This study presents an inverse procedure to identify multiple cracks in beams using an evolutionary algorithm. By considering the crack detection procedure as an optimization problem, an objective function can be cons...
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This study presents an inverse procedure to identify multiple cracks in beams using an evolutionary algorithm. By considering the crack detection procedure as an optimization problem, an objective function can be constructed based on the change of the eigenfrequencies and some strain energy parameters. Each crack is modeled by a rotational spring. The changes in natural frequencies due to the presence of the cracks are related to a damage index vector. Then, the bees algorithm, a swarm-based evolutionary optimization technique, is used to optimize the objective function and find the damage index vector, whose positive components show the number and position of the cracks. A second objective function is also optimized to find the crack depths. Several experimental studies on cracked cantilever beams are conducted to ensure the integrity of the proposed method. The results show that the number of cracks as well as their sizes and locations can be predicted well through this method.
The bees algorithm is an intelligent optimisation tool mimicking the food foraging behaviour of honey bees. As a powerful search algorithm suitable for both continuous function and combinatorial optimisation it has go...
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ISBN:
(纸本)9781424437597
The bees algorithm is an intelligent optimisation tool mimicking the food foraging behaviour of honey bees. As a powerful search algorithm suitable for both continuous function and combinatorial optimisation it has gone through several modifications since its inception in order to improve its overall performance. This paper presents a new version of the bees algorithm which uses pheromone, a chemical substance secreted by bees and other insects into their environment, enabling them to communicate with other members of their own species. The new bees algorithm employs the pheromone to attract bees to explore the promising regions of the search space. Following a description of the algorithm, the paper presents the results obtained for a number of benchmark problems for functional optimization. Compared to the original bees algorithm, the new version showed an average improvement of 41% in convergence speed.
The problem of determining the minimum motion time for a robot arm is not new. Several approaches have been successfully proposed to solve this problem. One of these approaches involves generating a fixed number of jo...
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ISBN:
(纸本)9781424437597
The problem of determining the minimum motion time for a robot arm is not new. Several approaches have been successfully proposed to solve this problem. One of these approaches involves generating a fixed number of joint displacements to construct the joint trajectories via cubic spline functions before scaling the travel time to avoid violating kinematic constraints such as the maximum permissible velocity, acceleration and jerk. Conforming to these kinematic constraints is necessary to prevent trajectories that are undulant and normally unsuitable for robotic motion. This paper presents a Pareto-based multi-objective bees algorithm to determine the minimum travelling time for a SCARA-type robot arm that takes consideration of trajectory smoothness. The results obtained are better than those reported for solutions using the genetic algorithm (with breeder genetic algorithm operators and the path redistribution with relaxation operator), the Nelder-Mead flexible polyhedron search and the improved polytope algorithm with a penalty function.
The differential quadrature method combined with an evolutionary optimization algorithm has been proposed for crack detection in cylindrical shell structures. The circumferential crack, which is assumed to be open, is...
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The differential quadrature method combined with an evolutionary optimization algorithm has been proposed for crack detection in cylindrical shell structures. The circumferential crack, which is assumed to be open, is modeled by the extended rotational spring. A crack with finite length divides the shell into four segments. The governing differential equations of motion of the shell are formulated based on Flugge's shell theory. Applying differential quadrature to the differential equations of each segment and the corresponding boundary and continuity conditions results in an algebraic system of equations. Then, an eigenvalue analysis is performed to obtain the natural frequencies of the cracked shell. To identify the crack parameters, an optimization problem is defined and minimized by bees algorithm, a swarm-based evolutionary optimization technique. The integrity and applicability of the proposed method is confirmed by some experimental case studies. The results show that the crack statuses are predicted well. (C) 2013 Elsevier Ltd. All rights reserved.
This paper focuses on using the bees algorithm to tune the scaling gains and other parameters of a fuzzy logic controller for a flexible single-link robot arm. The algorithm optimises those quantities so that the cont...
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ISBN:
(纸本)9781424437597
This paper focuses on using the bees algorithm to tune the scaling gains and other parameters of a fuzzy logic controller for a flexible single-link robot arm. The algorithm optimises those quantities so that the controller can move the link to a desired position with the minimum amount of vibration during the movement. Following a description of the algorithm, the paper gives the experimental results obtained for the robot demonstrating the efficiency and robustness of the design.
