The paper is introducing the principles of a new global optimization strategy, Imperialistic Strategy (IS), applied to the Continuous Global Optimization Problem (CGOP). Inspired from existing multi-population strateg...
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ISBN:
(纸本)9781479984480
The paper is introducing the principles of a new global optimization strategy, Imperialistic Strategy (IS), applied to the Continuous Global Optimization Problem (CGOP). Inspired from existing multi-population strategies, like the Island Model (IM) approaches to parallel evolutionary algorithms (EA) and the Imperialistic Competitive Algorithm (ICA), the proposed IS method is considered an optimization strategy for the reason that it can integrate other well-known optimization methods, which in the context are regarded as sub-methods (although in other contexts they are prominent global optimization methods). Four optimization methods were implemented and tested in the roles of sub-methods: Genetic Algorithm (GA) (a floating-point representation variant), Differential Evolution (DE), Quantum Particle Swarm Optimization (QPSO) and Artificial Bee Colony (ABC). The optimization performances of the proposed optimization methods were compared on a test bed of 9 known multimodal optimization problems by applying an appropriate testing methodology. The obtained increased success rates of IS multi-population variants compared to the success rates of the optimization sub-methods run separately, combined with the increased computing efficiencies possible to be perceived for parallel and distributed implementations, demonstrated that IS is a promising approach to CGOP.
DEAP (Distributed evolutionaryalgorithms in Python) is a novel evolutionary computation framework for rapid prototyping and testing of ideas. Its design departs from most other existing frameworks in that it seeks to...
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ISBN:
(纸本)9781450311786
DEAP (Distributed evolutionaryalgorithms in Python) is a novel evolutionary computation framework for rapid prototyping and testing of ideas. Its design departs from most other existing frameworks in that it seeks to make algorithms explicit and data structures transparent, as opposed to the more common black box type of frameworks. It also incorporates easy parallelism where users need not concern themselves with gory implementation details like synchronization and load balancing, only functional decomposition. Several examples illustrate the multiple properties of DEAP.
Distributed evolutionaryalgorithms are of increasing interest and importance for three main reasons: (i) a well designed distributed evolutionary algorithm (dEA) can outperform a 'standard' EA in terms of rel...
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ISBN:
(纸本)9781424450534
Distributed evolutionaryalgorithms are of increasing interest and importance for three main reasons: (i) a well designed distributed evolutionary algorithm (dEA) can outperform a 'standard' EA in terms of reliability, solution quality, and speed;(ii) they can (of course) be implemented on parallel hardware, and hence combine efficient utilization of parallel resources with very fast and reliable optimization;(iii) parallel hardware resources are increasingly common. A dEA operates as separate evolving populations with occasional interaction between them via 'migration'. A specific dEA is characterized by the topology and nature of these interactions. Although the field is sizeable, there is still relatively little exploration of the performance of alternative topologies and interaction mechanisms. In this paper we compare some simple, novel dEA topologies with the cube-based topology that forms the basis of Alba et al's GD-RCGA (a state of the art dEA). We find the best results (when topologies are compared on a like for like basis in terms of number of processors) emerge from a three-level tree-based topology.
When compared to biological experiments, using computational protein models can save time and effort in identifying native conformations of proteins. Nonetheless, given the sheer size of the conformation space, identi...
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ISBN:
(纸本)9780819471529
When compared to biological experiments, using computational protein models can save time and effort in identifying native conformations of proteins. Nonetheless, given the sheer size of the conformation space, identifying the native conformation remains a computationally hard problem - even in simplified models such as hydrophobic-hydrophilic (HP) models. Distributed systems have become the focus of protein folding, providing high performance computing power to accommodate the conformation space. To use a distributed system efficiently (with limited resources), an appropriate strategy should be designed accordingly. Communication incurs overhead but can provide useful information in distributed systems through careful consideration. Our study focuses on understanding the behavior of distributed systems and developing an efficient communication strategy to save computational effort in order to obtain good solutions. In this paper, we propose a distributed caching strategy, which reuses partial results of computations and transmits the cached and reusable information among neighboring inter-connected processors. In order to validate this idea in a practical setting, we present algorithms to retrieve and restore the cached information and apply them to 2D triangular HP lattice models through coarse-grained parallel genetic algorithms (CPGAs). Our experimental results demonstrate the time savings as well as the limits in caching improvements for our distributed caching strategy.
The paper presents an agent-based architecture facilitating implemetation of parallel evolutionary algorithms, utilising the novel concept of a flock. The model proposed is an extension to classical regional parallel ...
