Performance Analyses of Differential Evolution Algorithm Based on Dynamic Fitness Landscape

Li, Kangshun; Liang, Zhuo-Zhi; Yang, Shuling; Chen, Zhangxin; Wang, Hui; Lin, Zhiyi

doi:10.4018/ijcini.2019010104

Cited by 39 publications

(17 citation statements)

References 16 publications

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“…According to statistics, the central government invested 368 million yuan in ecological construction in Tibet from 1996 to 2002. Human activities had the underlying contribution to water and soil conservation (B. Li, Liang, Bao, et al, 2019; K. Li, Liang, Yang, et al, 2019). Natural forest protection, returning farmland to grassland, afforestation, and greening in Lhasa have been carried out successively and achieved an obvious ecological protection effect.…”

Section: Discussionmentioning

confidence: 99%

“…Landscape pattern is an important manifestation of land use, which mainly focuses on the research on landscape heterogeneity and landscape dynamics (Guadalupe Priego-Santander et al, 2013;B. Li, Liang, Bao, et al, 2019;K. Li, Liang, Yang, et al, 2019;Seidl & Golobic, 2020;Zhang et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

“…Landscape pattern is an important manifestation of land use, which mainly focuses on the research on landscape heterogeneity and landscape dynamics (Guadalupe Priego‐Santander et al, 2013; B. Li, Liang, Bao, et al, 2019; K. Li, Liang, Yang, et al, 2019; Seidl & Golobic, 2020; Zhang et al, 2020). The approaches to investigate landscape pattern mainly include the landscape pattern index method and spatial autocorrelation analysis method.…”

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Dynamic changes of land use/cover and landscape pattern in a typical alpine river basin of theQinghai‐TibetPlateau, China

Zuo

et al. 2021

Land Degrad Dev

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Landscape heterogeneity is a comprehensive reflection of spatial patch and gradient, which is an important feature of landscape pattern. The China land use/cover dataset (CLUD) with 1 km spatial resolution was used in this study to investigate the dynamic changes of land use/cover in the Yarlung Tsangpo River basin (YTRB) of China from 1980 to 2015 through the land use transition matrix and dynamic degrees. The semivariogram model of the study area was constructed at different scales to determine the landscape characteristic scale of the YTRB. The traditional landscape index method and moving window method were used to investigate the watershed landscape pattern at the class and landscape levels, respectively. The results showed that the overall change in land use over the YTRB was not significant during the period 1980-2015, whereas the relative change in each type of land use was significant. Permanent glacier-snow significantly degraded by 22.72%, while built-up areas increased by 106.38%. The degree of the landscape fragmentation and diversity gradually increased from upstream to downstream. The unused land has the highest degree of fragmentation, and the agglomeration degree of urban land patches increased. The result of land use dynamic degree indicated that natural factors were the main causes of changes in land use during the first sub-period 1980-2000, while human activities were the major drivers of changes in land use during the second sub-period 2000-2015. The results can provide important information on the impact of regional development and environmental governance policies on the changes in land use/cover and landscape in Tibet.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

See 2 more Smart Citations

Dynamic changes of land use/cover and landscape pattern in a typical alpine river basin of theQinghai‐TibetPlateau, China

Zuo

et al. 2021

Land Degrad Dev

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“…The TSP can be described as following: Since meta-heuristic methods are able to find solutions rapidly by the strong ability of exploring and exploiting the full search space, those algorithms have attracted a lot of attention in solving global optimization problems Li, Chen, et al, 2018). Considering the advantage of it, many heuristics algorithms are applied to deal with TSP in recent years, such as simulated annealing (SA) (Li, Zhou, Zhang, 2011), tabu search (TB) (Lin, Bian, Liu, 2016), neural networks (NN) (Li, Qiao, Li, 2016), and genetic algorithm (GA) Li, Liang, et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

A Novel Particle Swarm Optimization With Genetic Operator and Its Application to TSP

Wei

Xing

Xia

et al. 2021

International Journal of Cognitive Informatics and Natural Intelligence

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To solve some problems of particle swarm optimization, such as the premature convergence and falling into a sub-optimal solution easily, we introduce the probability initialization strategy and genetic operator into the particle swarm optimization algorithm. Based on the hybrid strategies, we propose a improved hybrid particle swarm optimization, namely IHPSO, for solving the traveling salesman problem. In the IHPSO algorithm, the probability strategy is utilized into population initialization. It can save much more computing resources during the iteration procedure of the algorithm. Furthermore, genetic operators, including two kinds of crossover operator and a directional mutation operator, are used for improving the algorithm’s convergence accuracy and population diversity. At last, the proposed method is benchmarked on 9 benchmark problems in TSPLIB and the results are compared with 4 competitors. From the results, it is observed that the proposed approach significantly outperforms others on most the 9 datasets.

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A dynamic population reduction differential evolution algorithm combining linear and nonlinear strategy piecewise functions

Wang

et al. 2021

Concurrency and Computation

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The population size has a great impact on the performance of the Differential Evolution (DE) algorithm, but in many classic DE algorithms, the population size is usually determined by the user based on the experience value, and it remains unchanged during the evolution process, which greatly affect the performance of DE. To this end, a dynamic population reduction differential evolution algorithm (DPSHADE) that combines linear and nonlinear strategy piecewise functions is proposed. The algorithm uses a dynamic population size reduction method to dynamically adjust the population size during operation, and construct a combination of linear and nonlinear piecewise functions for dynamic scale adaptive adjustment. In this paper, we proposed the DPSHADE algorithm and is compared with the four traditional algorithms in the CEC2017 benchmark set. The experimental results show that DPSHADE performs better in overall performance, which is significant better than the performance of SHADE.

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Performance Analyses of Differential Evolution Algorithm Based on Dynamic Fitness Landscape

Cited by 39 publications

References 16 publications

Dynamic changes of land use/cover and landscape pattern in a typical alpine river basin of theQinghai‐TibetPlateau, China

Dynamic changes of land use/cover and landscape pattern in a typical alpine river basin of theQinghai‐TibetPlateau, China

A Novel Particle Swarm Optimization With Genetic Operator and Its Application to TSP

A dynamic population reduction differential evolution algorithm combining linear and nonlinear strategy piecewise functions

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