As a popular type of parallel evolutionaryalgorithms, distributed evolutionary algorithms (DEAs) are widely used in a variety of fields. To get better solutions of concrete problems, a scheme of subpopulation diversi...
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ISBN:
(纸本)9781479920815
As a popular type of parallel evolutionaryalgorithms, distributed evolutionary algorithms (DEAs) are widely used in a variety of fields. To get better solutions of concrete problems, a scheme of subpopulation diversity based accepting immigrant in DEAs is proposed in this paper. In migration with this scheme, an immigrant will be put into its target subpopulation only if its current diversity is lower than a threshold value. algorithm analysis shows that the extra cost of time for this scheme is acceptable in many DEAs. Experiments are conducted on instances of the Traveling Salesman Problem from the TSPLIB. Results show that the DEA based on the proposed scheme can get better solutions than the one without it.
Migration strategy plays an important role in designing effective distributed evolutionary algorithms. In this work, a novel migration model inspired to the phenomenon known as biological invasion is devised. The migr...
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Migration strategy plays an important role in designing effective distributed evolutionary algorithms. In this work, a novel migration model inspired to the phenomenon known as biological invasion is devised. The migration strategy is implemented through a multistage process involving invading subpopulations and their competition with native individuals. Such a general approach is used within a stepping-stone parallel model adopting Differential Evolution as the local algorithm. The resulting distributedalgorithm is evaluated on a wide set of classical test functions against a large number of sequential and other distributed versions of Differential Evolution available in literature. The findings show that, in most of the cases, the proposed algorithm is able to achieve better performance in terms of both solution quality and convergence rate. (C) 2012 Elsevier Inc. All rights reserved.
In the recent years an increasing number of computational grids have been built, providing an unprecedented amount of computational power. Based on their inherent parallelism, evolutionaryalgorithms are well suited f...
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ISBN:
(纸本)9783642164927
In the recent years an increasing number of computational grids have been built, providing an unprecedented amount of computational power. Based on their inherent parallelism, evolutionaryalgorithms are well suited for distributed execution in such grids. Unfortunately, there are several challenges concerning the usage of a grid infrastructure (e.g. the synchronization and submission of jobs and file transfer tasks). In this paper we present a new framework which makes a Globus based grid easily accessible for evolutionaryalgorithms and takes care of the parallelization. The usability is demonstrated by the example of an evolutionaryalgorithm for the Traveling Salesman Problem.
In distributed mobile robot systems, autonomous robots accomplish complicated tasks through intelligent cooperation with each other. This paper presents behavior learning and online distributed evolution for cooperati...
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In distributed mobile robot systems, autonomous robots accomplish complicated tasks through intelligent cooperation with each other. This paper presents behavior learning and online distributed evolution for cooperative behavior of a group of autonomous robots. Learning and evolution capabilities are essential for a group of autonomous robots to adapt to unstructured environments. Behavior learning finds an optimal state-action mapping of a robot for a given operating condition. In behavior learning, a Q-learning algorithm is modified to handle delayed rewards in the distributed robot systems. A group of robots implements cooperative behaviors through communication with other robots. Individual robots improve the state-action mapping through online evolution with the crossover operator based on the Q-values and their update frequencies. A cooperative material search problem demonstrated the effectiveness of the proposed behavior learning and online distributed evolution method for implementing cooperative behavior of a group of autonomous mobile robots.
This paper presents a study of different models for the best individual's growth curve and the takeover time in a distributed evolutionary algorithm (dEA). The calculation of the takeover time is a common analytic...
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ISBN:
(纸本)0780393635
This paper presents a study of different models for the best individual's growth curve and the takeover time in a distributed evolutionary algorithm (dEA). The calculation of the takeover time is a common analytical approach to measure the selection pressure of an EA. This work is another step forward to mathematically unify and describe the roles of several parameters of the migration policy: the migration rate, the migration frequency, and the topology in the selection pressure induced by the dynamics of dEAs. In order to achieve these goals we comparatively evaluate the appropriateness of the well-known panmictic logistic model, hypergraph model and two new models for dEAs. We introduce here new accurate models for growth curves and takeover times in dEAs, and analytically explain the effects of the migration rate, migration frequency, and topology.
The aim of the paper is to present the application of the distributed evolutionary algorithms to selected optimization and defect identification problems. The coupling of evolutionaryalgorithms with the finite elemen...
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The aim of the paper is to present the application of the distributed evolutionary algorithms to selected optimization and defect identification problems. The coupling of evolutionaryalgorithms with the finite element method and the boundary element method creates a computational intelligence technique that is very suitable in computer aided optimal design. Several numerical examples for shape, topology optimization and identification are presented for elastic, thermoelastic and elastoplastic structures. (C) 2004 Elsevier Ltd. All rights reserved.
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