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• W2146453314 abstract "Three new Genetic Algorithm (GA) approaches and four new Particle Swarm Optimization (PSO) approaches are proposed and used to solve a Curve fitting problem for Parasitic Extraction Macro-modeling application. For GA, the first proposed approach, Diagonal GA (DGA); is based on replacing the traditional random population initialization method with a deterministic diagonal-like one. The second proposed approach, Elite Condensation GA (ECGA); is based on fine-tuning the GA by explicitly condensing the population around a number of elite individuals. The third proposed approach, ECGA2, is a modified version of ECGA; that chooses elite members among all the population in each generation, then it divides the population into a number of sub-populations where each sub-population is composed of a single elite and a condensed population around it. Then, it performs GA operations on each of those sub-populations separately before merging them all into one population and keep repeating that divide-merging sequence. For PSO, in the first proposed approach, Wiggling PSO (WPSO); we enforce the particles to vibrate in their motion towards the best position -instead of straight motion- to enlarge the scanning area. The second approach, Incrementally Social PSO (ISPSO); is utilizing a variable weight for the social term (xg-x). This variability enables changing the social relationship between the particles from highly repulsive to highly attractive. Finally, we proposed a new Control inspired approach, PID-PSO, where we dealt with the PSO motion as a process that needs a controller to be optimized. It is quite common to use PSO to tune PID parameters but in this context we used PID to tune PSO motion. Eventually, we have mixed PID and ISPSO in a certain proposal which resulted in the best performance over the rest of methods. The performances of these seven proposed approaches were measured on an extensive real data sets provided by Mentor Graphics and used along with the understanding of the physical problem to offer various explanations of the theoretical aspects of the new extensions." @default.
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• W2146453314 date "2009-12-01" @default.
• W2146453314 modified "2023-10-03" @default.
• W2146453314 title "Interconnects Parasitic Extraction using natural optimization techniques" @default.
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• W2146453314 doi "https://doi.org/10.1109/icces.2009.5383279" @default.
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