Tsutsui et al., 2011 - Google Patents
Fast QAP solving by ACO with 2-opt local search on a GPUTsutsui et al., 2011
View PDF- Document ID
- 321740634989192555
- Author
- Tsutsui S
- Fujimoto N
- Publication year
- Publication venue
- 2011 IEEE Congress of Evolutionary Computation (CEC)
External Links
Snippet
This paper proposes a parallel ant colony optimization (ACO) for solving quadratic assignment problems (QAPs) on a graphics processing unit (GPU) by combining fast, 2-opt local search in compute unified device architecture (CUDA). In 2-opt for QAP, 2-opt moves …
- 238000005457 optimization 0 abstract description 6
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F9/00—Arrangements for programme control, e.g. control unit
- G06F9/06—Arrangements for programme control, e.g. control unit using stored programme, i.e. using internal store of processing equipment to receive and retain programme
- G06F9/46—Multiprogramming arrangements
- G06F9/50—Allocation of resources, e.g. of the central processing unit [CPU]
- G06F9/5005—Allocation of resources, e.g. of the central processing unit [CPU] to service a request
- G06F9/5027—Allocation of resources, e.g. of the central processing unit [CPU] to service a request the resource being a machine, e.g. CPUs, Servers, Terminals
- G06F9/505—Allocation of resources, e.g. of the central processing unit [CPU] to service a request the resource being a machine, e.g. CPUs, Servers, Terminals considering the load
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F9/00—Arrangements for programme control, e.g. control unit
- G06F9/06—Arrangements for programme control, e.g. control unit using stored programme, i.e. using internal store of processing equipment to receive and retain programme
- G06F9/46—Multiprogramming arrangements
- G06F9/50—Allocation of resources, e.g. of the central processing unit [CPU]
- G06F9/5061—Partitioning or combining of resources
- G06F9/5066—Algorithms for mapping a plurality of inter-dependent sub-tasks onto a plurality of physical CPUs
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F9/00—Arrangements for programme control, e.g. control unit
- G06F9/06—Arrangements for programme control, e.g. control unit using stored programme, i.e. using internal store of processing equipment to receive and retain programme
- G06F9/46—Multiprogramming arrangements
- G06F9/50—Allocation of resources, e.g. of the central processing unit [CPU]
- G06F9/5083—Techniques for rebalancing the load in a distributed system
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computer systems based on biological models
- G06N3/12—Computer systems based on biological models using genetic models
- G06N3/126—Genetic algorithms, i.e. information processing using digital simulations of the genetic system
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F9/00—Arrangements for programme control, e.g. control unit
- G06F9/06—Arrangements for programme control, e.g. control unit using stored programme, i.e. using internal store of processing equipment to receive and retain programme
- G06F9/30—Arrangements for executing machine-instructions, e.g. instruction decode
- G06F9/38—Concurrent instruction execution, e.g. pipeline, look ahead
- G06F9/3885—Concurrent instruction execution, e.g. pipeline, look ahead using a plurality of independent parallel functional units
- G06F9/3889—Concurrent instruction execution, e.g. pipeline, look ahead using a plurality of independent parallel functional units controlled by multiple instructions, e.g. MIMD, decoupled access or execute
- G06F9/3891—Concurrent instruction execution, e.g. pipeline, look ahead using a plurality of independent parallel functional units controlled by multiple instructions, e.g. MIMD, decoupled access or execute organised in groups of units sharing resources, e.g. clusters
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computer systems based on biological models
- G06N3/02—Computer systems based on biological models using neural network models
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F15/00—Digital computers in general; Data processing equipment in general
- G06F15/76—Architectures of general purpose stored programme computers
- G06F15/80—Architectures of general purpose stored programme computers comprising an array of processing units with common control, e.g. single instruction multiple data processors
- G06F15/8007—Architectures of general purpose stored programme computers comprising an array of processing units with common control, e.g. single instruction multiple data processors single instruction multiple data [SIMD] multiprocessors
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N99/00—Subject matter not provided for in other groups of this subclass
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
- G06F19/10—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Tsutsui et al. | ACO with tabu search on a GPU for solving QAPs using move-cost adjusted thread assignment | |
Yuan et al. | Evolutionary multitasking in permutation-based combinatorial optimization problems: Realization with TSP, QAP, LOP, and JSP | |
Tsutsui et al. | Solving quadratic assignment problems by genetic algorithms with GPU computation: a case study | |
Ke et al. | Hybridization of decomposition and local search for multiobjective optimization | |
Shen et al. | Robustness measures and robust scheduling for multi-objective stochastic flexible job shop scheduling problems | |
Huang et al. | Multi-objective flexible job-shop scheduling problem using modified discrete particle swarm optimization | |
Tosun | On the performance of parallel hybrid algorithms for the solution of the quadratic assignment problem | |
Tsutsui et al. | Fast QAP solving by ACO with 2-opt local search on a GPU | |
Boyer et al. | Recent advances on GPU computing in operations research | |
Subotic et al. | Parallelized multiple swarm artificial bee colony algorithm (MS-ABC) for global optimization | |
Arbelaez et al. | A GPU implementation of parallel constraint-based local search | |
Cheng et al. | A two-stage hybrid memetic algorithm for multiobjective job shop scheduling | |
Chen et al. | CUDA-based genetic algorithm on traveling salesman problem | |
Wahib et al. | Optimization of parallel genetic algorithms for nVidia GPUs | |
Krömer et al. | A comparison of many-threaded differential evolution and genetic algorithms on CUDA | |
Gong et al. | GWMA: the parallel implementation of woodpecker mating algorithm on the GPU | |
Yu et al. | A parallel double-level multiobjective evolutionary algorithm for robust optimization | |
Li et al. | Evolutionary computation and reinforcement learning integrated algorithm for distributed heterogeneous flowshop scheduling | |
Soca et al. | PUGACE, a cellular evolutionary algorithm framework on GPUs | |
Su et al. | Fast pareto set approximation for multi-objective flexible job shop scheduling via parallel preference-conditioned graph reinforcement learning | |
Bożejko | A new class of parallel scheduling algorithms | |
Cárdenas et al. | A solution for the quadratic assignment problem (QAP) through a parallel genetic algorithm based grid on GPU | |
Ali et al. | Boosting cultural algorithms with an incongruous layered social fabric influence function | |
Lieber et al. | Scalable high-quality 1D partitioning | |
Tsutsui et al. | An analytical study of GPU computation for solving QAPs by parallel evolutionary computation with independent run |