Adaptive Learning Rate Elitism Estimation of Distribution Algorithm Combining Chaos Perturbation for Large Scale Optimization

Xu, Qingyang; Zhang, Chengjin; Sun, Jie; Zhang, Li

doi:10.2174/1874110x01610010020

Cited by 3 publications

(2 citation statements)

References 40 publications

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“…Qingyang et al [24] worked on an adaptive learning rate elitism Estimation of Distribution Algorithm (EDA), a kind of Evolutionary Algorithm, combining chaos perturbation (ALREEDA) to improve the performance of traditional EDA to solve high dimensional optimization problems. Chen-Yang et al [25] focused on a hybrid algorithm called Hybrid Algorithm-Ant Colony Algorithm Genetic Algorithm (HA-ACAGA) to find optimal solutions for users on dynamic web service composition where user's personal preference is different and web services are massive and dynamic; they used a function to control individuals and a function to update pheromones.…”

Section: Adaptation and Self-adaptationmentioning

confidence: 99%

Brief Review of Techniques Used to Develop Adaptive Evolutionary Algorithms

Bonilla-Vera¹,

Mora-Vargas²,

González-Mendoza³

et al. 2017

Open Cyber

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This paper presents a brief review of techniques used to allow evolutionary algorithms to adapt to optimization problems in dynamic environments, through exploration of the control parameters of genetic algorithms as well as genotypic interpreters. A description of some of the most used evolutionary techniques is included, with major emphasis on genetic algorithms and their relationship with the problem of adaptation to the environment. The article also discusses state used models to tackle these kinds of problems, including self-adaptation and genotype-phenotype mapping.

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Section: Adaptation and Self-adaptationmentioning

confidence: 99%

Brief Review of Techniques Used to Develop Adaptive Evolutionary Algorithms

Bonilla-Vera¹,

Mora-Vargas²,

González-Mendoza³

et al. 2017

Open Cyber

View full text Add to dashboard Cite

show abstract

“…The method retains the advantages of the genetic algorithm, and can purposefully and selectively use the relevant knowledge and information features in the problem to be solved to suppress the degradation phenomenon caused by some factors in the calculation process [19]. More and more scholars have begun to pay attention to the research, combining the algorithm with the immune algorithm [20], evolutionary algorithm [21], particle swarm optimization algorithm [22], and others [23], to improve the optimization performance of the algorithms. Hong et al [24] studied the convergence speed of the general artificial immune algorithm using random process theory instead of the eigenvalue estimation of traditional state transition matrix.…”

Section: Introductionmentioning

confidence: 99%

Investigation on Energy-Effectiveness Enhancement of Medium-Frequency Induction Furnace Based on an Adaptive Chaos Immune Optimization Algorithm with Mutative Scale

et al. 2022

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Based on the chaos algorithm and immune algorithm theory, an adaptive chaotic immune optimization algorithm (ACIOA) with a mutative scale was proposed and subsequently validated by the experiment result in this paper, and then the adaptive chaotic immune optimization algorithm with mutative scale was applied to investigate the performance characteristics of the medium-frequency induction furnace. The obtained results include the effects on the performance characteristics of a medium-frequency induction furnace of the diameter of the heated cylindrical material, the thickness of the crucible wall, the fullness degree of the induction coil, the ratio of diameter to current penetration depth, and the power frequency. The results showed that the optimization algorithm could continuously modify the variable search space and take the optimal number of cycles as the control index to carry out the search. In addition, the suitable ratio of diameter to current penetration depth was between 3.5 and 6.0, and was beneficial to the improvements of in power factor and thermal efficiency. This method had the characteristics of small calculation delay, high anti-noise ability, and high detection rate. Moreover, the maximum errors of KFF, Random, and PSO were 6.4%, 6.2%, and 5.4%, respectively. The improved method had good estimation accuracy and an excellent global optimization. Meanwhile, the suitable ratio of diameter to current penetration depth, the thickness of crucible wall, and power frequency were beneficial to the improvements in power factor and thermal efficiency. Thus, the finding is helpful as a guide to determining the design of a medium-frequency induction furnace, which may be of interest for improvements in performance under different operating conditions.

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EDA based Deep Neural Network Parameter Optimization

Liu

2019

Proceedings of the 3rd International Conference on Computer Science and Application Engineering

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Adaptive Learning Rate Elitism Estimation of Distribution Algorithm Combining Chaos Perturbation for Large Scale Optimization

Cited by 3 publications

References 40 publications

Brief Review of Techniques Used to Develop Adaptive Evolutionary Algorithms

Brief Review of Techniques Used to Develop Adaptive Evolutionary Algorithms

Investigation on Energy-Effectiveness Enhancement of Medium-Frequency Induction Furnace Based on an Adaptive Chaos Immune Optimization Algorithm with Mutative Scale

EDA based Deep Neural Network Parameter Optimization

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