Nature-Inspired Meta-Heuristics on Modern GPUs: State of the Art and Brief Survey of Selected Algorithms

Graphic processing units (GPUs) emerged recently as an exciting new hardware environment for a truly parallel implementation and execution of Nature and Bio-inspired Algorithms with excellent price-to-power ratio. In contrast to common multicore CPUs that contain up to tens of independent cores, the...

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Bibliographic Details
Published in:International journal of parallel programming 2014-10, Vol.42 (5), p.681-709
Main Authors: Krömer, Pavel, Platos, Jan, Snásel, Václav
Format: Article
Language:English
Subjects:
Algorithms
Analysis
Combinatorial analysis
Computer programming
Computer Science
Data compression
Design
Floating point arithmetic
Genetic algorithms
Heuristic
Heuristic methods
High performance computing
Optimization
Parallel processing
Processor Architectures
Programming languages
Software
Software Engineering/Programming and Operating Systems
State of the art
Studies
Swarm intelligence
Theory of Computation
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Summary:Graphic processing units (GPUs) emerged recently as an exciting new hardware environment for a truly parallel implementation and execution of Nature and Bio-inspired Algorithms with excellent price-to-power ratio. In contrast to common multicore CPUs that contain up to tens of independent cores, the GPUs represent a massively parallel single-instruction multiple-data devices that can nowadays reach peak performance of hundreds and thousands of giga floating-point operations per second. Nature and Bio-inspired Algorithms implement parallel optimization strategies in which a single candidate solution, a group of candidate solutions (population), or multiple populations seek for optimal solution or set of solutions of given problem. Genetic algorithms (GA) constitute a family of traditional and very well-known nature-inspired populational meta-heuristic algorithms that have proved its usefulness on a plethora of tasks through the years. Differential evolution (DE) is another efficient populational meta-heuristic algorithm for real-parameter optimization. Particle swarm optimization (PSO) can be seen as nature-inspired multiagent method in which the interaction of simple independent agents yields intelligent collective behavior. Simulated annealing (SA) is global optimization algorithm which combines statistical mechanics and combinatorial optimization with inspiration in metallurgy. This survey provides a brief overview of the latest state-of-the-art research on the design, implementation, and applications of parallel GA, DE, PSO, and SA-based methods on the GPUs.
ISSN:0885-7458
1573-7640
DOI:10.1007/s10766-013-0292-3