By Ricardo Lourenço, Nuno Lourenço, Nuno Horta
ISBN-10: 3319159542
ISBN-13: 9783319159546
This paintings addresses the examine and improvement of an cutting edge optimization kernel utilized to analog built-in circuit (IC) layout. rather, this works describes the changes contained in the AIDA Framework, an digital layout automation framework totally built via on the built-in Circuits Group-LX of the Instituto de Telecomunicações, Lisbon. It focusses on AIDA-CMK, via bettering AIDA-C, that is the circuit optimizer portion 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, particularly, an evolutionary procedure with NSGAII, a swarm intelligence procedure with MOPSO and stochastic hill hiking technique with MOSA. furthermore, the carried out constitution permits the straightforward hybridization among kernels remodeling the former basic NSGAII optimization module right into a extra developed and flexible module helping a number of unmarried and multi-kernel algorithms. the 3 multi-objective optimization methods have been established with CEC2009 benchmarks to restricted multi-objective optimization and demonstrated with actual analog IC layout difficulties. The accomplished effects have been in comparison by way of functionality, utilizing statistical effects got from a number of self reliant runs. ultimately, a few hybrid techniques have been additionally experimented, giving a foretaste to quite a lot of possibilities to discover in destiny work.
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Additional info for AIDA-CMK: Multi-Algorithm Optimization Kernel Applied to Analog IC Sizing
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3. 4 Multi-objective Simulated Annealing (MOSA) The MOSA is an adaptation of the single objective simulated annealing [8] to the multi-objective case. Like evolutionary algorithms, SA is inspired by a natural phenomenon. As the algorithm name states, it simulates the cooling and annealing of liquid material. When a liquid material cools and anneals quickly, the material will solidify into a suboptimal configuration. , ground state. 4. Despite the differences of concept, when comparing SA with stochastic hill climber, one cannot ignore the similarities.
IEEE Trans. 6(2), 182–197 (2002) 8. : Optimization by simulated annealing. Science 220 (4598), 671–680 (1983) 9. : Some single- and multiobjective optimization techniques. In: Unsupervised Classification, pp. 17–58. Springer, Berlin (2013) 10. : Particle swarm optimization. In: International Conference on Neural Networks, 1995 11. : Multi-objective particle swarm optimizers: a survey of the stateof-the-art. Int. J. Comput. Intell. Res. 2(3), 287–308 (2006) Chapter 4 Multi-objective Framework Implementation Abstract In this chapter, the details about the framework structure are presented, showing the application layers and their implementation.
The cubic approach to compute the rank of the elements in the population was introduced to clearly describe the nondominated sorting, however, in practice, the fast nondominated sorting algorithm described in [7] is used. It first computes the dominance between all solutions, storing the set of elements that are dominated (Sd) and the number of elements that dominate (d) for each solution. , d = 0) are set to the rank 1 and removed from the elements pool decrementing d for all the solutions in their Sds.
AIDA-CMK: Multi-Algorithm Optimization Kernel Applied to Analog IC Sizing by Ricardo Lourenço, Nuno Lourenço, Nuno Horta
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