A design automation system for CMOS analog integrated circuits using New Hybrid Shuffled Frog Leaping Algorithm

Jafari, Ali; Bijami, Ehsan; Bana, Hasan Rekabi; Sadri, Saeed

doi:10.1016/j.mejo.2012.05.010

Cited by 19 publications

(4 citation statements)

References 27 publications

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“…SFLA simulate the group behavior of frog, thoughts pass in the cluster, in order to achieve the global information exchange [7]. The individual in the group have the same structure, according to the idea of the differences are divided into different clusters, the individual of each cluster have the same thoughts, each frog represents a solution, Set the local extreme value is b F and global extreme value is g F , the individuals are close to global and local extreme value in the cluster, the thought kept passing between intra and inter cluster, continue to the convergence condition is satisfied.…”

Section: Shuffled Frog Leaping Algorithmmentioning

confidence: 99%

“…Shuffled frog leaping algorithm (SFLA) is proposed by Eusuff and Lansey in 2003, it is a kind of intelligent optimization algorithm, by simulating the characteristics of information communication in the process of frog foraging to solve combinatorial optimization problems [1][2][3]. The SFLA algorithm is simple in principle, less parameters, easy realization and strong capability of global search.…”

Section: Introductionmentioning

confidence: 99%

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A Hybrid Intelligent Optimization Algorithm of Fast Convergence

Li¹,

Zhang²

2015

IJHIT

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A hybrid intelligent optimization algorithm based on quantum particle swarm is presented to solve the problem that the local search ability of traditional SFLA is poor and converges very slowly. The particle is quantized and introduced chaos mechanism in the algorithm in order to enhance the global search ability, using the escape strategy, the group is divided into three clusters and mutation operation on the cluster within individuals, not only improves the convergence speed and ensure the performance of the algorithm. Experiments show that the improved algorithm has the characteristics of strong optimization capability and performance is improved greatly in whether comparison of the baseline function or analysis of universal database, compared with the other two algorithms have obvious advantages.

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Section: Shuffled Frog Leaping Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Hybrid Intelligent Optimization Algorithm of Fast Convergence

Li¹,

Zhang²

2015

IJHIT

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“…The close distance between the best and worst positions in each memeplex may decrease the chance of jumping to the best point in the memeplex. Jafari et al (2012) developed a new MSFLA for automated sizing of analog integrated circuits. In that paper, an equation for frog leaping was considered to enhance the local search ability.…”

Section: Modified Sflamentioning

confidence: 99%

“…(8) as an inertia weight could regulate the search operation and enhance local exploration. This factor is assigned to a large value at first (at the initializing phase) and is reduced regularly to find more sophisticated solutions (Li et al 2012a;Huynh (2008); Jafari et al 2012). The new frog leaping formula is as follows:…”

Section: To Avoid Being Trapped Into Local Optima/to Enhance the Locamentioning

confidence: 99%

The role of basic, modified and hybrid shuffled frog leaping algorithm on optimization problems: a review

2014

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Shuffled frog leaping algorithm (SFLA) is a meta-heuristic to handle different large-scale optimization problems. SFLA is a population-based algorithm that combines the advantages of memetic algorithm and particle swarm optimization. This paper compares previous researches on SFLA and its effectiveness, with the most applied optimization algorithms reviewed and analyzed. Based on the literature, many efforts by previous researchers on SFLA denote the next generations of basic SFLA with diverse structures for modified SFLA or hybrid SFLA. As well, an attempt is made to highlight these structures, their enhancements and advantages. Moreover, this paper considers top improvements on SFLA for solving multi-objective optimization problems, enhancing local and global exploration, avoiding being trapped into local optima, declining computational time and improving the quality of the initial population. The measured enhancements in SFLA are based on the statistical results obtained from 89 published papers and by considering the most common and effective modifications done by a large number of researchers. Finally, the quantitative validations address the SFLA as a robust algorithm employed in various applications which outperforms the other optimization algorithms.

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