Adaptive Differential Evolution Based on Successful Experience Information

Cheng, Lianzheng; Wang, Yun; Wang, Chao; Mohamed, Ali Wagdy; Xiao, Tiaojie

doi:10.1109/access.2020.3022049

Cited by 12 publications

(6 citation statements)

References 63 publications

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“…proposed a new variation of DE to reduce the variation strategy of randomness in search direction and improve the search capability of the algorithm [32].…”

Section: Literature Reviewmentioning

confidence: 99%

Optimal Transportation Scheduling of Prefabricated Components Based on Improved Hybrid Differential Firefly Algorithm

Zou

Zhu

et al. 2022

Mathematical Problems in Engineering

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The prefabricated construction industry has entered a new stage of development with the support of national policies. In order to solve the optimal scheduling problem of transportation vehicles in urban multi-prefabricated component factories, in this study, the aggregation optimization algorithm is introduced, and a new hybrid optimization algorithm is designed and improved, that is, the improved hybrid difference firefly algorithm (IHDFA). On the basis of fully considering the transportation cost and road traffic impedance, the prefabricated component factory constructs a linear programming model with the shortest transportation path and the lowest comprehensive cost as the main goal to reasonably plan the driving arrangement of transportation vehicles. Finally, the IHDFA algorithm is applied to a specific example and compared with the genetic algorithm, firefly algorithm, differential evolution algorithm, particle swarm algorithm, and hybrid differential firefly algorithm. The results show that the IHDFA proposed in this study effectively reduces the comprehensive transportation cost of prefabricated components, which verifies the practicability and effectiveness of the optimization model and algorithm.

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“…proposed a new variation of DE to reduce the variation strategy of randomness in search direction and improve the search capability of the algorithm [32].…”

Section: Literature Reviewmentioning

confidence: 99%

Optimal Transportation Scheduling of Prefabricated Components Based on Improved Hybrid Differential Firefly Algorithm

Zou

Zhu

et al. 2022

Mathematical Problems in Engineering

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“…2. The proposed algorithm is derived from SHADE, JADE and it might be assumed that SDIS is inherited (Awad et al, 2018), (Cheng et al, 2020), (Meng et al, 2020), (Yi et al, 2021), (Zhong and Cheng, 2021), (Kumar et al, 2022) (Zuo and Guo, 2022): HVB.…”

Section: State-of-the-art On Strategies Of Dealing With Infeasible So...mentioning

confidence: 99%

The importance of being constrained: dealing with infeasible solutions in Differential Evolution and beyond

Kononova¹,

Vermetten²,

Caraffini³

et al. 2022

Preprint

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We argue that results produced by a heuristic optimisation algorithm cannot be considered reproducible unless the algorithm fully specifies what should be done with solutions generated outside the domain, even in the case of simple box constraints. Currently, in the field of heuristic optimisation, such specification is rarely mentioned or investigated due to the assumed triviality or insignificance of this question. Here, we demonstrate that, at least in algorithms based on Differential Evolution, this choice induces notably different behaviours -in terms of performance, disruptiveness and population diversity. This is shown theoretically (where possible) for standard Differential Evolution in the absence of selection pressure and experimentally for the standard and state-of-the-art Differential Evolution variants on special test function f0 and BBOB benchmarking suite, respectively. Moreover, we demonstrate that the importance of this choice quickly grows with problem's dimensionality. Different Evolution is not at all special in this regard -there is no reason to presume that other heuristic optimisers are not equally affected by the aforementioned algorithmic choice. Thus, we urge the field of heuristic optimisation to formalise and adopt the idea of a new algorithmic component in heuristic optimisers, which we call here a strategy of dealing with infeasible solutions. This component needs to be consistently (a) specified in algorithmic descriptions to guarantee reproducibility of results, (b) studied to better understand its impact on algorithm's performance in a wider sense and (c) included in the (automatic) algorithmic design. All of these should be done even for problems with box constraints.

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“…However, it has few issues such as convergence rate and local exploitation ability. In order to overcome its shortcomings, lots of robust and effective DE has been designed in the literature like IDE, 18 GRCDE, 19 UDE, 20 EFADE, 21 MMDE, 22 DEPS, 23 EDE and EBDE, 24 AGDE, 25 EAGDE, 26 daDE, 27 DE with biological-based mutation operator, 28 HDEMCO, 29 MRDE, 30 ADEwSE, 31 EJADE, 32 BADE, 33 and ADE. 34 Also, PSO has attracted attention to solve many complex optimization problems due to its efficient search ability and simplicity.…”

Section: Related Workmentioning

confidence: 99%

An advanced hybrid algorithm for nonlinear function optimization with real world applications

Verma

Parouha

2021

Concurrency and Computation

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This article presents an advanced hybrid algorithm (haDEPSO) for nonlinear constrained function optimization problem. It consists of the advised advanced differential evolution, that is, aDE (wherein a novel mutation, crossover, and selection strategy is introduced, to avoid premature convergence) and particle swarm optimization, namely, aPSO (in which novel gradually varying parameters is utilized, to escape stagnation).The proposed haDEPSO achieve better results as aDE and aPSO provides diverse convergence characteristic to the solution space. Moreover, in haDEPSO distinct population is merged with other in a predefined way, to balance between exploration and exploitation. Efficiency of the proposed hybrid haDEPSO along with its integrating component aDE and aPSO are inspected on IEEE CEC'2006 constrained benchmark functions and IEEE CEC'2011 real world problems. According to the comparison results, the proposed methods achieved better results than traditional DE and PSO with their hybrids as well as over many state-of-the-art algorithms.

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Adaptive Differential Evolution Based on Successful Experience Information

Cited by 12 publications

References 63 publications

Optimal Transportation Scheduling of Prefabricated Components Based on Improved Hybrid Differential Firefly Algorithm

Optimal Transportation Scheduling of Prefabricated Components Based on Improved Hybrid Differential Firefly Algorithm

The importance of being constrained: dealing with infeasible solutions in Differential Evolution and beyond

An advanced hybrid algorithm for nonlinear function optimization with real world applications

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