Strategies for the Parallel Implementation of Metaheuristics∗
نویسندگان
چکیده
Parallel implementations of metaheuristics appear quite naturally as an effective alternative to speed up the search for approximate solutions of combinatorial optimization problems. They not only allow solving larger problems or finding improved solutions with respect to their sequential counterparts, but they also lead to more robust algorithms. We review some trends in parallel computing and report recent results about linear speedups that can be obtained with parallel implementations using multiple independent processors. Parallel implementations of tabu search, GRASP, genetic algorithms, simulated annealing, and ant colonies are reviewed and discussed to illustrate the main strategies used in the parallelization of different metaheuristics and their hybrids.
منابع مشابه
OPTIMAL DECOMPOSITION OF FINITE ELEMENT MESHES VIA K-MEDIAN METHODOLOGY AND DIFFERENT METAHEURISTICS
In this paper the performance of four well-known metaheuristics consisting of Artificial Bee Colony (ABC), Biogeographic Based Optimization (BBO), Harmony Search (HS) and Teaching Learning Based Optimization (TLBO) are investigated on optimal domain decomposition for parallel computing. A clique graph is used for transforming the connectivity of a finite element model (FEM) into that of the cor...
متن کاملParallel Meta-heuristics Parallel Meta-heuristics
We present a state-of-the-art survey of parallel meta-heuristic strategies, developments, and results. We discuss general design and implementation principles that apply to most metaheuristic classes and instantiate these principles for neighborhood and population-based metaheuristics. We also identify a number of trends and promising research directions.
متن کاملPMF: A Multicore-Enabled Framework for the Construction of Metaheuristics for Single and Multiobjective Optimization
This paper describes the design and implementation of the Parallel Metaheuristics Framework (PMF), a C++ framework for the construction of single and multiobjective metaheuristics utilizing Intel’s Threading Building Blocks library to allow easy parallelization of computationally intensive algorithms. The framework demonstrates a generic approach to the construction of metaheuristics, striving ...
متن کاملParallelization Strategies for Hybrid Metaheuristics Using a Single GPU and Multi-core Resources
Hybrid metaheuristics are powerful methods for solving complex problems in science and industry. Nevertheless, the resolution time remains prohibitive when dealing with large problem instances. As a result, the use of GPU computing has been recognized as a major way to speed up the search process. However, most GPU-accelerated algorithms of the literature do not take benefits of all the availab...
متن کاملImplementation of the direction of arrival estimation algorithms by means of GPU-parallel processing in the Kuda environment (Research Article)
Direction-of-arrival (DOA) estimation of audio signals is critical in different areas, including electronic war, sonar, etc. The beamforming methods like Minimum Variance Distortionless Response (MVDR), Delay-and-Sum (DAS), and subspace-based Multiple Signal Classification (MUSIC) are the most known DOA estimation techniques. The mentioned methods have high computational complexity. Hence using...
متن کامل