Application of Evolutionary Algorithms for Multi-objective Optimization in VLSI and Embedded Systems by M. C. Bhuvaneswari

Author:M. C. Bhuvaneswari
Language: eng
Format: epub
Publisher: Springer India, New Delhi

Fig. 5.7Solutions for WSGA (Deb 2008)

Deb et al. proposed the “Non-Dominated Sorting GA-II” or NSGA-II (Deb et al. 2002), which is a true multi-objective GA. It uses the notion of crowding distance to ensure diversity among the solutions in a population. Initially, a random seed is created. Chromosome encoding and objective functions are same as WSGA. The cost of each objective for all the solutions in determined and they are classified into Ranks based on non-dominance. The Rank I individuals are fully non-dominated whereas those in Rank 2 are dominated by the Rank I individuals and so on. Each solution is assigned a fitness based on its Rank and Crowding Distance. The Crowding Distance is a measure of the uniqueness of a solution. Crossover and mutation are performed on the individuals using the method described in (Krishnan and Katkoori 2006). The parents and offspring in a particular generation are merged and the individuals for the next generation are selected based on the crowding distance metric (Deb et al. 2002; Deb 2008). Selection of individuals with higher crowding distance is favored for better diversity among solutions. A population size of 100 is used in each generation and the algorithm is run for 200 generations. A flow diagram depicting the NSGA-II methodology for DFG scheduling is shown in Fig. 5.8.

Fig. 5.8NSGA-II based methodology for DFG scheduling

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