Evapotranspiration is an essential component of the hydrological cycle that is of particular interest for water resource *** quantification is helpful in irrigation scheduling,water balance studies,water allocation,**...
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Evapotranspiration is an essential component of the hydrological cycle that is of particular interest for water resource *** quantification is helpful in irrigation scheduling,water balance studies,water allocation,*** of reference evapotranspiration(ET0)using both gene expression programming(GEP)and artificial neural network(ANN)techniques was done using the daily meteorological data of the Pantnagar region,India,from 2010 to 2019.A total of 15 combinations of inputs were used in developing the ET0 *** model with the least number of inputs consisted of maximum and minimum air temperatures,whereas the model with the highest number of inputs consisted of maximum air temperature,minimum air temperature,mean relative humidity,number of sunshine hours,wind speed at 2mheight and extra-terrestrial radiation as inputs and with ET0 as the output for all the *** the GEP models were developed for a single functional set and pre-defined genetic operator values,while the best structure in each ANN model was found based on the performance during the testing *** was found that ANN models were superior to GEP models for the estimation *** was evident from the reduction in RMSE values ranging from 2%to 56%during training and testing phases in all the ANN models compared with GEP *** ANN models showed an increase of about 0.96%to 9.72%of R2 value compared to the respective GEP *** comparative study of these models with multiple linear regression(MLR)depicted that the ANN and GEP models were superior to MLR models.
Many software engineering tasks can potentially be automated using search heuristics. However, much work is needed in designing and evaluating search heuristics before this approach can be routinely applied to a softw...
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Many software engineering tasks can potentially be automated using search heuristics. However, much work is needed in designing and evaluating search heuristics before this approach can be routinely applied to a software engineering problem. Experimental methodology should be complemented with theoretical analysis to achieve this ***, there have been significant theoretical advances in the runtime analysis of evolutionary algorithms (EAs) and other search heuristics in other problem domains. We suggest that these methods could be transferred and adapted to gain insight into the behaviour of search heuristics on software engineering problems while automating software engineering.
In this study, stochastic computational intelligence techniques are presented for the solution of Troesch's boundary value problem. The proposed stochastic solvers use the competency of a feed-forward artificial n...
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In this study, stochastic computational intelligence techniques are presented for the solution of Troesch's boundary value problem. The proposed stochastic solvers use the competency of a feed-forward artificial neural network for mathematical modeling of the problem in an unsupervised manner, whereas the learning of unknown parameters is made with local and global optimization methods as well as their combinations. Genetic algorithm (GA) and pattern search (PS) techniques are used as the global search methods and the interior point method (IPM) is used for an efficient local search. The combination of techniques like GA hybridized with IPM (GA-IPM) and PS hybridized with IPM (PS-IPM) are also applied to solve different forms of the equation. A comparison of the proposed results obtained from GA, PS, IPM, PS-IPM and GA-IPM has been made with the standard solutions including well known analytic techniques of the Adomian decomposition method, the variational iterational method and the homotopy perturbation method. The reliability and effectiveness of the proposed schemes, in term of accuracy and convergence, are evaluated from the results of statistical analysis based on sufficiently large independent runs. [ABSTRACT FROM AUTHOR]
Traditional large-scale multi-objective optimization algorithms(LSMOEAs)encounter difficulties when dealing with sparse large-scale multi-objective optimization problems(SLM-OPs)where most decision variables are *** a...
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Traditional large-scale multi-objective optimization algorithms(LSMOEAs)encounter difficulties when dealing with sparse large-scale multi-objective optimization problems(SLM-OPs)where most decision variables are *** a result,many algorithms use a two-layer encoding approach to optimize binary variable Mask and real variable Dec ***,existing optimizers often focus on locating non-zero variable posi-tions to optimize the binary variables ***,approxi-mating the sparse distribution of real Pareto optimal solutions does not necessarily mean that the objective function is *** data mining,it is common to mine frequent itemsets appear-ing together in a dataset to reveal the correlation between *** by this,we propose a novel two-layer encoding learning swarm optimizer based on frequent itemsets(TELSO)to address these *** mined the frequent terms of multiple particles with better target values to find mask combinations that can obtain better objective values for fast ***-mental results on five real-world problems and eight benchmark sets demonstrate that TELSO outperforms existing state-of-the-art sparse large-scale multi-objective evolutionary algorithms(SLMOEAs)in terms of performance and convergence speed.
Community detection is a complex optimization problem that consists on searching homogeneous communities that belong to a given graph. This graph, which represent a network, has properties that enable the detection of...
