Parallel Multi-Objective Particle Swarm Optimization for Large Swarm and High Dimensional Problems
Parallel Multi-Objective Particle Swarm Optimization for Large Swarm and High Dimensional Problems
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DOI：
10.1109/cec.2018.8477848
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发表时间：
2018-07
期刊：
影响因子：
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通讯作者：
M. M. Hussain-M.;N. Fujimoto
中科院分区：
文献类型：
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作者：
M. M. Hussain-M.;N. Fujimoto
在过去的几年中，并行的两个或多个目标的多目标粒子群优化算法（MOPSO）已经在文献中提出。可枚举的实现已经发布，但是它们没有实现更快的执行时间和良好的Pareto前沿。他们指出了一些档案处理的局限性，拿起非支配的解决方案，高维问题等大型群体。此外，还没有一个研究人员实现了MOPSO，并测试了大规模群体和高维问题的性能。特别是，他们跳过了高维问题。本文提出了一种基于CUDA架构的并行MOPSO算法的GPU快速实现方案，该方案采用了合并内存访问、伪随机数发生器（PRNG）、推力库、原子函数、并行归档等技术，并对解决大种群高维优化问题的性能产生了积极的影响。因此，我们提出的算法可以广泛应用于真实的优化问题。建议的并行实现MOPSO使用主从模型提供了高达182倍的加速比相比，相应的CPU MOPSO。
In last couple of years, parallel two or many objective MOPSO (Multi-objective Particle Swarm Optimization) have been proposed in literature. Denumerable implementations were published, however they had not achieved faster execution time and good Pareto fronts. They have alluded some limitation of archive handling, picked up nondominated solutions, high dimensional problems and so on for large swarm population. Moreover, none of the researchers have implemented MOPSO and tested the performance for large swarm population and high dimensional problem simultaneously. In particular, they skipped high dimensional problems. This paper presents a faster implementation of parallel MOPSO on a GPU based on the CUDA architecture, which uses coalescing memory access, pseudorandom number generator (PRNG), Thrust library, atomic function, parallel archiving and so on. In addition, our implementation has a positive impact on the performance to solve high dimensional optimization problems with large swarm population. Therefore, our proposed algorithm can be widely used in real optimizing problems. The proposed parallel implementation of MOPSO using a master-slave model provides up to 182 times speedup compared to the corresponding CPU MOPSO.