Comprehensive learning pigeon-inspired optimization with tabu list

Xiang, Shang; Xing, Lining; Zou, Kai

doi:10.1007/s11432-018-9728-x

Cited by 21 publications

(9 citation statements)

References 6 publications

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“…其中, 在算法设计上, 鸽群优化算法发展出不同的变体. 例如 Cheng 等提出了一种具有不同拓扑结构的改进鸽群优化算法, 并将环形(Ring)结构应用于地图与指南针算子 [18] ; Yang 等提出了一种基于二进制和实数混合编码的改进鸽群优化算法 [20] [20] ; Duan 等提出了 Predator-Prey 鸽群优化算法 [21] 和变异多目标鸽群优化算法 [22] 等改进方法.…”

Section: 鸽群优化算法unclassified

Generalized pigeon-inspired optimization algorithm for balancing exploration and exploitation

Cheng¹,

Zhang²,

Shi³

et al. 2022

Sci. Sin.-Tech.

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Section: 鸽群优化算法unclassified

Generalized pigeon-inspired optimization algorithm for balancing exploration and exploitation

Cheng¹,

Zhang²,

Shi³

et al. 2022

Sci. Sin.-Tech.

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“…As a result, ( 11) is difficult to solve by traditional optimization methods (Liu et al, 2020). In this paper, we use EAs to solve (11) as they have great potential in dealing with complex optimization problems (Yi et al, 2020;Luo et al, 2020;Xiang et al, 2019). The encoding scheme in Fig.…”

Section: Motivationmentioning

confidence: 99%

A new differential evolution algorithm for joint mining decision and resource allocation in a MEC-enabled wireless blockchain network

Wang

Chen

Huang

et al. 2021

Computers & Industrial Engineering

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“…There have been published many metaheuristic algorithms for various practical engineering problems [47,48,49]. Particularly, the simulated annealing algorithm proposed decades ago [50,51], is a stochastic optimization method.…”

Section: Structure Of the Isa Based Heuristicmentioning

confidence: 99%

Simulated annealing based heuristic for multiple agile satellites scheduling under cloud coverage uncertainty

Chen

et al. 2020

Preprint

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Agile satellites are the new generation of Earth observation satellites (EOSs) with stronger attitude maneuvering capability. Since optical remote sensing instruments equipped on satellites cannot see through the cloud, the cloud coverage has a significant influence on the satellite observation missions. We are the first to address multiple agile EOSs scheduling problem under cloud coverage uncertainty where the objective aims to maximize the entire observation profit. The chance constraint programming model is adopted to describe the uncertainty initially, and the observation profit under cloud coverage uncertainty is then calculated via sample approximation method. Subsequently, an improved simulated annealing based heuristic combining a fast insertion strategy is proposed for large-scale observation missions. The experimental results show that the improved simulated annealing heuristic outperforms other algorithms for the multiple AEOSs scheduling problem under cloud coverage uncertainty, which verifies the efficiency and effectiveness of the proposed algorithm.

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Comprehensive learning pigeon-inspired optimization with tabu list

Cited by 21 publications

References 6 publications

Generalized pigeon-inspired optimization algorithm for balancing exploration and exploitation

Generalized pigeon-inspired optimization algorithm for balancing exploration and exploitation

A new differential evolution algorithm for joint mining decision and resource allocation in a MEC-enabled wireless blockchain network

Simulated annealing based heuristic for multiple agile satellites scheduling under cloud coverage uncertainty

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