Binary particle swarm optimization (BPSO) based state assignment for area minimization of sequential circuits
Binary particle swarm optimization (BPSO) based state assignment for area minimization of sequential circuits
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DOI：
10.1016/j.asoc.2013.08.004
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发表时间：
2013-12-01
影响因子：
8.7
通讯作者：
Sait, Sadiq M.
中科院分区：
文献类型：
--
作者：
El-Maleh, Aiman H.;Sheikh, Ahmad T.;Sait, Sadiq M.
有限状态机的状态分配是时序电路综合中的主要优化问题之一。它决定了其组合电路的复杂性，从而决定了其实现的面积、延迟、可测性和功耗。粒子群优化（PSO）是一种非确定性的启发式算法，它通过迭代地尝试改进关于给定质量度量的候选解来优化问题。PSO通过拥有一群称为粒子的候选解决方案来优化问题，并根据简单的数学公式在搜索空间中移动它们。本文提出了一种改进的二进制粒子群优化算法（BPSO），并证明了其在解决以区域优化为目标的时序电路综合中的状态分配问题中的有效性。实验结果表明，该算法克服了原BPSO算法的不足。实验结果表明，所提出的BPSO算法的有效性相比，其他BPSO变种在文献中报道，并在比较遗传算法（GA），模拟进化（SimE）和确定性算法，如绝地和新星。(c)2013爱思唯尔有限公司版权所有。
State assignment (SA) for finite state machines (FSMs) is one of the main optimization problems in the synthesis of sequential circuits. It determines the complexity of its combinational circuit and thus area, delay, testability and power dissipation of its implementation. Particle swarm optimization (PSO) is a non-deterministic heuristic that optimizes a problem by iteratively trying to improve a candidate solution with regard to a given measure of quality. PSO optimizes a problem by having a population of candidate solutions called particles, and moving them around in the search-space according to a simple mathematical formulae. In this paper, we propose an improved binary particle swarm optimization (BPSO) algorithm and demonstrate its effectiveness in solving the state assignment problem in sequential circuit synthesis targeting area optimization. It will be an evident that the proposed BPSO algorithm overcomes the drawbacks of the original BPSO algorithm. Experimental results demonstrate the effectiveness of the proposed BPSO algorithm in comparison to other BPSO variants reported in the literature and in comparison to Genetic Algorithm (GA), Simulated Evolution (SimE) and deterministic algorithms like Jedi and Nova. (c) 2013 Elsevier B.V. All rights reserved.