Improving the Performance of Whale Optimization Algorithm through OpenCL-Based FPGA Accelerator
Improving the Performance of Whale Optimization Algorithm through OpenCL-Based FPGA Accelerator
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DOI：
10.1155/2020/8810759
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发表时间：
2020-12
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影响因子：
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通讯作者：
Qiangqiang Jiang;Yuanjun Guo;Zhile Yang;Z. Wang;Dongsheng Yang;Xianyu Zhou
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文献类型：
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作者：
Qiangqiang Jiang;Yuanjun Guo;Zhile Yang;Z. Wang;Dongsheng Yang;Xianyu Zhou
鲸鱼优化算法（WOA）是一种新型的自然启发式群优化算法，在处理全局连续优化问题方面显示出优越性。然而，它的性能恶化时，应用于大规模的复杂问题，由于快速增加所需的巨大计算任务的执行时间。基于群体内的相互作用，WOA自然服从并行性，从而促进了有效的方法来减轻顺序WOA的缺点。本文采用现场可编程门阵列（FPGA）作为加速器，其高级综合采用开放计算语言（OpenCL）作为异构片上系统的通用编程范式。在此基础上，提出了一种新的WOA并行框架PWOA。该框架包括两种可行的并行模型，分别称为部分并行和全FPGA并行。在基于OpenCL的FPGA异构平台上，分别在CPU上执行WOA和PWOA，对10个著名的基准测试函数进行了实验。同时，采用粒子群优化（PSO）和竞争群优化（CSO）两种经典算法进行比较。数值结果表明，所提出的方法实现了一个有前途的计算性能加上相对大规模的复杂问题的有效优化。
Whale optimization algorithm (WOA), known as a novel nature-inspired swarm optimization algorithm, demonstrates superiority in handling global continuous optimization problems. However, its performance deteriorates when applied to large-scale complex problems due to rapidly increasing execution time required for huge computational tasks. Based on interactions within the population, WOA is naturally amenable to parallelism, prompting an effective approach to mitigate the drawbacks of sequential WOA. In this paper, field programmable gate array (FPGA) is used as an accelerator, of which the high-level synthesis utilizes open computing language (OpenCL) as a general programming paradigm for heterogeneous System-on-Chip. With above platform, a novel parallel framework of WOA named PWOA is presented. The proposed framework comprises two feasible parallel models called partial parallel and all-FPGA parallel, respectively. Experiments are conducted by performing WOA on CPU and PWOA on OpenCL-based FPGA heterogeneous platform, to solve ten well-known benchmark functions. Meanwhile, other two classic algorithms including particle swarm optimization (PSO) and competitive swarm optimizer (CSO) are adopted for comparison. Numerical results show that the proposed approach achieves a promising computational performance coupled with efficient optimization on relatively large-scale complex problems.