Polycell placement for analog LSI chip designs by genetic algorithms and tabu search

Handa, Keiichi; Kuga, S.

doi:10.1109/icec.1995.487473

Cited by 15 publications

(6 citation statements)

References 7 publications

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“…Glover et al [9] inceptively designed the scatter search, which integrates GA and TS for a better performance than running GA and TS alone. Handa and Kuga [13] considered the difference between the convergence speeds of GA and TS in the two halves of the search process. They proposed the concatenation of GA and TS that switches these two algorithms to avoid premature convergence.…”

Section: Related Workmentioning

confidence: 99%

Selecting survivors in genetic algorithm using tabu search strategies

Ting

Huang

2009

Memetic Comp.

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Genetic algorithm (GA) is well-known for its effectiveness in global search and optimization. To balance selection pressure and population diversity is an important issue of designing GA. This paper proposes a novel hybridization of GA and tabu search (TS) to address this issue. The proposed method embeds the key elements of TS-tabu restriction and aspiration criterion-into the survival selection operator of GA. More specifically, the tabu restriction is used to prevent inbreeding for diversity maintenance, and the aspiration criterion is activated to provide moderate selection pressure under the tabu restriction. The interaction of tabu restriction and aspiration criterion enables survivor selection to balance selection pressure and population diversity. The experimental results on numerical and combinatorial optimization problems show that this hybridization can significantly improve GAs in terms of solution quality as well as convergence speed. An empirical analysis further identifies the influences of the TS strategies on the performance of this hybrid GA.

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Section: Related Workmentioning

confidence: 99%

Selecting survivors in genetic algorithm using tabu search strategies

Ting

Huang

2009

Memetic Comp.

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“…Sue [18] exploited GA and TS to solve different levels of problems. K. Handa and S. Kuga considered the different convergence speed of GA and TS in the first and second half of the search [19]. Chin proposed that the intensity of TS is gradually increasing as generations [20].…”

Section: Introductionmentioning

confidence: 99%

Hybrid Genetic Algorithm Based Energy Management of Wireless Sensor Networks in Electric Distribution Networks

Huang

Miao

Liu

2007

2007 International Conference on Wireless Communications, Networking and Mobile Computing

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Energy constraint is one of the most demanding challenges in wireless sensor networks. In power systems, a wireless sensor is powered by either a battery or by energy that is harvested from the environment, such as photovoltaic cells, vibration, or an iron core winding which surrounds a line conductor. The average power that is available for activities is very limited. By analyzing the working model of sensor nodes in power electric system, an energy consumption model is constructed and a hybrid genetic algorithm is used to optimize the energy consumption in this paper.

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“…The solution space of the problem will increase exponentially with the growth of circuits scale, thus it is impossible to find the optimal solution by exploring the global solution space. Recently, many researchers resort to stochastic optimization algorithms, such as simulated annealing (Ho et al 2004), artificial neural networks (Gloria et al 1994) and tabu search (Handa and Kuga 1995). Genetic algorithm (GA) has been proved to be an effective method for tackling NP-hard optimization problem (Goldberg 1989), and has been successfully applied on VLSI floorplanning problems (Gwee and Lin 1999;Tang and Sebastian 2005;Tang and Yao 2007;Valenzuela and Wang 2002).…”

Section: Introductionmentioning

confidence: 99%

A PSO-based intelligent decision algorithm for VLSI floorplanning

2009

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Floorplanning is an important issue in the very large-scale integrated (VLSI) circuit design automation as it determines the performance, size, yield and reliability of VLSI chips. This paper proposes a novel intelligent decision algorithm based on the particle swarm optimization (PSO) technique to obtain a feasible floorplanning in VLSI circuit physical placement. The PSO was applied with integer coding based on module number and a new recommended value of acceleration coefficients for optimal placement solution. Inspired by the physics of genetic algorithm (GA), the principles of mutation and crossover operator in GA are incorporated into the proposed PSO algorithm to make this algorithm to break away from local optima and achieve a better diversity. Experiments employing MCNC and GSRC benchmarks show that the proposed algorithm is effective. The proposed algorithm can avoid local minimum and performs well in convergence. The experimental results of the proposed method in this paper can also greatly help floorplanning decision making in VLSI circuit design automation.

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Polycell placement for analog LSI chip designs by genetic algorithms and tabu search

Cited by 15 publications

References 7 publications

Selecting survivors in genetic algorithm using tabu search strategies

Selecting survivors in genetic algorithm using tabu search strategies

Hybrid Genetic Algorithm Based Energy Management of Wireless Sensor Networks in Electric Distribution Networks

A PSO-based intelligent decision algorithm for VLSI floorplanning

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