Mersenne twister: a 623-dimensionally equidistributed uniform pseudo-random number generator
ACM Transactions on Modeling and Computer Simulation (TOMACS) - Special issue on uniform random number generation
Handbook of Evolutionary Computation
Handbook of Evolutionary Computation
On the Convergence of Pattern Search Algorithms
SIAM Journal on Optimization
Analysis of Generalized Pattern Searches
SIAM Journal on Optimization
Locally-adaptive and memetic evolutionary pattern search algorithms
Evolutionary Computation
Revisiting Asynchronous Parallel Pattern Search for Nonlinear Optimization
SIAM Journal on Optimization
A particle swarm pattern search method for bound constrained global optimization
Journal of Global Optimization
Gpu gems 3
Many-threaded implementation of differential evolution for the CUDA platform
Proceedings of the 13th annual conference on Genetic and evolutionary computation
PARADE: a massively parallel differential evolution template for EASEA
SIDE'12 Proceedings of the 2012 international conference on Swarm and Evolutionary Computation
Journal of Parallel and Distributed Computing
A comparative study of three GPU-based metaheuristics
PPSN'12 Proceedings of the 12th international conference on Parallel Problem Solving from Nature - Volume Part II
From CPU to GP-GPU: challenges and insights in GPU-based environmental simulations
Proceedings of the 10th International Workshop on Middleware for Grids, Clouds and e-Science
Differential evolution for dynamic environments with unknown numbers of optima
Journal of Global Optimization
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This paper presents a novel parallel Differential Evolution (DE) algorithm with local search for solving function optimization problems, utilizing graphics hardware acceleration. As a population-based meta-heuristic, DE was originally designed for continuous function optimization. Graphics Processing Units (GPU) computing is an emerging desktop parallel computing technology that is becoming popular with its wide availability in many personal computers. In this paper, the classical DE was adapted in the data-parallel CPU-GPU heterogeneous computing platform featuring Single Instruction-Multiple Thread (SIMT) execution. The global optimal search of the DE was enhanced by the classical local Pattern Search (PS) method. The hybrid DE---PS method was implemented in the GPU environment and compared to a similar implementation in the common computing environment with a Central Processing Unit (CPU). Computational results indicate that the GPU-accelerated SIMT-DE-PS method is orders of magnitude faster than the corresponding CPU implementation. The main contribution of this paper is the parallelization analysis and performance analysis of the hybrid DE---PS with GPU acceleration. The research results demonstrate a promising direction for high speed optimization with desktop parallel computing on a personal computer.