Endless resources provisioning illusion is the mainstay for cloud computing paradigm. However, the unpredictable volatility nature involving web applications workload demand would highly hinder cloud computing platfor...
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Endless resources provisioning illusion is the mainstay for cloud computing paradigm. However, the unpredictable volatility nature involving web applications workload demand would highly hinder cloud computing platforms performance, furthermore, expose cloud resources for possible devastation. Accordingly, this work proposes autonomic power aware SLA-oriented cloud resources orchestration two-tier architecture. Despite complexity and uncertainties of the workload fluctuations, the proposed architecture geared for leveraging cloud system resources utilization, ensuring explicit guarantees on web applications' responsiveness obligations, meanwhile achieving power consumption minimization objectives. The proposed architecture consolidates heuristic methodologies along with control theory approaches in a resource orchestration hierarchical structure. Firstly, an autonomic global controller is presented. The proposed global controller exploits heuristic methodology for mapping virtual machines (VMs) to the appreciate cloud resources in accordance to heuristic multidimensional objectives based placement strategy. Secondly, a proactive fuzzy-logic based local controller is proposed. The proposed local controller aimed at in confronting workloads' sustainable fluctuations via proactive amendment for the placement and provisioning schedules. Furthermore, the proposed local controller oriented towards maintaining active power management policy especially during transient peak of usage, thereby mitigating overall costs, and extending resources capacity and performance capabilities. Simulation results and comparisons demonstrate that the proposed architecture significantly surpasses previous approaches in terms of total energy consumption, furthermore maintaining web applications SLAs objectives despite dynamic workload scenarios.
Due to sporadic climate change and global warming, world have signed international protocols promising to reduce their nation's emissions. This study focuses on the application of the bees algorithm, embedded with...
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ISBN:
(纸本)9781467361293;9781467361286
Due to sporadic climate change and global warming, world have signed international protocols promising to reduce their nation's emissions. This study focuses on the application of the bees algorithm, embedded with an artificial neural network, to determine practical yearly reductions for minimizing oil, natural gas, and coal emissions as by-products of energy consumption in Canada's manufacturing sector based on the Copenhagen Targets for Canada for 2020.
This paper describes the first application of the bees algorithm to mechanical design optimization. The bees algorithm is a search procedure inspired by the way honey bees forage for food. Two standard mechanical desi...
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This paper describes the first application of the bees algorithm to mechanical design optimization. The bees algorithm is a search procedure inspired by the way honey bees forage for food. Two standard mechanical design problems, the design of a welded beam structure and the design of coil springs, were used to benchmark the bees algorithm against other optimization techniques. The paper presents the results obtained showing the robust performance of the bees algorithm.
Detection of moving objects in a video sequence is a growing field, used in various domains. There are, in the literature, several approaches to detect such movements. In this paper, we will focus on one of these appr...
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ISBN:
(纸本)9781479902255
Detection of moving objects in a video sequence is a growing field, used in various domains. There are, in the literature, several approaches to detect such movements. In this paper, we will focus on one of these approaches namely block matching. We propose to speed the performances of the block matching algorithm using the bees' algorithm which is known for its efficient exploration of search space as it uses two interesting strategies intensification and diversification. The proposed algorithm, BAforBM, is evaluated and compared to two existing ones: genetic bloc matching algorithm and a block matching algorithm based on PSO (Particule Swarm Optimisation).
This paper focuses on the time delay estimation of the system described in the form of discrete-time state equation with multiple input delays. To estimate the input delays, a new evolutionary computation called the a...
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This paper focuses on the time delay estimation of the system described in the form of discrete-time state equation with multiple input delays. To estimate the input delays, a new evolutionary computation called the artificial bee colony (ABC) algorithm is utilized. This algorithm is originally motivated from the social behaviors of honeybee organization, and it has been proven to be a powerful means for solving the optimized problem. Based on the proposed algorithm, the unknown system input delays can be further solved by minimizing a quadratic cost function of the system. Two illustrative examples are provided to verify the potential of the presented method in the time delay estimation. Some simulations containing different initial condition examinations and appearance of noises are further given. Numerical results show that the proposed method can do well in the multiple inputs delay estimation of discrete-time state equations.
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