Determination of the Critical Slip Surface Using Artificial Fish Swarms Algorithm

Cheng, Yung Ming; Liang, Lu; C., S.; Wei, W. B.

doi:10.1061/(asce)1090-0241(2008)134:2(244)

Cited by 69 publications

(28 citation statements)

References 19 publications

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“…Nevertheless, the boundaries between the soil layers in this problem are not horizontal. The problem was analyzed by Zolfaghari et al (2005) and Cheng et al (2008). Zolfaghari et al(2005) and Cheng et al (2008) The soil and ACO parameters used in this study can again be found in Table 4.22 and Table 4.23.…”

Section: Examplementioning

confidence: 99%

“…The problem was analyzed by Zolfaghari et al (2005) and Cheng et al (2008). Zolfaghari et al(2005) and Cheng et al (2008) The soil and ACO parameters used in this study can again be found in Table 4.22 and Table 4.23. In the analysis, the total search domain is dived into 150 slices, each with the width of 0.25 m. The ranges of choices for the search parametersx , 1 α , and i α Δ can be found in Table 4.24.…”

Section: Examplementioning

confidence: 99%

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Determination of the critical failure surface for slope stability analysis using ant colony optimization

Kahatadeniya

Nanakorn

Neaupane

2009

Engineering Geology

101

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Section: Examplementioning

confidence: 99%

Section: Examplementioning

confidence: 99%

Determination of the critical failure surface for slope stability analysis using ant colony optimization

Kahatadeniya

Nanakorn

Neaupane

2009

Engineering Geology

101

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“…It is good at avoiding the local optimum and searching for the global optimum owing to its adaptive capacity in the parallel search of solution space through simulating these behaviors in nature [27][28][29]. In this section, the general AFSA is discussed below.…”

Section: The Basic Principles Of Afsamentioning

confidence: 99%

“…As a kind of swarm intelligence methods, AFSA is selected here for its significant ability to search for the global optimal value and to adapt its searching space automatically [28,29]. And its basic motivation is to find the global optimum by simulating the fish's behaviors, such as preying, swarming, and searching.…”

Section: Introductionmentioning

confidence: 99%

A complexity-performance-balanced multiuser detector based on artificial fish swarm algorithm for DS-UWB systems in the AWGN and multipath environments

Yin

Zong

Sun

et al. 2012

EURASIP J. Adv. Signal Process.

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In this article, an efficient multiuser detector based on the artificial fish swarm algorithm (AFSA-MUD) is proposed and investigated for direct-sequence ultrawideband systems under different channels: the additive white Gaussian noise channel and the IEEE 802.15.3a multipath channel. From the literature review, the issues that the computational complexity of classical optimum multiuser detection (OMD) rises exponentially with the number of users and the bit error rate (BER) performance of other sub-optimal multiuser detectors is not satisfactory, still need to be solved. This proposed method can make a good tradeoff between complexity and performance through the various behaviors of artificial fishes in the simplified Euclidean solution space, which is constructed by the solutions of some sub-optimal multiuser detectors. Here, these sub-optimal detectors are minimum mean square error detector, decorrelating detector, and successive interference cancellation detector. As a result of this novel scheme, the convergence speed of AFSA-MUD is greatly accelerated and the number of iterations is also significantly reduced. The experimental results demonstrate that the BER performance and the near-far effect resistance of this proposed algorithm are quite close to those of OMD, while its computational complexity is much lower than the traditional OMD. Moreover, as the number of active users increases, the BER performance of AFSA-MUD is almost the same as that of OMD.

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“…For slope stability problems, McCombie and Wilkinson (2002), Zolfaghari et al (2005), Cheng et al (2007b) and Jianping et al (2008) have adopted the genetic algorithm; Bolton et al (2003) have used the leap-frog optimization technique; Cheng et al (2008b) and Kahatadeniya et al (2009) have also applied the ant-colony method; and the simulated annealing method, PSO (particle swarm optimization), HS (harmony search), Tabu search and fish swarm methods are first adopted by Cheng (2003), Cheng et al (2007aCheng et al ( , b, 2008b for slope stability analysis. These methods are devised for large scale problems with the presence of multiple local minima.…”

mentioning

confidence: 99%

A coupled particle swarm and harmony search optimization algorithm for difficult geotechnical problems

Cheng

Sun

et al. 2011

Struct Multidisc Optim

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Many solutions in geotechnical problems are the result of optimization analysis. There are many practical engineering problems where the objective function is nonconvex, discontinuous with the presence of multiple strong local minima, and the classical optimization methods may sometime be trapped by the local minimum during the analysis. In this paper, a coupled optimization method is proposed for such difficult cases. The mixed optimization algorithm can takes the advantage of different algorithms, and the proposed algorithm is demonstrated to be effective and efficient in solving a very complicated hydropower problem with a high level of confidence. The proposed method can further be applied to different kinds of difficult engineering problems.

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Determination of the Critical Slip Surface Using Artificial Fish Swarms Algorithm

Cited by 69 publications

References 19 publications

Determination of the critical failure surface for slope stability analysis using ant colony optimization

Determination of the critical failure surface for slope stability analysis using ant colony optimization

A complexity-performance-balanced multiuser detector based on artificial fish swarm algorithm for DS-UWB systems in the AWGN and multipath environments

A coupled particle swarm and harmony search optimization algorithm for difficult geotechnical problems

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