Improved Genetic Algorithm with Local Search for Satellite Range Scheduling System and its Application in Environmental monitoring

Song, Yanjie; Zhang, Zhongshan; Song, Bingyu; Chen, Yingwu

doi:10.1016/j.suscom.2018.11.009

Cited by 19 publications

(6 citation statements)

References 25 publications

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“…According to the literature review so far, only [ 10 , 11 ] study the re-planning problem in agile methodologies, which are only introductory and only present the characteristics of the problem, so they do not present a tool to support managers of software projects in the context of agile methodologies. They use heuristics and focus on agile methodologies.…”

Section: Bibliographic Reviewmentioning

confidence: 99%

RETRACTED CHAPTER: Software Project Planning Through Comparison of Bio-inspired Algorithms

Silva

Varela

Molina

et al. 2020

Communications in Computer and Information Science

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Currently many organizations have adopted the development of software projects with agile methodologies, particularly Scrum, which has more than 20 years of development. In these methodologies, software is developed iteratively and delivered to the client in increments called releases. In the releases, the goal is to develop system functionality that quickly adds value to the client's business. At the beginning of the project, one or more releases are planned. For solving the problem of replanning in the context of releases, a model is proposed considering the characteristics of agile development using Scrum. The results obtained show that the algorithm takes a little less than 7 min for solutions that propose replanning composed by 16 sprints, which is equivalent to 240 days of project. They show that applying a repair operator increases the hypervolume quality indicator in the resulting population.

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Section: Bibliographic Reviewmentioning

confidence: 99%

RETRACTED CHAPTER: Software Project Planning Through Comparison of Bio-inspired Algorithms

Silva

Varela

Molina

et al. 2020

Communications in Computer and Information Science

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show abstract

“…Thus, a hierarchical scheduling algorithm based on ant colony optimization is proposed. Song et al [30] first analyzed the influence of satellite distance on resource scheduling and proposed an algorithm that combines an improved genetic algorithm and a local search method to rapidly improve the quality of the scheduling scheme. Reference [31] first used the heuristic-based optimization method to coarsely search the feasible region space where the optimal solution is most likely to appear.…”

Section: Related Workmentioning

confidence: 99%

Dynamic Beam Scheduling of Multibeam Low Earth Orbit Satellites Based on an Enhanced Artificial Bee Colony Algorithm

et al. 2022

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The application of beam-hopping technology to low earth orbit satellites can effectively achieve flexible allocation and efficient utilization of on-board resources. Considering that the power resources on low earth orbit satellites are limited, the electromagnetic environment is complex and changeable, and the terminal distribution and service requirements are highly dynamic. We established the service model, service priority model and multibeam resource scheduling model under the constraints of beam bandwidth, on-board power, service priorities, etc. To solve the catastrophic problem of a large solution space in the resource scheduling model and to improve the convergence of the algorithm, we propose an enhanced artificial bee colony algorithm. The optimization strategy improves the process of population initialization, solution updates, and search for the global optimal solution. The simulation results show that under the constraints of cochannel interference and on-board resource utilization, the algorithm always converges to the objective function at the fastest speed, which proves that the algorithm has high applicability to the high dynamic characteristics of LEO satellites. In addition, the algorithm can obtain the global optimal solution, and thus, it can ensure the fairness of resource allocation and the effectiveness of service completion.

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“…To the best of our knowledge, there is no such research that focuses on how to select these two parameters more scientifically and selfadaptively. Consequently, the GA algorithm is introduced to address the parameter selecting issue in EEMD, owing to its outstanding optimization capability (Song, Zhang, Song, & Chen, 2019;Zou, Li, Kong, Ouyang, & Li, 2019).…”

Section: Eemdmentioning

confidence: 99%

“…Traditional selection operators are determined by the roulette selection and the tournament selection (Song et al, 2019). Owing to the randomness property of these methods, the results are usually unstable and the individual with high fitness will be missed sometimes (Zou et al, 2019).…”

Section: Difference Selection Operatormentioning

confidence: 99%

An improved genetic algorithm for optimizing ensemble empirical mode decomposition method

Zhang

Cai

Chen

et al. 2019

Systems Science & Control Engineering

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This paper proposes an improved ensemble empirical mode decomposition method based on genetic algorithm to solve the mode mixing problem in empirical mode decomposition (EMD) algorithm as well as the parameters selection issue in ensemble empirical mode decomposition (EEMD) algorithm. In a genetic algorithm (GA), the orthogonality index is used to formulate the fitness function and the Hamming distance is specified to design the difference selection operator. By coupling GA with EEMD algorithm, an improved decomposition method with higher efficiency is generated, namely GAEEMD. Simulation experiment with both intermittent signals and sinusoidal signals verifies the effectiveness and robustness of the proposed GAEEMD, compared with EMD, EEMD, and original GA algorithm.

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Improved Genetic Algorithm with Local Search for Satellite Range Scheduling System and its Application in Environmental monitoring

Cited by 19 publications

References 25 publications

RETRACTED CHAPTER: Software Project Planning Through Comparison of Bio-inspired Algorithms

RETRACTED CHAPTER: Software Project Planning Through Comparison of Bio-inspired Algorithms

Dynamic Beam Scheduling of Multibeam Low Earth Orbit Satellites Based on an Enhanced Artificial Bee Colony Algorithm

An improved genetic algorithm for optimizing ensemble empirical mode decomposition method

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