Download PDF by M.C. Bhuvaneswari: Application of Evolutionary Algorithms for Multi-objective
By M.C. Bhuvaneswari
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 quarter of embedded and VLSI approach layout. Many complicated engineering optimization difficulties should be modelled as multi-objective formulations. This e-book presents 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 area exploration in high-level synthesis, hold up fault trying out in VLSI checking out and scheduling in heterogeneous allotted structures. it really is proven how, in each one case, a few of the features of the EA, particularly its illustration and operators like crossover, mutation, and so forth, might be individually formulated to resolve those difficulties. This booklet is meant for layout engineers and researchers within the box of VLSI and embedded approach layout. The e-book introduces the multi-objective GA and PSO in an easy and simply comprehensible approach that might entice introductory readers.
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Extra resources for Application of Evolutionary Algorithms for Multi-objective Optimization in VLSI and Embedded Systems
In: Proceedings of 7th annual conference evolutionary programming, 25–27 Mar 1998, San Deigo, pp 591–600 Sipakoulis GC, Karafyllidis I, Thanailakis A (1999) Genetic partitioning and placement for VLSI circuits. In Proceedings of sixth IEEE international conference on electronics, circuits and systems ICECS’99, 05–08 Sept 1999, Pafos, Cyprus, pp 1647–1650 Srinivasan V, Govindarajan S, Vemuri R (2001) Fine-grained and coarse-grained behavioral partitioning with effective utilization of memory and design space exploration for multiFPGA architectures.
2 Generational Distance (GD) This metric determines the average distance of the obtained solutions from the set T. GD is obtained using Eq. 4: X M GD ¼ dT i¼1 i 1=T M ð2:4Þ where di is the Euclidean distance between the solution i ∈ M and the nearest member of T, calculated using Eq. 5: vﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ u N 2 uX f ðniÞ À f ðnjÞ d i ¼ min t jT j j¼1 ð2:5Þ n¼1 ðjÞ where fn is the nth objective function value of the jth member of the true paretooptimal solutions (T ) and N is the total number of objectives used in the problem.
In this metric, the GD, which specifies the converging ability, and Δ, which specifies the diversity-preserving ability of the algorithm are combined. GD takes a small value for good convergence and Δ takes a small value for good diversity-preserving algorithm. The algorithm with an overall small value of W means that the algorithm is efficient in both the aspects. To combine the two metrics, the weights A and B are chosen depending on the importance of the performance metric. 5. The performance metrics namely ER, GD, MFE can be determined only when the true pareto-optimal solutions are known for the specified problem.
Application of Evolutionary Algorithms for Multi-objective Optimization in VLSI and Embedded Systems by M.C. Bhuvaneswari