Nonlinear bioreactors are considered essential technology in chemical and biochemical industries. This paper presents a proposal of a robust model based fault diagnosis in a nonlinear bioreactor, formulated as the sol...
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Nonlinear bioreactors are considered essential technology in chemical and biochemical industries. This paper presents a proposal of a robust model based fault diagnosis in a nonlinear bioreactor, formulated as the solution of an inverse problem. The optimization problem is solved by using four different evolutionary strategies: Particle Swarm Optimization (PSO), Differential Evolution (DE), Covariance Matrix Adaptation Evolution Strategy (CMA-ES) and Particle Swarm Optimization with Memory (PSO-M), with DE resulting the best according to the evaluated quantitative indicators. The results obtained with this approach indicate advantages in comparison to other methods of fault diagnosis (FDI) present in literature. (C) 2016 The Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.
Hybridizing evolutionary algorithms with local search has become a popular trend in recent years. There is empirical evidence for various combinatorial problems where hybrid evolutionary algorithms perform better than...
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Hybridizing evolutionary algorithms with local search has become a popular trend in recent years. There is empirical evidence for various combinatorial problems where hybrid evolutionary algorithms perform better than plain evolutionary algorithms. Due to the rapid development of a highly active field of research, theory lags far behind and a solid theoretical foundation of hybrid metaheuristics is sorely needed. We are aiming at a theoretical understanding of why and when hybrid evolutionary algorithms are successful in combinatorial optimization. To this end, we consider a hybrid of a simple evolutionary algorithm, the (1+1) EA, with a powerful local search operator known as variable-depth search (VDS) or Kernighan-Lin. Three combinatorial problems are investigated: Mincut, Knapsack, and Maxsat. More precisely, we focus on simply structured problem instances that contain local optima which are very hard to overcome for many common metaheuristics. The plain (1+1) EA, iterated local search, and simulated annealing need exponential time for optimization, with high probability. In sharp contrast, the hybrid algorithm using VDS finds a global optimum in expected polynomial time. These results demonstrate the usefulness of hybrid evolutionary algorithms with VDS from a rigorous theoretical perspective.
Dynamic optimization problems involving two or more conflicting objectives appear in many real-world scenarios, and more cases are expected to appear in the near future with the increasing interest in the analysis of ...
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Dynamic optimization problems involving two or more conflicting objectives appear in many real-world scenarios, and more cases are expected to appear in the near future with the increasing interest in the analysis of streaming data sources in the context of Big Data applications. However, approaches combining dynamic multi objective optimization with preference articulation are still scarce. In this paper, we propose a new dynamic multi-objective optimization algorithm called InDM2 that allows the preferences of the decision maker (DM) to be incorporated into the search process. When solving a dynamic multi-objective optimization problem with InDM2, the DM can not only express her/his preferences by means of one or more reference points (which define the desired region of interest), but these points can be also modified interactively. InDM2 is enhanced with methods to graphically display the different approximations of the region of interest obtained during the optimization process. In this way, the DM is able to inspect and change, in optimization time, the desired region of interest according to the information displayed. We describe the main features of InDM2 and detail how It is implemented. Its performance is illustrated using both synthetic and real-world dynamic multi-objective optimization problems.
The generalized travelling salesperson problem is an important NP-hard combinatorial optimization problem for which metaheuristics, such as local search and evolutionary algorithms, have been used very successfully. T...
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The generalized travelling salesperson problem is an important NP-hard combinatorial optimization problem for which metaheuristics, such as local search and evolutionary algorithms, have been used very successfully. Two hierarchical approaches with different neighbourhood structures, namely a cluster-based approach and a node-based approach, have been proposed by Hu and Raidl (2008) for solving this problem. In this article, local search algorithms and simple evolutionary algorithms based on these approaches are investigated from a theoretical perspective. For local search algorithms, we point out the complementary abilities of the two approaches by presenting instances where they mutually outperform each other. Afterwards, we introduce an instance which is hard for both approaches when initialized on a particular point of the search space, but where a variable neighbourhood search combining them finds the optimal solution in polynomial time. Then we turn our attention to analysing the behaviour of simple evolutionary algorithms that use these approaches. We show that the node-based approach solves the hard instance of the cluster-based approach presented in Corus et al. (2016) in polynomial time. Furthermore, we prove an exponential lower bound on the optimization time of the node-based approach for a class of Euclidean instances.
We consider the usage of evolutionary algorithms for multiobjective programming (MOP), i.e. for decision problems with alternatives taken from a real-valued vector space and evaluated according to a vector-valued obje...
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We consider the usage of evolutionary algorithms for multiobjective programming (MOP), i.e. for decision problems with alternatives taken from a real-valued vector space and evaluated according to a vector-valued objective function. Selection mechanisms, possibilities of temporary fitness deterioration, and problems of unreachable alternatives for such multiobjective evolutionary algorithms (MOEAs) are studied. Theoretical properties of MOEAs such as stochastic convergence with probability 1 are analyzed. (C) 1999 Elsevier Science B.V. All rights reserved.
