DE/EDA: A new evolutionary algorithm for global optimization

Sun, Jianyong; Zhang, Qingfu; Tsang, Edward

doi:10.1016/j.ins.2004.06.009

Cited by 289 publications

(107 citation statements)

References 7 publications

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“…For all experiments, unless mentioned otherwise, the utilized settings for the parameters are given by -F = 0.5 (as in [1,5,15,26,35,42]) -C r = 0.9 (as in [1,5,15,26,35,42]) -mutation strategy: DE/rand/1/bin (classic DE) (as in [5,21,24,26,35,36]…”

Section: Methodsmentioning

confidence: 99%

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Self-adaptive randomized and rank-based differential evolution for multimodal problems

Urfalıoḡlu

Arıkan

2011

J Glob Optim

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Differential Evolution (DE) is a widely used successful evolutionary algorithm (EA) based on a population of individuals, which is especially well suited to solve problems that have non-linear, multimodal cost functions. However, for a given population, the set of possible new populations is finite and a true subset of the cost function domain. Furthermore, the update formula of DE does not use any information about the fitness of the population. This paper presents a novel extension of DE called Randomized and Rank-based Differential Evolution (R2DE) and its self-adaptive version SAR2DE to improve robustness and global convergence speed on multimodal problems by introducing two multiplicative terms in the DE update formula. The first term is based on a random variate of a Cauchy distribution, which leads to a randomization. The second term is based on ranking of individuals, so that R2DE exploits additional information provided by the population fitness. In extensive experiments conducted with a wide range of complexity settings, we show that the proposed heuristics lead to an overall improvement in robustness and speed of convergence compared to several global optimization techniques, including DE, Opposition based Differential Evolution (ODE), DE with Random Scale Factor (DERSF) and the self-adaptive Cauchy distribution based DE (NSDE).

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Section: Methodsmentioning

confidence: 99%

“…Other methods for improving DE are based on hybridization. In [36], Sun et al propose a hybrid algorithm using an estimation of distribution method. This method is based on a probability model to generate additional solution candidates.…”

Section: Introductionmentioning

confidence: 99%

Self-adaptive randomized and rank-based differential evolution for multimodal problems

Urfalıoḡlu

Arıkan

2011

J Glob Optim

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“…Since EMNA is proposed earlier, and the sampling method RE-CEDA used is a common technique in random variate generation, RECEDA can be seen as a specific implementation of EMNA; EGNA, A class of algorithms ( [9], [1], and [10]) based on learning and simulation of Gaussian network. The conditional variance used in sampling is computed by equations (2.35)-(2.40), pp.49 in [1], which can also be found as equations (13)- (15) in [10]; IDEA, a framework that different pdf (probabilistic density function) can be used within ( [11], [12], and [6]). If normal pdf is used, sample mean and covariance matrix are estimated in the same way as in EMNA global .…”

Section: A Gaussian Based Edas Using Full Covariance Matrixmentioning

confidence: 99%

“…Such EDAs include: SHCLVND [13], PBIL c [14], UMDA G c [1] and EDA+DE [15]. Their common characteristic is only diagonal covariance matrix is used to express a univariate marginal Gaussian model.…”

Section: B Gaussian Based Edas Using Diagonal Covariance Matrixmentioning

confidence: 99%

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Covariance matrix repairing in Gaussian based EDAs

Dong

Yao²

2007

2007 IEEE Congress on Evolutionary Computation

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Abstract-Gaussian models are widely adopted in continuous Estimation of Distribution Algorithms (EDAs). In this paper, we analyze continuous EDAs and show that they don't always work because of computation error: covariance matrix of Gaussian model can be ill-posed and Gaussian based EDAs using full covariance matrix will fail under specific conditions. It is a universal problem that all existing Gaussian based EDAs using full covariance matrix suffer from. Through theoretical analysis with examples of simulated data and experiments, we show that the ill-posed covariance matrix strongly affects those EDAs. This paper proposes a Covariance Matrix Repairing (CMR) method to fix ill-posed covariance matrix. CMR significantly improves the robustness of EDAs. Even some EDA's performance that was previously thought inefficient can be improved surprisingly with the help of CMR. CMR can also guarantee those EDAs to be used with small scale of population (but still should be large enough to find the global optimum) to accelerate the convergence rate while maintaining the quality of solutions.

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Gaussian bare‐bones gradient‐based optimization: Towards mitigating the performance concerns

Qiao

Shan

Jiang

et al. 2021

Int J of Intelligent Sys

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Gradient‐based optimizer (GBO) is a metaphor‐free mathematic‐based algorithm proposed in recent years. Encouraged by the gradient‐based Newton's method, this algorithm combines with population‐based evolutionary methods. The disadvantage of the traditional GBO algorithm is that the global search ability of the algorithm is too strong, and the local search ability is too weak; accordingly, it is difficult to obtain the global optimal solution efficiently. Therefore, a new improved GBO algorithm (GOMGBO) is developed to mitigate such performance concerns by introducing a Gaussian bare‐bones mechanism, an opposition‐based learning mechanism, and a moth spiral mechanism enhanced GBO algorithm. The proposed GOMGBO has been compared against many famous methods and improved variants on 30 benchmark functions. The experimental results show that GOMGBO has apparent advantages in convergence speed and precision. In addition, this paper analyzes the balance and diversity of the GOMGBO algorithm and compares GOMGBO with other algorithms on several engineering problems. The experimental results show that the GOMGBO algorithm is also better than the competitive algorithm in engineering problems. This study uses the GOMGBO algorithm to optimize kernel extreme learning machine (KELM), and a new GOMGBO‐KELM model is proposed. The model is used to deal with four clinical disease diagnosis problems. Compared with GBO‐KELM, back propagation neural network algorithm, and other models, comparative experiments show that GOMGBO‐KELM has high performance in dealing with practical cases. We invite the community to investigate further our method for solving problems more efficiently with reasonable speed and efficiency. Readers of this study can refer to https://aliasgharheidari.com for any guidance about the proposed GOMGBO method.

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DE/EDA: A new evolutionary algorithm for global optimization

Cited by 289 publications

References 7 publications

Self-adaptive randomized and rank-based differential evolution for multimodal problems

Self-adaptive randomized and rank-based differential evolution for multimodal problems

Covariance matrix repairing in Gaussian based EDAs

Gaussian bare‐bones gradient‐based optimization: Towards mitigating the performance concerns

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