We propose an algorithm that is based on the Ant Colony Optimization (ACO) metaheuristic for producing harmonized melodies. The algorithm works in two stages. In the first stage it creates a melody. This melody is the...
详细信息
We propose an algorithm that is based on the Ant Colony Optimization (ACO) metaheuristic for producing harmonized melodies. The algorithm works in two stages. In the first stage it creates a melody. This melody is then harmonized according to the rules of Baroque harmony in the second stage. This is the first ACO algorithm to create music that uses domain knowledge and the first employed for harmonization of a melody.
New Particle Swarm Optimization (PSO) methods for dynamic and noisy function optimization are studied in this paper. The new methods are based on the hierarchical PSO (H-PSO) and a new type of H-PSO algorithm, called ...
详细信息
A self-organized allocation scheme for service tasks in computingsystems is proposed in this paper. Usually components of a computing system need some service from time to time in order perform their work efficiently...
详细信息
Dynamically reconfigurable hardware has already been deployed for accelerating computationally demanding applications. Some of these hardware architectures allow run time reconfiguration but this leads usually to a la...
详细信息
Dynamically reconfigurable hardware has already been deployed for accelerating computationally demanding applications. Some of these hardware architectures allow run time reconfiguration but this leads usually to a large reconfiguration overhead. The advantage of run time reconfiguration is that it allows new algorithmic solutions for many applications. To study the potential of frequent run time reconfiguration it is interesting to investigate its costs and benefits from an abstract point of view and to develop new architectural concepts. Multilevel reconfigurable architectures are one such concept that introduce several levels of reconfiguration. This paper deals with new types of multi-level reconfigurable architectures. The corresponding problem of finding the best granularity for different reconfiguration levels is formulated and investigated. Although this problem is shown to be NP-complete, an interesting restricted subcase is solved optimally in polynomial time. For the general case, a good heuristic is proposed that is based on solutions for the restricted case. Results on three example applications show that the reconfiguration cost can be reduced with the new architectures. Based on a proposed measure of relative efficiency it is also shown that the new architectures are more efficient so that they obtain a larger reconfiguration cost reduction with less additional hardware
A self-organized allocation scheme for service tasks in computingsystems is proposed in this paper. Usually components of a computing system need some service from time to time in order perform their work efficiently...
详细信息
ISBN:
(纸本)9781424400546
A self-organized allocation scheme for service tasks in computingsystems is proposed in this paper. Usually components of a computing system need some service from time to time in order perform their work efficiently. In adaptive computingsystems the components and the necessary tasks adapt to the needs of users or the environment. Since in such cases the type of service tasks will often change it is attractive to use reconfigurable hardware to perform the service tasks. The studied system consists of normal worker components and helper components which have reconfigurable hardware and can perform different service tasks. The speed with which a service tasks is executed by a helper depends on its actual configuration. Different strategies for the helpers to decide about service task acceptance and reconfiguration are proposed. These strategies are inspired by stimulus-threshold models that are used to explain task allocation in social insects
Multi task parallel processor arrays are a common machine architecture in which, typically, the tasks running in parallel occupy disjoint subarrays of the machine. On dynamically and partially reconfigurable processor...
详细信息
ISBN:
(纸本)0769523129
Multi task parallel processor arrays are a common machine architecture in which, typically, the tasks running in parallel occupy disjoint subarrays of the machine. On dynamically and partially reconfigurable processor arrays the tasks can be changed during run time. This is useful for online scenarios when the relative importance of tasks might change and therefore the assignment of computational resources to the tasks should be changed. Examples are optimization tasks in an online scenario in which the results of some tasks are needed earlier than expected at initialization. For such tasks the size of their subarrays must be increased because they need more computational resources to speed up. In this paper we design flexible Particle Swarm Optimization (PSO) algorithms for 2-dimensional reconfigurable processor arrays where the algorithms can change their size and have a good optimization behaviour. Since PSO is an iterative, individual-based optimization algorithm that relies upon interactions of neighbouring particles suitable for fine-grained parallel architectures. We propose a dynamic 2-dimensional hierarchical ordering of the particles within a tasks subarray so that the best particles are concentrated in the center. This gives the best particles the strongest influence on the swarm. A further advantage is that size reductions of the tasks can easily be done by cutting off the outer parts of the swarm which contain mainly the less good particles. It is experimentally shown that the proposed algorithms perform better than standard PSO algorithms under conditions with varying supply of computing resources that are available for the tasks. Moreover, also for conditions with constant supply of processing resources and no need for size changes the proposed algorithms perform well.
Multi-task parallel processor arrays are a common machine architecture in which, typically, the tasks running in parallel occupy disjoint subarrays of the machine. On dynamically and partially reconfigurable processor...
详细信息
Multi-task parallel processor arrays are a common machine architecture in which, typically, the tasks running in parallel occupy disjoint subarrays of the machine. On dynamically and partially reconfigurable processor arrays the tasks can be changed during run time. This is useful for online scenarios when the relative importance of tasks might change and therefore the assignment of computational resources to the tasks should be changed. Examples are optimization tasks in an online scenario in which the results of some tasks are needed earlier than expected at initialization. For such tasks the size of their subarrays must be increased because they need more computational resources to speed up. In this paper we design flexible particle swarm optimization (PSO) algorithms for 2-dimensional reconfigurable processor arrays where the algorithms can change their size and have good optimization behaviour. Since PSO is an iterative, individual-based optimization algorithm that relies upon interactions of neighbouring particles suitable for fine-grained parallel architectures. We propose a dynamic 2-dimensional hierarchical ordering of the particles within a tasks subarray so that the best particles are concentrated in the center. This gives the best particles the strongest influence on the swarm. A further advantage is that size reductions of the tasks can easily be done by cutting off the outer parts of the swarm which contain mainly the less good particles. It is experimentally shown that the proposed algorithms perform better than standard PSO algorithms under conditions with varying supply of computing resources that are available for the tasks. Moreover, also for conditions with constant supply of processing resources and no need for size changes the proposed algorithms perform well.
The purpose of this paper is to develop a numerical algorithm to track the preheat interface motion driven by radiation transfer in high-intensity laser experiments. Our front-tracking algorithm is coupled to a radiat...
详细信息
Particle Swarm Optimization (PSO) methods for dynamic function optimization are studied in this paper. We compare dynamic variants of standard PSO and Hierarchical PSO (H-PSO) on different dynamic benchmark functions....
详细信息
A hierarchical version of the particle swarm optimization method called H-PSO is introduced. In H-PSO the particles are arranged in a dynamic hierarchy that is used to define a neighborhood structure. Depending on the...
详细信息
A hierarchical version of the particle swarm optimization method called H-PSO is introduced. In H-PSO the particles are arranged in a dynamic hierarchy that is used to define a neighborhood structure. Depending on the quality of their so far best found solution the particles move up or down the hierarchy so that good particles have a higher influence on the swarm. Moreover, the hierarchy is used to define different search properties for the particles. Several variants of H-PSO are compared experimentally with variants of the standard PSO.
暂无评论