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.
There has been renewed interest in modelling the behaviour of evolutionary algorithms (EAs) by more traditional mathematical objects, such as ordinary differential equations or Markov chains. The advantage is that the...
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There has been renewed interest in modelling the behaviour of evolutionary algorithms (EAs) by more traditional mathematical objects, such as ordinary differential equations or Markov chains. The advantage is that the analysis becomes greatly facilitated due to the existence of well established methods. However, this typically comes at the cost of disregarding information about the process. Here, we introduce the use of stochastic differential equations (SDEs) for the study of EAs. SDEs can produce simple analytical results for the dynamics of stochastic processes, unlike Markov chains which can produce rigorous but unwieldy expressions about the dynamics. On the other hand, unlike ordinary differential equations (ODEs), they do not discard information about the stochasticity of the process. We show that these are especially suitable for the analysis of fixed budget scenarios and present analogues of the additive and multiplicative drift theorems from runtime analysis. In addition, we derive a new more general multiplicative drift theorem that also covers non-elitist EAs. This theorem simultaneously allows for positive and negative results, providing information on the algorithm's progress even when the problem cannot be optimised efficiently. Finally, we provide results for some well-known heuristics namely Random Walk (RW), Random Local Search (RLS), the (1+1) EA, the Metropolis Algorithm (MA), and the Strong Selection Weak Mutation (SSWM) algorithm.
This paper uses evolutionary optimization algorithms to study the multi-objective optimization of mechanically stabilized earth (MSE) retaining walls. Five multi-objective optimization algorithms, including the non-do...
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This paper uses evolutionary optimization algorithms to study the multi-objective optimization of mechanically stabilized earth (MSE) retaining walls. Five multi-objective optimization algorithms, including the non-dominated sorting genetic algorithm II (NSGA-II), strength Pareto evolutionary algorithm II (SPEA-II), multi-objective particle swarm optimization (MOPSO), multi-objective multi-verse optimization (MVO), and Pareto envelope-based selection algorithm II (PESA-II), are applied to the design procedure. MSE wall design requires two major requirements: external stability and internal stability. In this study, the optimality criterion is to minimize cost and its trade-off with the factor of safety (FOS). To this end, two objectives are defined: (1) minimum cost, (2) maximum FOS. Three different strategies are considered for reinforcement combinations in the numerical simulations. Moreover, a sensitivity analysis was conducted on the variation of significant parameters, including backfill slope, wall height, horizontal earthquake coefficient, and surcharge load. The efficiency of the utilized algorithms was assessed through three well-known coverage set measures, diversity, and hypervolume. These measures were further examined using basic statistical measures (i.e., min, max, standard deviation) and the Friedman test with a 95% confidence level.
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.
The combination of traditional evolution strategies and heuristics from expert knowledge leads to the RELOPAT optimization program. In combination with reactor simulation codes-in this investigation the nodal reactor ...
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The combination of traditional evolution strategies and heuristics from expert knowledge leads to the RELOPAT optimization program. In combination with reactor simulation codes-in this investigation the nodal reactor code PRISM of Siemens/KWU - a powerful program system for the design of a numerically optimized pressurized water reactor (PWR) loading pattern was designed. Furthermore, the technic of parallel computing was introduced successfully. Simple parallel algorithmic structures on the level of optimization algorithms, combined with a low amount of communication between processors, allow workstation clusters to be used efficiently. Highly promising results were obtained by comparing recalculations of different known loading patterns for several PWRs of different sizes and varying constraints with solutions based on human expertise. The economic potential of the improvements now leads to a program that meets industrial requirements.
The Steiner tree problem (STP) aims to determine some Steiner nodes such that the minimum spanning tree over these Steiner nodes and a given set of special nodes has the minimum weight, which is NP-hard. STP includes ...
