在高层次综合中,时变与固定加速度系数的粒子群驱动勘探:性能和质量评估

A. Sengupta, V. Mishra
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引用次数: 7

摘要

粒子群优化算法(PSO)的性能在很大程度上取决于加速系数这一重要调节指标的有效选择(特别是在设计空间探索(DSE)问题中),该指标在搜索过程中包含了在探索和利用之间进行临床平衡的能力。本文的主要贡献如下:a)新颖地分析了PSO中两种加速度系数(分层时变加速度系数和恒定加速度系数)的变化及其对HLS多目标DSE下收敛时间和探索时间的影响。该分析有助于设计者在DSE启动前将加速系数预先调整到最优值,以实现更好的收敛和探索时间。b)基于代距、最大帕累托最优前误差、间隔、扩散和加权度量等MO进化算法的质量指标,将PSO驱动的DSE (PSO-DSE)与先前的研究进行了新的性能比较。将两种加速度系数(恒定和时变)进行比较,结果表明,恒定加速度系数下的PSO-DSE比分层时变加速度系数下的平均勘探速度高9.5%。此外,在恒定加速度系数的条件下,PSO-DSE算法的代距、最大帕累托最优前误差、间隔、扩展和加权度量均优于以往的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Time Varying vs. Fixed Acceleration Coefficient PSO Driven Exploration during High Level Synthesis: Performance and Quality Assessment
The performance of particle swarm optimization (PSO) greatly depends upon the effective selection of vital tuning metric known as acceleration coefficients (especially when applied to design space exploration (DSE) problem) which incorporates ability to clinically balance between exploration and exploitation during searching. The major contributions of the paper are as follows: a) A novel analysis of two variants of acceleration coefficient (hierarchical time varying acceleration coefficient vs. Constant acceleration coefficient) in PSO and their impact on convergence time and exploration time in context of multi objective (MO) DSE in HLS. The analysis assists the designer in pre-tuning the acceleration coefficient to an optimal value for achieving better convergence and exploration time before DSE initiation, b) A novel performance comparison of PSO driven DSE (PSO-DSE) with previous works based on quality metrics for MO evolutionary algorithms such as generational distance, maximum pareto-optimal front error, spacing, spreading and weighted metric. When two variants of acceleration coefficients (constant and time varying) were compared, it was revealed from the results that the PSO-DSE has on average 9.5% better exploration speed with constant acceleration coefficient as compared to hierarchical time varying acceleration coefficient. Further, with setting of constant acceleration coefficient, the PSO-DSE produces results with efficient generational distance, maximum pareto-optimal front error, spacing, spreading and weighted metric as compared to previous approaches.
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