Enhancing Gaussian Estimation of Distribution Algorithm by Exploiting Evolution Direction With Archive

Liang, Yongsheng; Ren, Zhigang; Yao, Xianghua; Feng, Zuren; An, Chen; Guo, Wenhua

doi:10.1109/tcyb.2018.2869567

Cited by 36 publications

(17 citation statements)

References 48 publications

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“…During the optimization process, most EAs generate the offspring mainly based on the current population. Historical solutions generated in the previous generation are usually abandoned [46]. However, these solutions may be useful for improving the convergence performance.…”

Section: Learner Phase With Cooperative Learningmentioning

confidence: 99%

Teaching-Learning-Based Optimization Enhanced With Multiobjective Sorting Based and Cooperative Learning

Fan

2020

IEEE Access

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Teaching-learning-based optimization (TLBO) algorithm has been shown to be an effective optimization algorithm. However, it is easily trapped into local optima when the global optimal solution of the function to be optimized is at the original dot or around the original dot. This paper presents a novel TLBO variant by incorporating multiobjective sorting-based mechanism and cooperative learning strategy to alleviate this problem. Taking advantages of multiobjective optimization in maintaining good population diversity, several teachers are selected based on non-dominated sorting, so as to guide learners to learn more effectively. In addition, the proposed algorithm adopts cooperative learning, including learning within and between groups, to improve the search ability of the algorithm. Experimental and statistical analyses are performed on CEC2014 benchmark functions. The experimental results demonstrate the effectiveness of the proposed algorithm in comparison with other variants of TLBO and other state-of-the-art optimization algorithms.

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Section: Learner Phase With Cooperative Learningmentioning

confidence: 99%

Teaching-Learning-Based Optimization Enhanced With Multiobjective Sorting Based and Cooperative Learning

Fan

2020

IEEE Access

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“…In RW-GEDA, we set a criterion judging the stagnation of the population, i.e., if the mean fitness value of the first half of the promising solutions remains unchanged, the algorithm is regarded as having stagnated, and the random walk strategy i is updated using Gaussian random walk or Lévy walk randomly by (16) or (18), otherwise,…”

Section: B Mathematical Presentation Of Rw-gedamentioning

confidence: 99%

“…In the first group, five other state-of-theart algorithms from different communities are incorporated to make comparisons, including L-SHADE [46], AAVS-EDA [17], VCS [35], COA [34] and BLPSO5 [47]. In the second group, five promising GEDAs are utilized as competitors, i.e., EMNA g [1], AMaLGaM [14], IPOP-CMAES [23], EDA 2 [16] and ISR-EDA [19]. Finally, we added a third comparison to reveal the efficiency of the different components in our modification.…”

Section: Numerical Experiments Using Cec 2014 Benchmarksmentioning

confidence: 99%

“…In recent work, Ren et al [15] proposed using an improved anticipated mean shift (AMS) technique to shift the mean value of the selected solutions to efficiently adjust the search scope and the direction. Another GEDA variants explored by them is EDA 2 [16], using an archive that can save more promising solutions to adjust the evolution direction. Their recent research [17] also proposed a novel variances adjustment technique, which has the advantage of tuning the variances and main search direction of GEDA simultaneously.…”

Section: Introductionmentioning

confidence: 99%

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A Gaussian Estimation of Distribution Algorithm With Random Walk Strategies and Its Application in Optimal Missile Guidance Handover for Multi-UCAV in Over-the-Horizon Air Combat

et al. 2019

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To overcome the premature convergence caused by the ill-distribution of solutions in the basic Gaussian estimation of distribution algorithm (GEDA), this paper explores a novel GEDA variant with random walk strategies, namely RW-GEDA. In RW-GEDA, the weighted maximum likelihood estimation method is used to estimate the Gaussian distribution. The new candidates are sampled using a shifted mean to enhance exploration performance. When the algorithm stagnates, two random walk strategies, namely, Gaussian random walk and Lévy walk, are activated to enrich the population diversity. Moreover, RW-GEDA is executed in an Eigen coordinate framework to promote the evolution towards the dominant region. The performance of RW-GEDA is evaluated by using the CEC 2014 test suite and compared with other top algorithms from different communities as well as promising GEDA extensions. The statistical results demonstrate the competitive performance of our proposed RW-GEDA in terms of efficiency and accuracy. In addition, RW-GEDA is applied to solve the optimal missile guidance handover problem. To fill the gap in solving this problem, a novel missile guidance advantage model is established, and the optimal missile guidance handover is determined by optimizing the control variables of unmanned combat aerial vehicles. The validity and practicability of the problem model as well as the accuracy and efficiency of RW-GEDA are demonstrated by the experimental results. INDEX TERMS Gaussian estimation of distribution algorithm, CEC 2014, numerical optimization, air combat, UCAV.

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“…Moreover, they explored a novel AVS strategy with anisotropic adjustment based on the local fitness landscape [18]. Their other work was EDA 2 [24], in which an archive is adopted to store more promising solutions to revise the distribution scope. Other representative EDA modifications of this type are the covariance matrix adaptation evolution strategy (CMA-ES) [25] and its variants.…”

Section: Introductionmentioning

confidence: 99%

An Estimation of Distribution Algorithm With Multi-Leader Search

2020

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The estimation of distribution algorithm (EDA) is a well-known stochastic search method but is easily affected by the ill-shaped distribution of solutions and can thus become trapped in stagnation. In this paper, we propose a novel modified EDA with a multi-leader search (MLS) mechanism, namely, the MLS-EDA. To strengthen the exploration performance, an enhanced distribution model that considers the information of population and distribution is utilized to generate new candidates. Moreover, when the algorithm stagnates, the MLS mechanism will be activated to perform a local search and shrink the search scope. The performance of the MLS-EDA in addressing complex optimization problems is verified using the CEC 2014 and CEC 2017 testbeds with 30D, 50D and 100D tests. Several modern algorithms, including the top-performing methods in the CEC 2014 and CEC 2017 competitions, are considered as competitors. The competitive performance of our proposed MLS-EDA is discussed based on the comparison results.

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Enhancing Gaussian Estimation of Distribution Algorithm by Exploiting Evolution Direction With Archive

Cited by 36 publications

References 48 publications

Teaching-Learning-Based Optimization Enhanced With Multiobjective Sorting Based and Cooperative Learning

Teaching-Learning-Based Optimization Enhanced With Multiobjective Sorting Based and Cooperative Learning

A Gaussian Estimation of Distribution Algorithm With Random Walk Strategies and Its Application in Optimal Missile Guidance Handover for Multi-UCAV in Over-the-Horizon Air Combat

An Estimation of Distribution Algorithm With Multi-Leader Search

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