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The Steiner tree problem (STP) aims to determine some Steiner nodes such that the minimum spanning tree over these Steiner nodes and a given set of special nodes has the minimum weight, which is NP-hard. STP includes several important cases. The Steiner tree problem in graphs (GSTP) is one of them. Many heuristics have been proposed for STP, and some of them have proved to be performance guarantee approximation algorithms for this problem. Since evolutionary algorithms (EAs) are general and popular randomized heuristics, it is significant to investigate the performance of EAs for STP. Several empirical investigations have shown that EAs are efficient for STP. However, up to now, there is no theoretical work on the performance of EAs for STP. In this article, we reveal that the (1+1) EA achieves 3/2-approximation ratio for STP in a special class of quasi-bipartite graphs in expected runtime O(r(r + s - 1) . w(max)), where r, s, and w(max) are, respectively, the number of Steiner nodes, the number of special nodes, and the largest weight among all edges in the input graph. We also show that the (1+1) EA is better than two other heuristics on two GSTP instances, and the (1+1) EA may be inefficient on a constructed GSTP instance.
With uncertainty, reliability assessment is fundamental in structural optimization, because optimization itself is often against safety. To avoid Monte Carlo methods, the Reliability Index Approach (RIA) approximates ...
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With uncertainty, reliability assessment is fundamental in structural optimization, because optimization itself is often against safety. To avoid Monte Carlo methods, the Reliability Index Approach (RIA) approximates the structural failure probability and is formulated as a minimization problem, usually solved with fast gradient-methods, at the expense of local convergence, or even divergence, particularly for highly dimensional problems and implicit physical models. In this paper, a new procedure for global convergence of the RIA, with practical efficiency, is presented. Two novel evolutionary operators and a mixed real-binary genotype, suitable to hybridize any evolutionary Algorithm with elitist strategy, are developed. As an example, a shell laminate structure is presented and the results validated, showing good convergence and efficiency. The proposed method is expected to set the basis for further developments on the design optimization of more complex structures with multiple failure criteria.
The water sharing dispute in a multi-reservoir river basin forces the water resources planners to have an integrated operation of multi-reservoir system rather than considering them as a single reservoir system. Thus,...
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The water sharing dispute in a multi-reservoir river basin forces the water resources planners to have an integrated operation of multi-reservoir system rather than considering them as a single reservoir system. Thus, optimizing the operations of a multi-reservoir system for an integrated operation is gaining importance, especially in India. Recently, evolutionary algorithms have been successfully applied for optimizing the multi-reservoir system operations. The evolutionary optimization algorithms start its search from a randomly generated initial population to attain the global optimal solution. However, simple evolutionary algorithms are slower in convergence and also results in sub-optimal solutions for complex problems with hardbound variables. Hence, in the present study, chaotic technique is introduced to generate the initial population and also in other search steps to enhance the performance of the evolutionary algorithms and applied for the optimization of a multi-reservoir system. The results are compared with that of a simple GA and DE algorithm. From the study, it is found that the chaotic algorithm with the general optimizer has produced the global optimal solution (optimal hydropower production in the present case) within lesser generations. This shows that coupling the chaotic algorithm with evolutionary algorithm will enrich the search technique by having better initial population and also converges quickly. Further, the performances of the developed policies are evaluated for longer run using a simulation model to assess the irrigation deficits. The simulation results show that the model satisfactorily meets the irrigation demand in most of the time periods and the deficit is very less.
The computation of parameters for group contribution models in order to predict thermodynamic properties usually leads to a multiparameter optimization problem. The model parameters are calculated using a regression m...
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The computation of parameters for group contribution models in order to predict thermodynamic properties usually leads to a multiparameter optimization problem. The model parameters are calculated using a regression method and applying certain error criteria. A complex objective function occurs for which an optimization algorithm has to find the global minimum. For simple increment or group contribution models it is often sufficient to use simplex or gradient algorithms. However, if the model contains complex terms such as sums of exponential expressions, the search of the global or even of an fairly good optimum becomes rather difficult. evolutionary algorithms represent a possibility for solving such problems. In most cases, the use of biological principles for optimization problems yields satisfactory results. A genetic algorithm and an optimization method using an evolutionary strategy were programmed at the Institute for Thermodynamics at the University of Dortmund and were tested with an Enthalpy Based Group Contribution Model (EBGCM). The results obtained with these procedures were compared with the results obtained using a simplex algorithm. A test system was created and the corresponding objective function was examined in detail. For this purpose, 3D-plots were produced by varying two out of six model parameters. In this paper, the development of a genetic algorithm is presented and the fitting procedure of the model parameters is discussed. Part 2 of this article series will deal with the efficiency of evolutionary strategies applied to such a prototype of non-linear regression problems. (C) 1998 Published by Elsevier Science Ltd. All rights reserved.
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