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 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.
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.
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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The level set method is commonly used to address image noise removal. Existing studies concentrate mainly on determining the speed function of the evolution equation. Based on the idea of a Canny operator, this letter...
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The level set method is commonly used to address image noise removal. Existing studies concentrate mainly on determining the speed function of the evolution equation. Based on the idea of a Canny operator, this letter introduces a new method of controlling the level set evolution, in which the edge strength is taken into account in choosing curvature flows for the speed function and the normal to edge direction is used to orient the diffusion of the moving interface. The addition of an energy term to penalize the irregularity allows for better preservation of local edge information. In contrast with previous Canny-based level set methods that usually adopt a two-stage framework, the proposed algorithm can execute all the above operations in one process during noise removal.
Our new evolutionary method allows electronic commerce (EC) services on distinct distribution channels. Launching EC services on the Internet require careful on mobile agents. It supports EC transition flows written i...
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This paper describes a project funded by the European Commission which seeks to provide the technology and software infrastructure necessary to support the next generation of evolving infohabitants in a way that makes...
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Quantum-Inspired evolutionary Algorithm (QEA) is proposed as one of approximate algorithms to solve combinatorial optimization. QEA is evolutionary computation that uses quantum bits and suerposition states in quantum...
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Surface wave dispersion analysis is widely used in geophysics to infer near-surface shear (S)-wave velocity profiles for a wide variety of applications. However, inversion of surface wave data is challenging for most ...
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Surface wave dispersion analysis is widely used in geophysics to infer near-surface shear (S)-wave velocity profiles for a wide variety of applications. However, inversion of surface wave data is challenging for most local-search methods due to its high nonlinearity and to its multimodality. In this work, we proposed and implemented a new Rayleigh wave dispersion curve inversion scheme based on backtracking search algorithm (BSA), a novel and powerful evolutionary algorithm (EA). Development of BSA is motivated by studies that attempt to develop an algorithm that possesses desirable features for different optimization problems which include the ability to reach a problem's global minimum more quickly and successfully with a small number of control parameters and low computational cost, as well as robustness and ease of application to different problem models. The proposed inverse procedure is applied to nonlinear inversion of fundamental-mode Rayleigh wave dispersion curves for near-surface S-wave velocity profiles. To evaluate calculation efficiency and effectiveness of BSA, four noise-free and four noisy synthetic data sets are firstly inverted. Then, the performance of BSA is compared with that of genetic algorithms (GA) by two noise-free synthetic data sets. Finally, a real-world example from a waste disposal site in NE Italy is inverted to examine the applicability and robustness of the proposed approach on real surface wave data. Furthermore, the performance of BSA is compared against that of GA by real data to further evaluate scores of BSA. Results from both synthetic and actual data demonstrate that BSA applied to nonlinear inversion of surface wave data should be considered good not only in terms of the accuracy but also in terms of the convergence speed. The great advantages of BSA are that the algorithm is simple, robust and easy to implement. Also there are fewer control parameters to tune. (C) 2015 Elsevier B.V. All rights reserved.
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