In this article, a novel approach to deal with the design of in-building wireless networks deployments is proposed. This approach known as MOQZEA (Multiobjective Quality Zone Based evolutionary Algorithm) is a hybrid ...
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In this article, a novel approach to deal with the design of in-building wireless networks deployments is proposed. This approach known as MOQZEA (Multiobjective Quality Zone Based evolutionary Algorithm) is a hybrid evolutionary algorithm adapted to use a novel fitness function, based on the definition of quality zones for the different objective functions considered. This approach is conceived to solve wireless network design problems without previous information of the required number of transmitters, considering simultaneously a high number of objective functions and optimizing multiple configuration parameters of the transmitters.
This paper is devoted to the identification problems for structures which contain cracks. The problem of crack identification is formulated as the minimization of the difference between the measured and computed value...
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This paper is devoted to the identification problems for structures which contain cracks. The problem of crack identification is formulated as the minimization of the difference between the measured and computed values of displacements or stresses at selected boundary nodes. The coupling of the dual boundary element method and evolutionary algorithms is proposed to solve the problem. The identification of single cracks of different shapes is presented. The multiple crack identification is also considered. The problem of the identification of unknown number of cracks is formulated by introducing a special kind of chromosome. The influence of random errors in experimentally measured displacements on a convergence of the evolutionary identification is examined. A hybrid evolutionary approach based on sensitivity information of the fitness: function is tested. Several numerical examples are presented. (C) 2001 Elsevier Science Ltd. All rights reserved.
In this paper mathematical programming problems with fuzzy constraints are dealt with. Fuzzy solutions are obtained by means of a parametric approach in conjunction with evolutionary techniques. Some relevant characte...
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In this paper mathematical programming problems with fuzzy constraints are dealt with. Fuzzy solutions are obtained by means of a parametric approach in conjunction with evolutionary techniques. Some relevant characteristics of the evolutionary algorithm are for instance a real-coded representation of solutions and the preselection scheme as niche formation and elitist technique. Three test problems with fuzzy constraints and different structures are used in order to check and compare the proposed technique. The results obtained are very good in comparison with those from another methods. (C) 2003 Elsevier Science Inc. All rights reserved.
This paper presents a survey of evolutionary algorithms designed for clustering tasks. It tries to reflect the profile of this area by focusing more on those subjects that have been given more importance in the litera...
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This paper presents a survey of evolutionary algorithms designed for clustering tasks. It tries to reflect the profile of this area by focusing more on those subjects that have been given more importance in the literature. In this context, most of the paper is devoted to partitional algorithms that look for hard clusterings of data, though overlapping (i.e., soft and fuzzy) approaches are also covered in the paper. The paper is original in what concerns two main aspects. Firsts it provides an up-to-date overview that is fully devoted to evolutionary algorithms for clustering, is not limited to any particular kind of evolutionary approach, and comprises advanced topics like multiobjective and ensemble-based evolutionary clustering. Second, it provides a taxonomy that highlights some very important aspects in the context of evolutionary data clustering, namely, fixed or variable number of clusters, cluster-oriented or nonoriented operators, context-sensitive or context-insensitive operators, guided or unguided operators, binary, integer, or real encodings, centroid-based, medoid-based, label-based, tree-based, or graph-based representations, among others. A number of references are provided that describe applications of evolutionary algorithms for clustering in different domains, such as image processing, computer security, and bioinformatics. The paper ends by addressing some important issues and open questions that can be subject of future research.
Thermodynamic simulation programs are widely used for designing complex thermal Systems, but most of them do not incorporate second law optimization techniques. In this study, an efficient optimization strategy is pre...
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Thermodynamic simulation programs are widely used for designing complex thermal Systems, but most of them do not incorporate second law optimization techniques. In this study, an efficient optimization strategy is presented, which integrates three optimization techniques with a professional power plant and a cogeneration simulator so as to perform exergoeconomic optimization of complex thermal systems and generate combined pinch and exergy representations. This paper deals with the application of an evolutionary algorithm based on NSGA-II to multi-objective thermoeconomic optimization of coupling desalination plant with pressurized water reactor (PWR). In addition, one-objective thermoeconomic optimization through genetic algorithm and mixed integer non-linear mathematical programming methods has been applied for evaluation of multi-objective optimization. The thermodynamic simulation of this plant has been performed in the THERMOFLEX simulator. An Excel Add-in called THERMOFLEX link has been developed to Calculate the exergy of each stream from THERMOFLEX Simulation results. In addition, a computer code has been developed for thermoeconomic and improved combined pinch-exergy analysis in the MATLAB environment. Also, multi-objective and one-objective evolutionary algorithm optimization has been performed in MATLAB and one-objective mathematical programming has been performed in LINGO software. Both the design configuration and the process variables are optimized Simultaneously. The optimization algorithm can choose among several design options included in a Superstructure of the feed water heaters and multistage flash desalination in a dual-purpose plant. For the assumptions and simplifications made in this study, a 3000 MWh PWR power plant similar to Bushehr power plant has been considered. Copyright (C) 2008 John Wiley & Sons, Ltd.
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