Journal
INTERNATIONAL JOURNAL OF PARALLEL PROGRAMMING
Volume 42, Issue 5, Pages 681-709Publisher
SPRINGER/PLENUM PUBLISHERS
DOI: 10.1007/s10766-013-0292-3
Keywords
Graphic processing units; Genetic algorithms; Differential evolution; Particle swarm optimization; Simulated annealing; Survey
Categories
Funding
- European Regional Development Fund in the IT4Innovations Centre of Excellence project [CZ.1.05/1.1.00/02.0070]
- Bio-Inspired Methods: research, development and knowledge transfer project - Operational Programme Education for Competitiveness [CZ.1.07/2.3.00/20.0073]
- ESF
- state budget of the Czech Republic
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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.
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