of a cryogenic air separation unit using a derivative free Optimization approach
Computers & Chemical Engineering, 2018Co-Authors: Israel Negrellosortiz, Antonio Florestlacuahuac, Miguel Angel GutierrezlimonAbstract:Abstract Optimal Dynamic product transition is a challenging and important issue in manufacturing plants. When a reliable Dynamic model is available, gradient-based Optimization algorithms can be used to achieve this aim. However, in some cases a first principles Dynamic model may not be available. In this work, we will assume that only input-output information from a Dynamic model embedded in a Dynamic process simulator is available for optimal product transitions between products. We present a derivative-free Optimization trust region approach to deal with the product Dynamic Optimization problem of an air separation unit used in several processing plants to obtain pure oxygen. High-purity oxygen is required in intensive energy applications such as steel plants and in combustion processes. A closed-loop model predictive control strategy is used where the system to be optimized is embedded in the ASPEN Dynamics simulation environment. The results demonstrate that black-box Dynamic models can be Dynamically optimized when model of the Dynamic model and/or its gradient information are not available. We have successfully applied the non-linear model predictive control and derivate-free approach to several oxygen composition and productivity transition issues.
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Dynamic Optimization of hips open loop unstable polymerization reactors
Industrial & Engineering Chemistry Research, 2005Co-Authors: Antonio Florestlacuahuac, Lorenz T Biegler, Enrique SaldivarguerraAbstract:We consider Dynamic Optimization strategies for grade transitions for high-impact polystyrene reactors. Because our desired operating conditions are at unstable points, we apply a simultaneous Dynamic Optimization (SDO) approach, where state and control variables in the optimal control problem are discretized and a large-scale nonlinear programming solver is applied. For this purpose, we consider Radau collocation on finite elements and the IPOPT NLP solver. In addition, we describe the stability of the SDO strategy through the presentation of dichotomy properties for boundary-value problems. The resulting SDO approach is then demonstrated on a wide variety of operating scenarios for the high-impact polystyrene (HIPS) reactor, with highly reliable and efficient performance results.
Wolfgang Marquardt - One of the best experts on this subject based on the ideXlab platform.
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integrated scheduling and Dynamic Optimization of grade transitions for a continuous polymerization reactor
Computers & Chemical Engineering, 2008Co-Authors: Adrian Prata, Jan Oldenburg, Andreas Kroll, Wolfgang MarquardtAbstract:Abstract This paper presents a modeling and numerical solution method for an integrated grade transition and production scheduling problem for a continuous polymerization reactor. The optimal sequence of production stages and the transitions between them is supposed to be determined for producing a given number of polymer grades at certain amounts and quality specifications in the most economical way. The production schedule has to satisfy due dates for specific orders. This operational problem is cast into a mixed-integer Dynamic Optimization problem. Disjunctions and logical constraints are combined with a validated differential–algebraic model describing the polymer process during the production of a specific grade as well as along a transition between two different grades. The modeling and solution approach proposed by Oldenburg et al. [Oldenburg, J., Marquardt, W., Heinz D., & Leineweber, D. B. (2003)] is tailored to this problem class to provide an efficient solution technique. An industrial example process serves as an example to illustrate the modeling and solution techniques suggested.
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Dynamic predictive scheduling of operational strategies for continuous processes using mixed logic Dynamic Optimization
Computers & Chemical Engineering, 2007Co-Authors: Jan Busch, Jan Oldenburg, Marcella Santos, Andreas Cruse, Wolfgang MarquardtAbstract:Industrial processes are usually operated in a highly Dynamic environment, e.g. with time-varying market prizes, customer demand, technological development or up- and downstream processes. Due to these disturbances, the operational strategies comprising objectives and constraints are regularly adjusted to reflect a change in the environment in order to achieve or maintain optimal process performance. The related operational objectives need not only be of an economical nature, but can also include flexibility, risk or ecological objectives. In this paper, a novel methodology is presented for the modeling and Dynamic predictive scheduling of operational strategies for continuous processes. Optimal control actions are computed on a moving horizon employing discrete-continuous modeling and mixed-logic Dynamic Optimization as introduced by Oldenburg et al. (2003). The approach is successfully demonstrated considering the operation of a wastewater treatment plant.
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detection and exploitation of the control switching structure in the solution of Dynamic Optimization problems
Journal of Process Control, 2006Co-Authors: Martin Schlegel, Wolfgang MarquardtAbstract:Abstract In this paper we present a novel method for the numerical solution of Dynamic Optimization problems. After obtaining a first solution at a coarse resolution of the control profiles with a direct sequential approach, the structure of the control profiles is analyzed for possible switching times and arcs. Subsequently, the problem is reformulated automatically and solved as a multi-stage problem, with each stage corresponding to a potential arc. Order and resolution of the control parameterization are adapted to the type of the particular arc. By means of some case studies we show that accurate solutions with only few degrees of freedom can be obtained.
