By M.C. Bhuvaneswari
This ebook describes how evolutionary algorithms (EA), together with genetic algorithms (GA) and particle swarm optimization (PSO) can be used for fixing multi-objective optimization difficulties within the zone of embedded and VLSI procedure layout. Many complicated engineering optimization difficulties may be modelled as multi-objective formulations. This publication offers an creation to multi-objective optimization utilizing meta-heuristic algorithms, GA and PSO and the way they are often utilized to difficulties like hardware/software partitioning in embedded structures, circuit partitioning in VLSI, layout of operational amplifiers in analog VLSI, layout house exploration in high-level synthesis, hold up fault trying out in VLSI trying out and scheduling in heterogeneous dispensed structures. it's proven how, in each one case, a few of the facets of the EA, specifically its illustration and operators like crossover, mutation, and so forth, may be individually formulated to unravel those difficulties. This publication is meant for layout engineers and researchers within the box of VLSI and embedded procedure layout. The booklet introduces the multi-objective GA and PSO in an easy and simply comprehensible means that may attract introductory readers.
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Additional info for Application of Evolutionary Algorithms for Multi-objective Optimization in VLSI and Embedded Systems
IEEE Trans Evol Comput 6(1):58–73 Deb K (2002) Multi-objective optimization using evolutionary algorithms. John Wiley & Sons, USA Drechsler R, Gunther W, Eschbach T, Linhard L, Angst G (2003) Recursive bi-partitioning of net lists for large number of partitions. J Syst Architect 49(12–15):521–528 Fiduccia CM, Mattheyses RM (1982) A linear time heuristic for improving network partitions. In: Proceedings of nineteenth design automation conference, IEEE Press, Piscataway, pp 175–181 Gajski DD, Vahid F, Narayau S, Gong J (1994) Specification and design of embedded system.
0 multi-objective algorithms for MCNC benchmarks are better than the mean cut cost obtained using conventional GA (Areibi and Vannelli 1993; Mazumder and Rudnick 1999). 4. From the comparison results, it is observed that NSGA-II outperforms the other two algorithms with respect to two objectives. This shows that NSGA-II obtains smaller area imbalance and fewer net cuts compared to WSGA and MOPSO-CD for MCNC benchmarks in circuit bipartitioning applications. 5. It is seen that for most of the benchmark circuits NSGA-II is faster in yielding better bipartition solutions compared to WSGA and MOPSOCD.
Bhuvaneswari and M. Jagadeeswari Khan JA, Sait SM, Minhas MR (2002) Fuzzy bias less simulated evolution for multi-objective VLSI placement. In: IEEE CEC 2002, Hawaii, USA, 12–17 May 2002, pp 1642–1647 Mardhana E, Ikeguchi T (2003) Neuro search: a program library for neural network driven search meta-heuristics. In: Proceedings of 2003 international symposium on circuits and systems, 25–28 May 2003, Bangkok, Thailand, pp V-697–V-700 Mazumder P, Rudnick EM (1999) Genetic algorithms for VLSI design layout and test automation.