Download AIDA-CMK: Multi-Algorithm Optimization Kernel Applied to by Ricardo Lourenço, Nuno Lourenço, Nuno Horta PDF

By Ricardo Lourenço, Nuno Lourenço, Nuno Horta

This paintings addresses the learn and improvement of an cutting edge optimization kernel utilized to analog built-in circuit (IC) layout. really, this works describes the ameliorations contained in the AIDA Framework, an digital layout automation framework absolutely built through on the built-in Circuits Group-LX of the Instituto de Telecomunicações, Lisbon. It focusses on AIDA-CMK, by way of improving AIDA-C, that's the circuit optimizer section of AIDA, with a brand new multi-objective multi-constraint optimization module that constructs a base for a number of set of rules implementations. The proposed answer implements 3 ways to multi-objective multi-constraint optimization, specifically, an evolutionary technique with NSGAII, a swarm intelligence technique with MOPSO and stochastic hill hiking technique with MOSA. additionally, the carried out constitution permits the straightforward hybridization among kernels remodeling the former easy NSGAII optimization module right into a extra advanced and flexible module assisting a number of unmarried and multi-kernel algorithms. the 3 multi-objective optimization methods have been tested with CEC2009 benchmarks to restricted multi-objective optimization and validated with actual analog IC layout difficulties. The accomplished effects have been in comparison when it comes to functionality, utilizing statistical effects received from a number of autonomous runs. ultimately, a few hybrid ways have been additionally experimented, giving a foretaste to quite a lot of possibilities to discover in destiny work.

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Extra info for AIDA-CMK: Multi-Algorithm Optimization Kernel Applied to Analog IC Sizing

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3. However, if the MOSPO is removed, a closer look shows that the implemented MOSA greatly outperforms the NSGA-II in CF3–CF7, as illustrated in Fig. 4. 3 Evaluation of the Multi-kernel Methods 45 Fig. 3 Evaluation of the Multi-kernel Methods To experiment the multi-kernel methods and evaluate the performance of the merge operator in the parallel multi-algorithm kernel, two combinations are considered: first with two NSGAII kernels, and secondly with a MOSA and a NSGAII. The resultant POF is shown in Figs.

The process is repeated for rank 2, and successively until the elements pool is empty, leading to a quadratic algorithm. To solve ties between elements of the same rank, the crowding distance criterion is used. The crowding distance is an estimate of the density of elements. Each element with the same rank is assigned a value that relates to the distance to the closer elements. 6 illustrates the four ranking fronts and the crowding distance of the solution B in a problem with two objectives. The crowding distance of the elements in a front is computed by iterating in the M objective functions, sorting the elements using each objective and for each element accumulating the Fig.

D = 0) are set to the rank 1 and removed from the elements pool decrementing d for all the solutions in their Sds. The process is repeated for rank 2, and successively until the elements pool is empty, leading to a quadratic algorithm. To solve ties between elements of the same rank, the crowding distance criterion is used. The crowding distance is an estimate of the density of elements. Each element with the same rank is assigned a value that relates to the distance to the closer elements. 6 illustrates the four ranking fronts and the crowding distance of the solution B in a problem with two objectives.

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