Application of Evolutionary Algorithms for Multi-objective by M.C. Bhuvaneswari

By M.C. Bhuvaneswari

This publication 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 quarter of embedded and VLSI approach layout. Many advanced engineering optimization difficulties might be modelled as multi-objective formulations. This booklet presents an advent 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 area exploration in high-level synthesis, hold up fault trying out in VLSI trying out and scheduling in heterogeneous allotted structures. it's proven how, in every one case, many of the features of the EA, specifically its illustration and operators like crossover, mutation, and so forth, could be individually formulated to unravel those difficulties. This booklet is meant for layout engineers and researchers within the box of VLSI and embedded method layout. The booklet introduces the multi-objective GA and PSO in an easy and simply comprehensible manner that might attract introductory readers.

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Application of Evolutionary Algorithms for Multi-objective Optimization in VLSI and Embedded Systems

This e-book 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 method layout. Many advanced engineering optimization difficulties may be modelled as multi-objective formulations.

Extra info for Application of Evolutionary Algorithms for Multi-objective Optimization in VLSI and Embedded Systems

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Some researchers used custom heuristics to solve HW/SW partitioning (Barros et al. 1993). This includes Global Criticality/Local Phase (GCLP) algorithm (Kalavade and Lee 1994), expert system of Lopez Vallejo et al. (2000), and binary constraint search algorithm by Vahid and Gajski (2001). Concerning the system model, further distinctions can be made. In particular, many researchers consider scheduling as a part of partitioning (Kalavade and Lee 1994; Niemann and Marwedel 1997; Chatha and vemuri 2001; Lopez-Vallejo et al.

8: XN Δ¼ dnes þ XN n¼1 XM   d i À d i¼1 d es þ Md n¼1 n ð2:8Þ where di and d are calculated using Eqs. 7, respectively. The parameter des n is the distance between the extreme solutions corresponding to the nth objective function. This metric takes a value of zero for an ideal distribution only when des ¼ 0 and all di values are identical to their mean d. d es ¼ 0 means that only true pareto-optimal solutions exist in the obtained solutions and if di values are identical to their mean d, then the distribution of intermediate solutions obtained is uniform.

Int J Comput Appl Technol 36(3/4):181–190 Kalavade A, Lee E (1994) A global criticality/local phase driven algorithm for the constrained hardware/software partitioning problem. In: Proceedings of third international workshop on hardware/software co design, 22–24 Sept 1994, Grenoble, pp 42–48 Kirkpatrick S Jr, Gelatt CD, Vecchi MP (1983) Optimization by simulated annealing. Comput Sci J 220(4598):671–680 Lopez Vallejo M, Grajal J, Lopez JC (2000) Constraint-driven system partitioning. In: Proceedings of design automation and test in Europe, Jan 2000, Paris, France, pp 411–416 Lopez Vallejo M, Lopez JC (2001) Multi-way clustering techniques for system level partitioning.

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