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ISBN:
(纸本)9781450349390
Community detection is a complex optimization problem that consists on searching homogeneous communities that belong to a given graph. This graph, which represent a network, has properties that enable the detection of characteristics or functional relationships in the network. A large number of approaches have been proposed to solve this problem in different disciplines. Nevertheless, only a few research papers have applied community detection to power grids. This paper presents a new evolutionary algorithm for community detection that is applied in power grids. This evolutionary approach employs an efficient initialization strategy that only considers feasible solutions and uses two different search operators that allow the algorithm to obtain a good convergence and diversity of solutions. The preliminary results show that the proposed algorithm obtain quality results in real power grids.
The proceedings contain 80 papers. The topics discussed include: performance of ant routing algorithms when using TCP;evolving buffer overflow attacks with detector feedback;genetic representations for evolutionary mi...
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ISBN:
(纸本)3540718044
The proceedings contain 80 papers. The topics discussed include: performance of ant routing algorithms when using TCP;evolving buffer overflow attacks with detector feedback;genetic representations for evolutionary minimization of network coding resources;bacterial foraging algorithm with varying population for optimal power flow;an ant algorithm for the Steiner tree problem in graphs;message authentication protocol based on cellular automata;an adaptive global-local memetic algorithm to discover resources in P2P networks;evolutionary computation for quality of service Internet routing optimization;radio network design using population-based incremental learning and grid computing with BOINC;evaluation of different metaheuristics solving the RND problem;a comparative investigation on heuristic optimization of WCDMA radio networks;and design of a user software suite for probabilistic routing in ad-hoc networks.
In this paper, we apply an evolutionary Algorithm (EA) to solve the Rubinstein's Basic Alternating- Offer Bargaining Problem, and compare our experimental results with its analytic game-theoretic solution. The app...
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In this paper, we apply an evolutionary Algorithm (EA) to solve the Rubinstein's Basic Alternating- Offer Bargaining Problem, and compare our experimental results with its analytic game-theoretic solution. The application of EA employs an alternative set of assumptions on the players' behaviors. Experimental outcomes suggest that the applied co-evolutionary algorithm, one of evolutionary algorithms, is able to generate convincing approximations of the theoretic solutions. The major advantages of EA over the game-theoretic analysis are its flexibility and ease of application to variants of Rubinstein Bargaining Problems and complicated bargaining situations for which theoretic solutions are unavailable.
In this research paper we present an immunological algorithm (IA) to solve global numerical optimization problems for high-dimensional instances. Such optimization problems are a crucial component for many real-world ...
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In this research paper we present an immunological algorithm (IA) to solve global numerical optimization problems for high-dimensional instances. Such optimization problems are a crucial component for many real-world applications. We designed two versions of the IA: the first based on binary-code representation and the second based on real values, called opt-IMMALG01 and opt-IMMALG, respectively. A large set of experiments is presented to evaluate the effectiveness of the two proposed versions of IA. Both opt-IMMALG01 and opt-IMMALG were extensively compared against several nature inspired methodologies including a set of Differential Evolution algorithms whose performance is known to be superior to many other bio-inspired and deterministic algorithms on the same test bed. Also hybrid and deterministic global search algorithms (e.g., DIRECT, LeGO, PSwarm) are compared with both IA versions, for a total 39 optimization *** results suggest that the proposed immunological algorithm is effective, in terms of accuracy, and capable of solving large-scale instances for well-known benchmarks. Experimental results also indicate that both IA versions are comparable, and often outperform, the state-of-the-art optimization algorithms.
In the areas of chemical processes and energy systems, the relevance of black-box optimization problems is growing because they arise not only in the optimization of processes with modular/sequential simulation codes ...
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In the areas of chemical processes and energy systems, the relevance of black-box optimization problems is growing because they arise not only in the optimization of processes with modular/sequential simulation codes but also when decomposing complex optimization problems into bilevel programs. The objective function is typically discontinuous, non-differentiable, not defined in some points, noisy, and subject to linear and nonlinear relaxable and unrelaxable constraints. In this work, after briefly reviewing the main available direct-search methods applicable to this class of problems, we propose a new hybrid algorithm, referred to as PGS-COM, which combines the positive features of Constrained Particle Swarm, Generating Set Search, and Complex. The remarkable performance and reliability of PGS-COM are assessed and compared with those of eleven main alternative methods on twenty five test problems as well as two challenging process engineering applications related to the optimization of a heat recovery steam cycle and a styrene production process. (C) 2014 Elsevier Ltd. All rights reserved.
Modelling railway train-track dynamic systems with particular interest on track performance under the passage of the train relies on information which often carries some uncertainties. These uncertainties are usually ...
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