Enhancing differential evolution with interactive information

Zheng, Liang‐Wen; Liu, Lu; Zhang, Sheng Xin; Zheng, Shao Yong

doi:10.1007/s00500-017-2740-2

Cited by 9 publications

(4 citation statements)

References 45 publications

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“…The first category is to propose new mutation strategies. In [21], a novel interactive information scheme (IIN) was proposed by incorporating a directional vector that is constructed with the ranking information into the mutation operator for providing promising directional information. In [22], an enhanced fitness-adaptive DE (EFADE) with a new triangular mutation scheme was presented, in which three solutions are selected randomly and their difference vectors are used to define the convex combination vector of the triplet.…”

Section: Related Workmentioning

confidence: 99%

“…To further study the effectiveness of DELDG, the comparisons of DELDG with 11 up-to-date DE variants are made. These DE variants include ZEPDE [16], DI-DE [18], IIN-SHADE [21], EFADE [22], AGDE [23], UMS-SHADE [27], MPEDE [28], DMPSADE [71], CoBiDE [72], EBSHADE [73], and FADE [30]. ZEPDE, DI-DE, IIN-SHADE, EFADE, AGDE, UMS-SHADE, MPEDE and FADE have been briefly introduced in Section III.…”

Section: F Comparison With Up-to-date De Variantsmentioning

confidence: 99%

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Enhancing Differential Evolution on Continuous Optimization Problems by Detecting Promising Leaders

Cai

Liu

et al. 2020

IEEE Access

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Section: Related Workmentioning

confidence: 99%

Section: F Comparison With Up-to-date De Variantsmentioning

confidence: 99%

Enhancing Differential Evolution on Continuous Optimization Problems by Detecting Promising Leaders

Cai

Liu

et al. 2020

IEEE Access

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“…In the collective information-powered DE (CIPDE) [34], multiple top best solutions of current population are linearly combined to construct a collective vector, which is used as a terminal point of difference vector to generate the mutant vector. In DE with interactive information scheme (IIN-DE) [35], a new vector is constructed by an interactive information scheme, which is treated as the directional vector for generating the mutant vector.…”

Section: A Developing New Mutation Strategymentioning

confidence: 99%

Differential Evolution With Adaptive Guiding Mechanism Based on Heuristic Rules

et al. 2019

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This paper proposes to resolve the limitation of differential evolution (DE) that the difference between the individuals in search behavior has not yet been utilized effectively for guiding the evolution of the population. An adaptive guiding mechanism (AGM) based on the heuristic rules is thus suggested to make possible, individual-dependent guidance. The AGM mainly comprises three stages: construction, separation, and guidance. In the construction stage, the elite leadership team (ELT ) is established with an adaptive control scheme by using good information of the population. In the separation stage, the ELT is divided into distinct elite groups that are allocated to different individuals based on their search behaviors. In the guidance stage, the leader that is chosen from the respective elite group, as well as the promising directions extracted from the population, are used together to guide the search of each individual. By incorporating AGM into DE, a novel algorithm framework, named DE with AGM (DE-AGM), is proposed to enhance the performance of DE. As a general framework, DE-AGM can be easily and seamlessly applied to most DE variants. The experimental results on 58 benchmark functions have demonstrated the competitive performance of DE-AGM.

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“…A new DE with hybrid linkage crossover was introduced by Cai & Wang (2015) , which extracts and integrates the problem linkage information to be optimized into the crossover phase. A new interactive information scheme (IIN) replaces the best solution in the “current to-best” variant with many best solutions ( Zheng et al, 2018 ), in which using a weighted mixture of ranking information, a steering vector is constructed, and IIN is included into the mutation operator to provide hopeful guiding data. Designing new mutation techniques falls into the first group.…”

Section: Introductionmentioning

confidence: 99%

A hybridizing-enhanced differential evolution for optimization

Ghasemi

Zare

Trojovský

et al. 2023

PeerJ Computer Science

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Differential evolution (DE) belongs to the most usable optimization algorithms, presented in many improved and modern versions in recent years. Generally, the low convergence rate is the main drawback of the DE algorithm. In this article, the gray wolf optimizer (GWO) is used to accelerate the convergence rate and the final optimal results of the DE algorithm. The new resulting algorithm is called Hunting Differential Evolution (HDE). The proposed HDE algorithm deploys the convergence speed of the GWO algorithm as well as the appropriate searching capability of the DE algorithm. Furthermore, by adjusting the crossover rate and mutation probability parameters, this algorithm can be adjusted to pay closer attention to the strengths of each of these two algorithms. The HDE/current-to-rand/1 performed the best on CEC-2019 functions compared to the other eight variants of HDE. HDE/current-to-best/1 is also chosen as having superior performance to other proposed HDE compared to seven improved algorithms on CEC-2014 functions, outperforming them in 15 test functions. Furthermore, jHDE performs well by improving in 17 functions, compared with jDE on these functions. The simulations indicate that the proposed HDE algorithm can provide reliable outcomes in finding the optimal solutions with a rapid convergence rate and avoiding the local minimum compared to the original DE algorithm.

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Enhancing differential evolution with interactive information

Cited by 9 publications

References 45 publications

Enhancing Differential Evolution on Continuous Optimization Problems by Detecting Promising Leaders

Enhancing Differential Evolution on Continuous Optimization Problems by Detecting Promising Leaders

Differential Evolution With Adaptive Guiding Mechanism Based on Heuristic Rules

A hybridizing-enhanced differential evolution for optimization

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