مشخصات مقاله:
عنوان فارسی مقاله:
یک چارچوب تحلیلی برای بهینه سازی ازدحام ذرات سخت افزاری با سرعت بالا
عنوان انگلیسی مقاله:
An analytical framework for high-speed hardware particle swarm optimization
کلمات کلیدی مقاله:
بهینه سازی ازدحام ذرات، سخت افزار، نرم افزار، کارایی، تحلیل و بررسی، آرایه های دروازه
کلمات کلیدی انگلیسی:
Particle swarm optimization – Hardware – Software – Performance – Analysis – Gate arrays
مناسب برای رشته های دانشگاهی زیر:
مهندسی کامپیوتر
مناسب برای گرایش های دانشگاهی زیر:
مهندسی سخت افزار، مهندسی الگوریتم و محاسبات
وضعیت مقاله انگلیسی و ترجمه:
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فهرست مطالب:
Abstract
Graphical abstract
Keywords
1. Introduction
2. Research objectives
3. Related work
4. Hardware design
5. Analytical model development
5.1. Goal
5.2. Input
5.3. Activities
5.4. Output
5.5. Outcomes
5.6. Performance
6. Analysis and evaluation
6.1. Implementation aspects
6.2. Hardware performance analysis
6.2.1. Partitioned versus nonpartitioned hardware implementations
6.2.2. Sequential versus parallel hardware implementations
6.3. Analysis of combined indicators
6.4. General evaluation
7. Conclusion
Declaration of Competing Interest
Appendix A
References
قسمتی از مقاله انگلیسی:
1. Introduction
Optimization is an important concept in the engineering domain [1–4]. Indeed, all engineering applications involve some sort of optimization in order to realize the final product. Optimization involves reducing cost, power consumption, time delay or increases the yield, profit, quality of solution, etc. As engineering problems became more challenging; with properties such as large size, discontinuity, non-differentiability, non-linearity, multi-objectiveness, and mixed variable types; it becomes essential to develop new optimization paradigms that can reach acceptable solutions in less time. Meta-heuristics is a substantial and considerably important optimization paradigm that can be used to tackle nowadays engineering problems. A major class of meta-heuristics is Population-based algorithms, which update a population of solutions over a number of iterations until some stopping condition is satisfied. Population-based algorithms are categorized based on the inspiration behind their population update scheme. These categories include Evolutionary Algorithms and Swarm Intelligence algorithms. Due to the heavy computational workload constituting of optimization with population-based algorithms, computational speed matters for solving many dynamic and real-time problems with Evolutionary Algorithms and Swarm Intelligence algorithms. For example, radio resource scheduling for recent generations of wireless communication networks requires a decision to be made within nano-seconds level, which is not viable with conventional computing infrastructures [5,6]. Further research has been conducted over the past two decades in order to speed up the execution of such algorithms, whether by manipulating parameters, developing multiple/distributed versions or implementing them on specific hardware.