A two-stage cooperative scatter search algorithm with multi-population hierarchical learning mechanism

Zhao, Fuqing; Zhou, Gang; Xu, Tianpeng; Zhu, Ningning; Jonrinaldi,

doi:10.1016/j.eswa.2022.117444

Cited by 11 publications

(2 citation statements)

References 62 publications

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“…In [37] a two-stage cooperative scatter search algorithm with a multi-population hierarchical learning mechanism was introduced. This algorithm is also based on the division of individuals into three groups.…”

Section: Related Workmentioning

confidence: 99%

“…However, a disadvantageous phenomenon is giving a new interpretation to previously known search formulas with only their symbolic modification. Undoubtedly, some interesting issues here include creating hybrid PBAs [6,19,20], creating multi-population PBAs [3,8,9,14,15,17,18,22,24,28,36,37], creating multi-criteria PBAs [14,15], and developing interesting applications [3,8,9,17,22,25,28,34], etc.…”

Section: Introductionmentioning

confidence: 99%

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Multi-Population-Based Algorithm with an Exchange of Training Plans Based on Population Evaluation

Łapa

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Kisiel-Dorohinicki

et al. 2022

Journal of Artificial Intelligence and Soft Computing Research

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Population Based Algorithms (PBAs) are excellent search tools that allow searching space of parameters defined by problems under consideration. They are especially useful when it is difficult to define a differentiable evaluation criterion. This applies, for example, to problems that are a combination of continuous and discrete (combinatorial) problems. In such problems, it is often necessary to select a certain structure of the solution (e.g. a neural network or other systems with a structure usually selected by the trial and error method) and to determine the parameters of such structure. As PBAs have great application possibilities, the aim is to develop more and more effective search formulas used in them. An interesting approach is to use multiple populations and process them with separate PBAs (in a different way). In this paper, we propose a new multi-population-based algorithm with: (a) subpopulation evaluation and (b) replacement of the associated PBAs subpopulation formulas used for their processing. In the simulations, we used a set of typical CEC2013 benchmark functions. The obtained results confirm the validity of the proposed concept.

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Section: Related Workmentioning

confidence: 99%