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[This corrects the article DOI: 10.3897/zookeys.1059.63581.].
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The balance between exploitation and exploration essentially determines the performance of a population-based optimization algorithm, which is also a big challenge in algorithm design. Particle swarm optimization (PSO) has strong ability in exploitation, but is relatively weak in exploration, while crow search algorithm (CSA) is characterized by simplicity and more randomness. This study proposes a new crow swarm optimization algorithm coupling PSO and CSA, which provides the individuals the possibility of exploring the unknown regions under the guidance of another random individual. The proposed CSO algorithm is tested on several benchmark functions, including both unimodal and multimodal problems with different variable dimensions. The performance of the proposed CSO is evaluated by the optimization efficiency, the global search ability, and the robustness to parameter settings, all of which are improved to a great extent compared with either PSO and CSA, as the proposed CSO combines the advantages of PSO in exploitation and that of CSA in exploration, especially for complex high-dimensional problems.
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Corvos , Algoritmos , Animais , Benchmarking , HumanosRESUMO
Plateau pikas (Ochotonacurzoniae) are regarded as one of the main causes of the degradation of alpine meadows in the Qinghai-Tibet Plateau (QTP). The population density of plateau pikas is directly related to the degree of grassland damage. In this study, field observation was conducted for one week in the southeastern QTP in August 2019. A random encounter model (REM) was used to estimate the population density of plateau pikas from photographs and videos, and the frequencies of different behaviors were calculated. In addition, the effects of water-source distance and terrain on the distribution of plateau pikas and the frequencies of different pika behaviors under different population densities were explored. The observations and knowledge derived from this study provide a reference for the population control of plateau pikas.
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As an important species in the Qinghai-Tibet Plateau, the roles played by plateau pikas in grassland degradation and protection are controversial. The behavior characteristics and population density of this species are important in answering this question, but these traits have not been fully elucidated to date. Camera-capture methods have been used widely in recent years to characterize or calculate population density with the advantage of simple operation and nonintrusive investigation. However, establishing the relationship between actual population density and monitoring data with the condition that individual identification is not possible is a major challenge for this method. In this study, a model composed of a behavioral module and a burrow system module is proposed and applied to simulate the moving path of each individual pika. Based on Monte Carlo method, the model is used to develop the relationship between population density and recorded capture number, which is compared with the results derived from the random encounter model (REM) based on field observations. The simulated results mixed with the calculated density based on observation data could reach R 2 = 0.98 using linear fitting, with proper parameter settings. A novel index named activity intensity of pikas per population density is also proposed, providing information on both the ecological physical characteristics and monitoring space. The influence of different parameters on this index, mainly the pika number per burrow system, pika activity time outside the burrow, and activity intensity, is discussed. The proposed methodology can be applied to different scenarios in further studies when behavioral characteristics of pikas change for such reasons as climate change and vegetation degradation.