Adaptive multiple-elites-guided composite differential evolution algorithm with a shift mechanism

Cui, Laizhong; Li, Genghui; Zhu, Zexuan; Lin, Qiuzhen; Wong, Ka-Chun; Lu, Nan; Lü, Jian

doi:10.1016/j.ins.2017.09.002

Cited by 97 publications

(36 citation statements)

References 37 publications

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“…Additionally, in the ALS function there is a special parameter , which is the standard deviation (std) of the normal distribution that generates the scaling factor used in Equation (14). is designed to be adaptive to the current state of the search by DE.…”

Section: Combining Als With Dementioning

confidence: 99%

“…• In relation to parameter : This parameter is used in Equation (14). In DEALS, the parameter is calculated by creating a random number following a uniform distribution in the range (0, 1) (rand (0, 1)), while in MFDEALS is generated using a Normal distribution with 0 as the mean and as the std.…”

Section: Combining Als With Dementioning

confidence: 99%

“…However, DE also encounters two problems in some practical applications: slow convergence speed and stagnation in local optima [9]. To overcome these problems, a number of adaptive DE algorithms have been developed by adapting mutation strategies and control parameters in the optimization processes [10][11][12][13][14][15]. An alternative way of improving the performance of DE is to merge it with LS for enhancing the exploitation capability, leading to memetic DE.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Novel Memetic Framework for Enhancing Differential Evolution Algorithms via Combination With Alopex Local Search

León¹,

Xiong²,

Molina³

et al. 2019

IJCIS

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Differential evolution (DE) represents a class of population-based optimization techniques that uses differences of vectors to search for optimal solutions in the search space. However, promising solutions/regions are not adequately exploited by a traditional DE algorithm. Memetic computing has been popular in recent years to enhance the exploitation of global algorithms via incorporation of local search. This paper proposes a new memetic framework to enhance DE algorithms using Alopex Local Search (MFDEALS). The novelty of the proposed MFDEALS framework lies in that the behavior of exploitation (by Alopex local search) can be controlled based on the DE global exploration status (population diversity and search stage). Additionally, an adaptive parameter inside the Alopex local search enables smooth transition of its behavior from exploratory to exploitative during the search process. A study of the important components of MFDEALS shows that there is a synergy between them. MFDEALS has been integrated with both the canonical DE method and the adaptive DE algorithm L-SHADE, leading to the MDEALS and ML-SHADEALS algorithms, respectively. Both algorithms were tested on the benchmark functions from the IEEE CEC'2014 Conference. The experiment results show that Memetic Differential Evolution with Alopex Local Search (MDEALS) not only improves the original DE algorithm but also outperforms other memetic DE algorithms by obtaining better quality solutions. Further, the comparison between ML-SHADEALS and L-SHADE demonstrates that applying the MFDEALS framework with Alopex local search can significantly enhance the performance of L-SHADE.

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Section: Combining Als With Dementioning

confidence: 99%

Section: Combining Als With Dementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Novel Memetic Framework for Enhancing Differential Evolution Algorithms via Combination With Alopex Local Search

León¹,

Xiong²,

Molina³

et al. 2019

IJCIS

View full text Add to dashboard Cite

show abstract

“…On the contrary, a smaller CR can improve the local exploitation ability and convergence at the later stage of evolution [19]. In this regard, different adaptive mechanisms eliminate the need for manual tuning of control parameters, which are adjusted adaptively using the feedback from the evolution process [20,21].…”

Section: Introductionmentioning

confidence: 99%

Passive Target Localization Problem Based on Improved Hybrid Adaptive Differential Evolution and Nelder-Mead Algorithm

2020

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This paper considers a passive target localization problem in Wireless Sensor Networks (WSNs) using the noisy time of arrival (TOA) measurements, obtained from multiple receivers and a single transmitter. The objective function is formulated as a maximum likelihood (ML) estimation problem under the Gaussian noise assumption. Consequently, the objective function of the ML estimator is a highly nonlinear and nonconvex function, where conventional optimization methods are not suitable for this type of problem. Hence, an improved algorithm based on the hybridization of an adaptive differential evolution (ADE) and Nelder-Mead (NM) algorithms, named HADENM, is proposed to find the estimated position of a passive target. In this paper, the control parameters of the ADE algorithm are adaptively updated during the evolution process. In addition, an adaptive adjustment parameter is designed to provide a balance between the global exploration and the local exploitation abilities. Furthermore, the exploitation is strengthened using the NM method by improving the accuracy of the best solution obtained from the ADE algorithm. Statistical analysis has been conducted, to evaluate the benefits of the proposed modifications on the optimization performance of the HADENM algorithm. The comparison results between HADENM algorithm and its versions indicate that the modifications proposed in this paper can improve the overall optimization performance. Furthermore, the simulation shows that the proposed HADENM algorithm can attain the Cramer-Rao lower bound (CRLB) and outperforms the constrained weighted least squares (CWLS) and differential evolution (DE) algorithms. The obtained results demonstrate the high accuracy and robustness of the proposed algorithm for solving the passive target localization problem for a wide range of measurement noise levels.

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“…YEH et al [12] proposed the mixed mutation strategy of Gauss mutation and the "DE/best/1". Cui et al [13] proposed an adaptive multiple-elites-guided composite differential evolution algorithm with a shift mechanism (AMECoDEs). Wu et al [14] focused on the high-level ensemble of different DE variants and proposed a new algorithm named EDEV.…”

Section: Introductionmentioning

confidence: 99%

Hybridizing Dragonfly Algorithm with Differential Evolution for Global Optimization

Duan

Yang

et al. 2019

IEICE Trans. Inf. & Syst.

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Due to its simplicity and efficiency, differential evolution (DE) has gained the interest of researchers from various fields for solving global optimization problems. However, it is prone to premature convergence at local minima. To overcome this drawback, a novel hybrid dragonfly algorithm with differential evolution (Hybrid DA-DE) for solving global optimization problems is proposed. Firstly, a novel mutation operator is introduced based on the dragonfly algorithm (DA). Secondly, the scaling factor (F) is adjusted in a self-adaptive and individual-dependent way without extra parameters. The proposed algorithm combines the exploitation capability of DE and exploration capability of DA to achieve optimal global solutions. The effectiveness of this algorithm is evaluated using 30 classical benchmark functions with sixteen state-of-the-art meta-heuristic algorithms. A series of experimental results show that Hybrid DA-DE outperforms other algorithms significantly. Meanwhile, Hybrid DA-DE has the best adaptability to high-dimensional problems.

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Adaptive multiple-elites-guided composite differential evolution algorithm with a shift mechanism

Cited by 97 publications

References 37 publications

A Novel Memetic Framework for Enhancing Differential Evolution Algorithms via Combination With Alopex Local Search

A Novel Memetic Framework for Enhancing Differential Evolution Algorithms via Combination With Alopex Local Search

Passive Target Localization Problem Based on Improved Hybrid Adaptive Differential Evolution and Nelder-Mead Algorithm

Hybridizing Dragonfly Algorithm with Differential Evolution for Global Optimization

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