An Efficient and Effective Algorithm for Large Scale Global Optimization Problems

Kan-chao, Lian; Peng, Xuyu; Ouyang, Aijia

doi:10.1142/s0218001415590065

Cited by 4 publications

(1 citation statement)

References 52 publications

(40 reference statements)

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“…In addition, taking advantage of the invasive weed optimization algorithm [42] and the quantum-behaved PSO algorithm [43], Lian et al [44] developed a quantum-behaved invasive weed optimization algorithm, which correspondingly adjusts and improves the quantum models of these two algorithms.…”

Section: B Novel Learning or Updating Strategies For Easmentioning

confidence: 99%

A Level-Based Learning Swarm Optimizer for Large-Scale Optimization

Yang

Chen

Deng

et al. 2018

IEEE Trans. Evol. Computat.

261

126

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In pedagogy, teachers usually separate mixed-level students into different levels, treat them differently and teach them in accordance with their cognitive and learning abilities. Inspired from this idea, we consider particles in the swarm as mixed-level students and propose a level-based learning swarm optimizer (LLSO) to settle large-scale optimization, which is still considerably challenging in evolutionary computation. At first, a level-based learning strategy is introduced, which separates particles into a number of levels according to their fitness values and treats particles in different levels differently. Then, a new exemplar selection strategy is designed to randomly select two predominant particles from two different higher levels in the current swarm to guide the learning of particles. The cooperation between these two strategies could afford great diversity enhancement for the optimizer. Further, the exploration and exploitation abilities of the optimizer are analyzed both theoretically and empirically in comparison with two popular particle swarm optimizers. Extensive comparisons with several state-of-the-art algorithms on two widely used sets of large-scale benchmark functions confirm the competitive performance of the proposed optimizer in both solution quality and computational efficiency. Finally, comparison experiments on problems with dimensionality increasing from 200 to 2000 further substantiate the good scalability of the developed optimizer.

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Section: B Novel Learning or Updating Strategies For Easmentioning

confidence: 99%

A Level-Based Learning Swarm Optimizer for Large-Scale Optimization

Yang

Chen

Deng

et al. 2018

IEEE Trans. Evol. Computat.

261

126

View full text Add to dashboard Cite

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The Application of HIWO–SVM in Analog Circuit Fault Diagnosis

Tian

Guo

et al. 2015

Int. J. Patt. Recogn. Artif. Intell.

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The paper proposes a fault diagnosis model based on the HIWO–SVM algorithm given the fact that the basic support vector machines (SVM) cannot solve effectively the problem of fault diagnosis in analog circuit. First of all, the wavelet package technique is adopted for extracting the information of the faults from the test points in the analog circuit. The differential evolution (DE) algorithm is then integrated with the purpose of improving the performance of the basic IWO algorithm, i.e. a hybrid IWO (HIWO) algorithm. The HIWO algorithm is further used to optimize the parameters of SVM in order to avoid the randomness of the parameter selection, thereby improving the diagnosis precision and robustness. The experimental results on a filter circuit show that the method is more effective and reliable than the other methods for fault diagnosis.

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An Efficient Hybrid Algorithm Based on HS and SFLA

Ouyang

Peng

Liu

et al. 2016

Int. J. Patt. Recogn. Artif. Intell.

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When used for optimizing complex functions, harmony search (HS) and shuffled frog leaping algorithm (SFLA) algorithm tend to easily get trapped into local optima and result in low convergence precision. To overcome such shortcomings, a hybrid mechanism of selective search by combining HS algorithm and SFLA algorithm is as well proposed. An HS-SFLA algorithm is designed by taking the advantages of HS and SFLA algorithms. The hybrid algorithm of HS-SFLA is adopted for dealing with complex function optimization problems, the experimental results show that HS-SFLA outperforms other state-of-the-art intelligence algorithms significantly in terms of global search ability, convergence speed and robustness on 80% of the benchmark functions tested. The HS-SFLA algorithm could directly be applied to all kinds of continuous optimization problems in the real world.

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An Efficient and Effective Algorithm for Large Scale Global Optimization Problems

Cited by 4 publications

References 52 publications

A Level-Based Learning Swarm Optimizer for Large-Scale Optimization

A Level-Based Learning Swarm Optimizer for Large-Scale Optimization

The Application of HIWO–SVM in Analog Circuit Fault Diagnosis

An Efficient Hybrid Algorithm Based on HS and SFLA

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