Kai Sundmacher - One of the best experts on this subject based on the ideXlab platform.
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toward fast Dynamic Optimization an indirect algorithm that uses parsimonious input parameterization
Industrial & Engineering Chemistry Research, 2018Co-Authors: Erdal Aydin, Dominique Bonvin, Kai SundmacherAbstract:Dynamic Optimization plays an important role toward improving the operation of chemical systems, such as batch and semibatch processes. The preferred strategy to solve constrained nonlinear Dynamic Optimization problems is to use a so-called direct approach. Nevertheless, based on the problem at hand and the solution algorithm used, direct approaches may lead to large computational times. Indirect approaches based on Pontryagin’s Minimum Principle (PMP) represent an efficient alternative for the Optimization of batch and semibatch processes. This paper details the combination of an indirect solution scheme together with a parsimonious input parametrization. The idea is to parametrize the sensitivity-seeking inputs in a parsimonious way so as to decrease the computational load of constrained nonlinear Dynamic Optimization problems. In addition, this article discusses structural differences between direct and indirect approaches. The proposed method is tested on both a batch binary distillation column with ...
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Dynamic Optimization of constrained semi batch processes using pontryagin s minimum principle and parsimonious parameterization
27th European Symposium on Computer Aided Process Engineering, 2017Co-Authors: Erdal Aydin, Dominique Bonvin, Kai SundmacherAbstract:Abstract This paper proposes a PMP-based solution scheme with parsimonious parameterization of sensitivity-seeking arcs in order to reduce the computational complexity of constrained Dynamic Optimization problems. We tested our method on a binary batch distillation column and a two-phase semi-batch reactor for the hydroformylation of 1-dodecene. The performance of the proposed solution scheme is compared with the fully parameterized PMP-based and the direct simultaneous solution approaches. The results show that the alternative parameterization applied together with the PMP can reduce the computational time significantly.
Miguel Angel Gutierrezlimon - One of the best experts on this subject based on the ideXlab platform.
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Dynamic Optimization of a cryogenic air separation unit using a derivative free Optimization approach
Computers & Chemical Engineering, 2018Co-Authors: Israel Negrellosortiz, Antonio Florestlacuahuac, Miguel Angel GutierrezlimonAbstract:Abstract Optimal Dynamic product transition is a challenging and important issue in manufacturing plants. When a reliable Dynamic model is available, gradient-based Optimization algorithms can be used to achieve this aim. However, in some cases a first principles Dynamic model may not be available. In this work, we will assume that only input-output information from a Dynamic model embedded in a Dynamic process simulator is available for optimal product transitions between products. We present a derivative-free Optimization trust region approach to deal with the product Dynamic Optimization problem of an air separation unit used in several processing plants to obtain pure oxygen. High-purity oxygen is required in intensive energy applications such as steel plants and in combustion processes. A closed-loop model predictive control strategy is used where the system to be optimized is embedded in the ASPEN Dynamics simulation environment. The results demonstrate that black-box Dynamic models can be Dynamically optimized when model of the Dynamic model and/or its gradient information are not available. We have successfully applied the non-linear model predictive control and derivate-free approach to several oxygen composition and productivity transition issues.
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Dynamic Optimization of a cryogenic air separation unit using a derivative free Optimization approach
Computers & Chemical Engineering, 2018Co-Authors: Israel Negrellosortiz, Antonio Florestlacuahuac, Miguel Angel GutierrezlimonAbstract:Abstract Optimal Dynamic product transition is a challenging and important issue in manufacturing plants. When a reliable Dynamic model is available, gradient-based Optimization algorithms can be used to achieve this aim. However, in some cases a first principles Dynamic model may not be available. In this work, we will assume that only input-output information from a Dynamic model embedded in a Dynamic process simulator is available for optimal product transitions between products. We present a derivative-free Optimization trust region approach to deal with the product Dynamic Optimization problem of an air separation unit used in several processing plants to obtain pure oxygen. High-purity oxygen is required in intensive energy applications such as steel plants and in combustion processes. A closed-loop model predictive control strategy is used where the system to be optimized is embedded in the ASPEN Dynamics simulation environment. The results demonstrate that black-box Dynamic models can be Dynamically optimized when model of the Dynamic model and/or its gradient information are not available. We have successfully applied the non-linear model predictive control and derivate-free approach to several oxygen composition and productivity transition issues.
