Parallel Strategies for Harmony Search on CUDA to Solve Traveling Salesman Problem

Le, Kang Chian; Abdullah, Rosni

doi:10.1109/icitcs.2015.7293008

Cited by 1 publication

(3 citation statements)

References 13 publications

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“…Cecilia et al 8 proposed an agnostic vectorization approach for ACO on GPUs to solve TSP, and also analyzed the atomic‐based approach at pheromone updated on different GPUs. Le and Abdullah 9 presented parallel strategies applied harmony search on CUDA for solving TSP. In their experiment, the parallel implementation can acquire 32 times speedup as compared with CPU‐based implementation.…”

Section: Background and Related Workmentioning

confidence: 99%

“…Therefore, many scholars have tried to combine GPU parallel technology with intelligence optimization algorithms. For example, parallel ant colony optimization (ACO) on GPU, 6‐8 parallel strategies for harmony search on CUDA to solve traveling salesman problem (TSP), 9 parallel PSO on GPU, 10‐12 a parallel bees colony algorithm implementation on GPU, 13 parallel genetic algorithm, 14,15 parallel local search algorithm 16,17 . These GPU‐based implementations of intelligent optimization algorithms have indicated that the GPU can be applied to significantly improve the performance of the algorithms.…”

Section: Introductionmentioning

confidence: 99%

“…for harmony search on CUDA to solve traveling salesman problem (TSP), 9 parallel PSO on GPU, [10][11][12] a parallel bees colony algorithm implementation on GPU, 13 parallel genetic algorithm, 14,15 parallel local search algorithm. 16,17 These GPU-based implementations of intelligent optimization algorithms have indicated that the GPU can be applied to significantly improve the performance of the algorithms.…”

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confidence: 99%

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Graphic process units‐based chicken swarm optimization algorithm for function optimization problems

Lin

Zhong

Chen

2020

Concurrency and Computation

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This article focuses on how to design an efficient GPU-based chicken swarm optimization (CSO) algorithm (GCSO), so as to improve diversity and speed up convergence by running a large number of populations in parallel. GCSO mainly improves the sequential CSO in three aspects: (i) GCSO modifies the location updating equation of the rooster and proposes a parallel iterative strategy to transform the sequential iteration process into a parallel iterative process, thereby achieving fine-grained parallelism and improving the convergence speed. (ii) A multirange search strategy is proposed to build different neighborhoods for each flock on the graphic process units (GPU), so that each flock searched in their respective neighborhoods, thus increasing the density and diversity of the search, and making it not easy to fall into a local optimum. (iii) A new column storage structure is designed to meet the requirement of coalescent access on GPU. Twelve benchmark functions are selected to compare GCSO algorithm with some sequential intelligence optimization algorithms and the GPU-based particle swarm algorithm. The results show that the GCSO is able to obtain a speedup up to 163.09× compared with the CSO and achieve better optimization results in terms of both optimization accuracy and convergence speed than some intelligence optimization algorithms.

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Section: Background and Related Workmentioning

confidence: 99%