Frequency modulation sound parameter identification using novel hybrid evolutionary algorithms

Baghmisheh, Mohammad Taghi Vakil; Ahandani, Morteza Alinia; Talebi, Majid

doi:10.1109/istel.2008.4651273

Cited by 5 publications

(4 citation statements)

References 13 publications

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“…is the best selection for this parameter. A comparison between the authors' obtained results and results reported in Vakil-Baghmisheh et al, (2008) A comparison between the real wave and the estimated wave by SPSO for values of cost function equal to 0.5 and 1.0E-10 are shown in Figures 5 and 6. As this figure shows, for a cost 0.5 some of differences are observable, whereas for the cost value of 1.0E-10 two figures are not distinguishable.…”

Section: Simulation Results and Discussionmentioning

confidence: 92%

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Frequency Modulation Sound Parameter Identification using Shuffled Particle Swarm Optimization

Ahandan

Alavi-Rad

Jafari

2013

International Journal of Applied Evolutionary Computation

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The frequency modulation sound parameter identification is a complex multimodal optimization problem. This problem is modeled in the form of a cost function that is the sum-squared error between the samples of estimated wave and the samples of real wave. In this research, the authors propose a shuffled particle swarm optimization algorithm to solve this problem. In the shuffled particle swam optimization proposed here, population such as shuffled frog leaping algorithm is divided to several memeplexes and each memeplex is improved by the particle swam optimization algorithm. A comparison among the obtained results of the authors' proposed algorithm with the results reported in the literature confirms a better performance of the authors' proposed algorithm.

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Section: Simulation Results and Discussionmentioning

confidence: 92%

“…of the frequency modulated sound model given in Equation 3to approximate the real sound wave given in Equation 4, so as to minimize the sum of squared-error between the samples of measured and the estimated data (Vakil-Baghmisheh et al, 2008).…”

Section: Frequency Modulation Sound Paprameter Identificationmentioning

confidence: 99%

Frequency Modulation Sound Parameter Identification using Shuffled Particle Swarm Optimization

Ahandan

Alavi-Rad

Jafari

2013

International Journal of Applied Evolutionary Computation

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“…So, a value equals to 10 is considered for m in all algorithms. A comparison among the proposed algorithms in this research, and results of different algorithms reported in [28] is presented in Table 6. Based on these results, only the proposed algorithms in this research obtain a success rate of 100 on all threshold values.…”

Section: F(g )mentioning

confidence: 99%

“…Comparison of performance of the proposed algorithms in this research and results reported in[28] on nestedmodulator FM.…”

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confidence: 94%

Quasi-Opposition-Based Learning in a Shuffled Particle Swarm Optimization for Solving Frequency Modulation Sounds Parameter Identification Problem

Ahandani

2023

Preprint

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This paper proposes some novel versions of the shuffled particle swarm optimization (SPSO) for solving the frequency modulation sound parameter identification (FMSPI) problem. In the SPSO, a population is divided into several parallel groups and then each group is independently evolved in an evolutionary process using a particle swarm optimization (PSO). This paper employs two different strategies to prevent a premature convergence and providing a better balance between the exploration and exploitation abilities of the SPSO algorithm. Firstly, it proposes that we can use a separate strategy for the inertia weight factor parameter of each group in each iteration of the SPSO algorithm. For the second strategy to provide a deep search of promising areas, a quasi-opposition-based strategy is inserted in the SPSO. Experimental results on FMSPI problems show that new employed strategies reduction lead to achieving a more effective and robust algorithm so as it can considerably improve the performance of the SPSO.

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Memetic Algorithms

Cotta

Mathieson

Moscato

2018

Handbook of Heuristics

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Frequency modulation sound parameter identification using novel hybrid evolutionary algorithms

Cited by 5 publications

References 13 publications

Frequency Modulation Sound Parameter Identification using Shuffled Particle Swarm Optimization

Frequency Modulation Sound Parameter Identification using Shuffled Particle Swarm Optimization

Quasi-Opposition-Based Learning in a Shuffled Particle Swarm Optimization for Solving Frequency Modulation Sounds Parameter Identification Problem

Memetic Algorithms

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