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ISBN:
(纸本)3540221239
The paper presents an agent-based architecture facilitating implemetation of parallel evolutionary algorithms, utilising the novel concept of a flock. The model proposed is an extension to classical regional parallelevolutionary algorithm. Flocks introduce additional level of organisation of the system, allowing for separation of distribution and evolution issues, and thus opening possibility of dynamic reconfiguration of subpopulations adequately to the structure of the problem being solved. Selected experimental results illustrate the idea "at work".
This paper proposes the Java evolutionary Computation Library (JECoLi), an adaptable, flexible, extensible and reliable software framework implementing metaheuristic optimization algorithms, using the Java programming...
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ISBN:
(纸本)9783642239601;9783642239595
This paper proposes the Java evolutionary Computation Library (JECoLi), an adaptable, flexible, extensible and reliable software framework implementing metaheuristic optimization algorithms, using the Java programming language. JECoLi aims to offer a solution suited for the integration of evolutionary Computation (EC)-based approaches in larger applications, and for the rapid and efficient benchmarking of EC algorithms in specific problems. Its main contributions are (i) the implementation of pluggable parallelization modules, independent from the EC algorithms, allowing the programs to adapt. to the available hardware resources in a transparent way, without. changing the base code;(ii) a flexible platform for software quality assurance that allows creating tests for the implemented features and for user-defined extensions. The library is freely available as an open-source project.
The goal of this paper is to investigate under which settings it is effective to use an asynchronous version of the master-slave model of a population-based optimization algorithm. Both versions, synchronous and async...
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ISBN:
(纸本)9781905088294
The goal of this paper is to investigate under which settings it is effective to use an asynchronous version of the master-slave model of a population-based optimization algorithm. Both versions, synchronous and asynchronous, are implemented and compared using data from the multi-objective identification of parameters for a material constitutive law for concrete. Results have shown that the synchronous version is preferable for the low number of processors whereas for the high number of available CPUs the asynchronous version is clear winner.
Forest fires are a critical natural hazard in many regions of the World. For this reason, the prediction of this kind of phenomenon is considered a very important task that involves a high degree of complexity and pre...
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ISBN:
(纸本)9781509033393
Forest fires are a critical natural hazard in many regions of the World. For this reason, the prediction of this kind of phenomenon is considered a very important task that involves a high degree of complexity and precision. The ability to predict the forest fire behaviour constitutes an important tool for managers, helping to improve the effectiveness of fire prevention, detection and firefighting resources allocation. For this reason, prediction methods should be configured to operate as efficiently as possible. In this paper, a calibration study of evolutionary-Statistical System with Island Model's evolutionary parameters is presented (ESS-IM). ESS-IM is a general-parallel uncertainty reduction method applied to the forest fires spread prediction.
Island Model parallel genetic algorithms rely on various migration models and their associated parameter settings. A fine understanding of how the islands interact and exchange informations is an important issue for t...
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ISBN:
(纸本)9783662455234;9783662455227
Island Model parallel genetic algorithms rely on various migration models and their associated parameter settings. A fine understanding of how the islands interact and exchange informations is an important issue for the design of efficient algorithms. This article presents GridVis, an interactive tool for visualising the exchange of individuals and the propagation of fitness values between islands. We performed several experiments on a grid and on a cluster to evaluate GridVis' ability to visualise the activity of each machine and the communication flow between machines. Experiments have been made on the optimisation of a Weierstrass function using the EASEA language, with two schemes: a scheme based on uniform islands and another based on specialised islands (Exploitation, Exploration and Storage Islands).
In recent years, the use of Unmanned Aerial Vehicles (UAVs) has grown quickly due to its low cost and easily programming for autonomous path following for accomplishing different types of missions. Due to the numerous...
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ISBN:
(纸本)9781450343237
In recent years, the use of Unmanned Aerial Vehicles (UAVs) has grown quickly due to its low cost and easily programming for autonomous path following for accomplishing different types of missions. Due to the numerous advantages of multi-UAVs, when comparing with a single powerful one, to perform reconnaissance, monitoring, detection and surveying missions the use of multi-UAVs is generally preferred. While the number of control points and the number of UAVs are increased, the complexity of the problem also increases. This paper presents a solution to the problem of minimum time coverage of ground areas using a number of UAVs. The solution is divided into two parts: Firstly the area is partitioned with K-means clustering and then the problem is solved in each cluster with parallel genetic algorithm approach on CUDA architecture. To illustrate the methodology, the paper presents the experimental results obtained with a multi-UAV system, which has a different number of control points. The results showed the proposed approach produces efficient solutions for these type NP-Hard problems of homeland security applications like wide-area surveillance and site security by using multiple UAVs.
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