One of the main objectives of artificial intelligence lies in the simulation of the behavior of living organisms;emotions are a fundamental part of life, and they cannot be left aside when simulating behavior. In this...One of the main objectives of artificial intelligence lies in the simulation of the behavior of living organisms;emotions are a fundamental part of life, and they cannot be left aside when simulating behavior. In this research, software is developed that simulates the behavior of birds with different characteristics. The latter interacts by considering different stimuli from the environment (external), and the internal state of the subject (objectives). To achieve this, a model of birds in the role of prey and predators is developed that focuses on the study of the interaction between these organisms that exhibit specific behaviors in their environment. This project is a seminal work that aims to represent the emotions of birds, and the latter caused by stimuli from a dynamic environment.展开更多
To investigate the activities of typical night-migrating birds under the light with various spectral compositions during autumn migration,23 Siberian Rubythroat Luscinia calliope were tested under colorful LED lights ...To investigate the activities of typical night-migrating birds under the light with various spectral compositions during autumn migration,23 Siberian Rubythroat Luscinia calliope were tested under colorful LED lights with different wavelengths and mixed white at 100 lx.Using behavior response as an indicator,the quantitative relationship between the activity level of test birds and illumination stimulation duration was acquired.It was found that the visible light sensitivity of Siberian Rubythroat was the highest for the 478 nm turquoise and decreased towards the 622 nm red.展开更多
Bird swarm algorithm(BSA), a novel bio-inspired algorithm, has good performance in solving numerical optimization problems. In this paper, a new improved bird swarm algorithm is conducted to solve unconstrained optimi...Bird swarm algorithm(BSA), a novel bio-inspired algorithm, has good performance in solving numerical optimization problems. In this paper, a new improved bird swarm algorithm is conducted to solve unconstrained optimization problems. To enhance the performance of BSA, handling boundary constraints are applied to fix the candidate solutions that are out of boundary or on the boundary in iterations, which can boost the diversity of the swarm to avoid the premature problem. On the other hand, we accelerate the foraging behavior by adjusting the cognitive and social components the sin cosine coefficients. Simulation results and comparison based on sixty benchmark functions demonstrate that the improved BSA has superior performance over the BSA in terms of almost all functions.展开更多
The rapid advent in artificial intelligence and big data has revolutionized the dynamic requirement in the demands of the computing resource for executing specific tasks in the cloud environment.The process of achievi...The rapid advent in artificial intelligence and big data has revolutionized the dynamic requirement in the demands of the computing resource for executing specific tasks in the cloud environment.The process of achieving autonomic resource management is identified to be a herculean task due to its huge distributed and heterogeneous environment.Moreover,the cloud network needs to provide autonomic resource management and deliver potential services to the clients by complying with the requirements of Quality-of-Service(QoS)without impacting the Service Level Agreements(SLAs).However,the existing autonomic cloud resource managing frameworks are not capable in handling the resources of the cloud with its dynamic requirements.In this paper,Coot Bird Behavior Model-based Workload Aware Autonomic Resource Management Scheme(CBBM-WARMS)is proposed for handling the dynamic requirements of cloud resources through the estimation of workload that need to be policed by the cloud environment.This CBBM-WARMS initially adopted the algorithm of adaptive density peak clustering for workloads clustering of the cloud.Then,it utilized the fuzzy logic during the process of workload scheduling for achieving the determining the availability of cloud resources.It further used CBBM for potential Virtual Machine(VM)deployment that attributes towards the provision of optimal resources.It is proposed with the capability of achieving optimal QoS with minimized time,energy consumption,SLA cost and SLA violation.The experimental validation of the proposed CBBMWARMS confirms minimized SLA cost of 19.21%and reduced SLA violation rate of 18.74%,better than the compared autonomic cloud resource managing frameworks.展开更多
白鲨优化算法是受白鲨捕猎行为的启发设计的一种新元启发式算法。该算法在求解高维优化问题时,易进入早熟状态,寻优结果精度较低。为此,文章提出一种改进的白鲨优化(improved white shake optimizer,IWSO)算法。首先使用Sinusoidal混沌...白鲨优化算法是受白鲨捕猎行为的启发设计的一种新元启发式算法。该算法在求解高维优化问题时,易进入早熟状态,寻优结果精度较低。为此,文章提出一种改进的白鲨优化(improved white shake optimizer,IWSO)算法。首先使用Sinusoidal混沌映射初始化种群,以提高种群多样性及初始解在解空间的分布性;其次,引入鸟群搜索行为,赋予白鲨游动速度自适应动态惯性权重,以提高算法的收敛速度;最后,在位置更新阶段引入精英白鲨余弦变异策略,利用余弦函数的周期性特征,驱使白鲨个体在精英白鲨的有限邻域内进行精细化开发,以提高收敛精度。在23个著名基准函数和CEC2014函数上做了性能对比实验,其结果表明,IWSO算法优于6种对比算法,适合求解函数优化问题。展开更多
文摘One of the main objectives of artificial intelligence lies in the simulation of the behavior of living organisms;emotions are a fundamental part of life, and they cannot be left aside when simulating behavior. In this research, software is developed that simulates the behavior of birds with different characteristics. The latter interacts by considering different stimuli from the environment (external), and the internal state of the subject (objectives). To achieve this, a model of birds in the role of prey and predators is developed that focuses on the study of the interaction between these organisms that exhibit specific behaviors in their environment. This project is a seminal work that aims to represent the emotions of birds, and the latter caused by stimuli from a dynamic environment.
