Read e-book online AIDA-CMK: Multi-Algorithm Optimization Kernel Applied to PDF

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. relatively, this works describes the alterations contained in the AIDA Framework, an digital layout automation framework absolutely constructed via on the built-in Circuits Group-LX of the Instituto de Telecomunicações, Lisbon. It focusses on AIDA-CMK, through 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 resolution implements 3 techniques to multi-objective multi-constraint optimization, specifically, an evolutionary strategy with NSGAII, a swarm intelligence technique with MOPSO and stochastic hill hiking method with MOSA. furthermore, the carried out constitution permits the simple hybridization among kernels remodeling the former basic NSGAII optimization module right into a extra developed and flexible module assisting a number of unmarried and multi-kernel algorithms. the 3 multi-objective optimization methods have been demonstrated with CEC2009 benchmarks to limited multi-objective optimization and established with genuine analog IC layout difficulties. The completed effects have been in comparison when it comes to functionality, utilizing statistical effects bought from a number of self sufficient runs. ultimately, a few hybrid ways have been additionally experimented, giving a foretaste to a variety 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, as a tie breaker for solutions with the same rank. 2 illustrates this procedure. Elements with lower rank dominate the ones with higher rank. 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.

PSO associates each particle as a candidate solution and lets them explore the search space. This technique is focused on the collective behavior of a distributed population of simple agents that interact locally with each other. Each particle is associated with a stochastic velocity vector which indicates where the particle is moving to. The next move of each particle at a given time, illustrated in Fig. 7, is a stochastic combination of the velocity in the previous time instant, the direction toward the best position ever occupied by the particle, and the direction toward the best swarm positions.

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 structure of classes is described in detail showing their relations and the flexibility of the proposed framework.

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AIDA-CMK: Multi-Algorithm Optimization Kernel Applied to Analog IC Sizing by Ricardo Lourenço, Nuno Lourenço, Nuno Horta


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