基金Supported by Science and Technology Support Program of Beijing(No.Y0604017040391)National Natural Science Foundation of China(No.51208350)Doctoral Fund of Ministry of Education of China(No.20100032110047)
文摘To investigate the activities of typical night-migrating birds under the light with various spectral compositions during autumn migration,23 Siberian Rubythroat Luscinia calliope were tested under colorful LED lights with different wavelengths and mixed white at 100 lx.Using behavior response as an indicator,the quantitative relationship between the activity level of test birds and illumination stimulation duration was acquired.It was found that the visible light sensitivity of Siberian Rubythroat was the highest for the 478 nm turquoise and decreased towards the 622 nm red.
基金Supported by the National Natural Science Foundation of China(11871383,71471140 and 11771058)
文摘Bird swarm algorithm(BSA), a novel bio-inspired algorithm, has good performance in solving numerical optimization problems. In this paper, a new improved bird swarm algorithm is conducted to solve unconstrained optimization problems. To enhance the performance of BSA, handling boundary constraints are applied to fix the candidate solutions that are out of boundary or on the boundary in iterations, which can boost the diversity of the swarm to avoid the premature problem. On the other hand, we accelerate the foraging behavior by adjusting the cognitive and social components the sin cosine coefficients. Simulation results and comparison based on sixty benchmark functions demonstrate that the improved BSA has superior performance over the BSA in terms of almost all functions.
文摘The rapid advent in artificial intelligence and big data has revolutionized the dynamic requirement in the demands of the computing resource for executing specific tasks in the cloud environment.The process of achieving autonomic resource management is identified to be a herculean task due to its huge distributed and heterogeneous environment.Moreover,the cloud network needs to provide autonomic resource management and deliver potential services to the clients by complying with the requirements of Quality-of-Service(QoS)without impacting the Service Level Agreements(SLAs).However,the existing autonomic cloud resource managing frameworks are not capable in handling the resources of the cloud with its dynamic requirements.In this paper,Coot Bird Behavior Model-based Workload Aware Autonomic Resource Management Scheme(CBBM-WARMS)is proposed for handling the dynamic requirements of cloud resources through the estimation of workload that need to be policed by the cloud environment.This CBBM-WARMS initially adopted the algorithm of adaptive density peak clustering for workloads clustering of the cloud.Then,it utilized the fuzzy logic during the process of workload scheduling for achieving the determining the availability of cloud resources.It further used CBBM for potential Virtual Machine(VM)deployment that attributes towards the provision of optimal resources.It is proposed with the capability of achieving optimal QoS with minimized time,energy consumption,SLA cost and SLA violation.The experimental validation of the proposed CBBMWARMS confirms minimized SLA cost of 19.21%and reduced SLA violation rate of 18.74%,better than the compared autonomic cloud resource managing frameworks.
文摘白鲨优化算法是受白鲨捕猎行为的启发设计的一种新元启发式算法。该算法在求解高维优化问题时,易进入早熟状态,寻优结果精度较低。为此,文章提出一种改进的白鲨优化(improved white shake optimizer,IWSO)算法。首先使用Sinusoidal混沌映射初始化种群,以提高种群多样性及初始解在解空间的分布性;其次,引入鸟群搜索行为,赋予白鲨游动速度自适应动态惯性权重,以提高算法的收敛速度;最后,在位置更新阶段引入精英白鲨余弦变异策略,利用余弦函数的周期性特征,驱使白鲨个体在精英白鲨的有限邻域内进行精细化开发,以提高收敛精度。在23个著名基准函数和CEC2014函数上做了性能对比实验,其结果表明,IWSO算法优于6种对比算法,适合求解函数优化问题。
