This paper proposes a new self-adaptive genetic algorithm. This new algorithm divides the whole evolution process into three stages. At each stage, the new algorithm adopts different operation method. The main ideas a...
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This paper proposes a new self-adaptive genetic algorithm. This new algorithm divides the whole evolution process into three stages. At each stage, the new algorithm adopts different operation method. The main ideas are grading balance selection, continuous crossover operation. The new algorithm designs especially self-adaptive mutation probability according to the principle of searching for things. Numerical experiments show that the new algorithm is more effective than the comparative algorithm in realizing the high convergence precision, reducing the convergence generation and good at keeping the stability of the adaptive genetic algorithm.
This paper focuses on the parcel hub scheduling problem with shortcuts (PHSPwS), which is a critical inbound scheduling problem in parcel delivery industries such as postal service. PHSPwS employs shortcuts to determi...
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This paper focuses on the parcel hub scheduling problem with shortcuts (PHSPwS), which is a critical inbound scheduling problem in parcel delivery industries such as postal service. PHSPwS employs shortcuts to determine the unloading schedule of inbound trailers/trucks in the closed-loop sorting process to minimize total makespan. PHSPwS not only considers the unequal batch sizes and various arrival times of inbound trailers but most importantly, the alternative parcel routing caused by the existence of multiple shortcuts in the closed-loop sortation system. A non-linear mixed integer program model is first formulated to address this problem. Given the computational complexity of the developed model, this research further proposes an adaptive genetic algorithm (AGA) to solve PHSPwS effectively. In the proposed AGA, local search (LS) is adopted to avoid entrapment in the local optimal solution, whereas fuzzy logic control (FLC) is utilized to adjust the probability of crossover and mutation rates. Considering the changes in average fitness values of parents and offspring in two consecutive generations could enhance the searching capability of the proposed algorithm. Computational results show that AGA with LS and FLC performs the solution better and with more stability than the other algorithms.
An improved self-calibrating algorithm for visual servo based on adaptive genetic algorithm is proposed in this paper. Our approach introduces an extension of Mendonca-Cipolla and G. Chesi's self-calibration for the ...
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An improved self-calibrating algorithm for visual servo based on adaptive genetic algorithm is proposed in this paper. Our approach introduces an extension of Mendonca-Cipolla and G. Chesi's self-calibration for the positionbased visual servo technique which exploits the singular value property of the essential matrix. Specifically, a suitable dynamic online cost function is generated according to the property of the three singular values. The visual servo process is carried out simultaneous to the dynamic self-calibration, and then the cost function is minimized using the adaptive genetic algorithm instead of the gradient descent method in G. Chesi's approach. Moreover, this method overcomes the limitation that the initial parameters must be selected close to the true value, which is not constant in many cases. It is not necessary to know exactly the camera intrinsic parameters when using our approach, instead, coarse coding bounds of the five parameters are enough for the algorithm, which can be done once and for all off-line. Besides, this algorithm does not require knowledge of the 3D model of the object. Simulation experiments are carried out and the results demonstrate that the proposed approach provides a fast convergence speed and robustness against unpredictable perturbations of camera parameters, and it is an effective and efficient visual servo algorithm.
Due to the currently insufficient consideration of task fitness and task coordination for task allocation in collaborative customized product development, this research was conducted based on the analysis of collabora...
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Due to the currently insufficient consideration of task fitness and task coordination for task allocation in collaborative customized product development, this research was conducted based on the analysis of collaborative customized product development process and task allocation strategy. The definitions and calculation formulas of task fitness and task coordination efficiency were derived, and a multiobjective optimization model of product customization task allocation was constructed. A solution based on adaptive genetic algorithm was proposed, and the feasibility and effectiveness of the task allocation algorithm were tested and verified using a 5-MW wind turbine product development project as example.
This paper proposes a Linear adaptive genetic algorithm (LAGA) for optimal power flow (OPF) problem. The proposed approach offers faster convergence than the standard geneticalgorithm. In this study, LAGA is applied ...
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This paper proposes a Linear adaptive genetic algorithm (LAGA) for optimal power flow (OPF) problem. The proposed approach offers faster convergence than the standard geneticalgorithm. In this study, LAGA is applied to the 6-bus system and the IEEE 14-bus power system. Shunt capacitance, transformer taps and generator voltages are used as control system variables to minimize the system power loss. The output of the systems under investigation is compared with the output of a classical nonlinear optimization routine to evaluate the impact of LAGA technique to OPF. Moreover, to validate the performance of LAGA approach, a demonstration and comparison with earlier published results is presented. Simulation results are found to be effective and promising.
The problem of unequal facility location involves determining the location of a set of production equipment whose dimensions are different, as well as the interrelationships between each of them. This paper presents a...
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The problem of unequal facility location involves determining the location of a set of production equipment whose dimensions are different, as well as the interrelationships between each of them. This paper presents an efficient method for optimizing the problem of unequal facility layouts. In this method, the geneticalgorithm is improved and developed into an adaptive genetic algorithm. In this algorithm, the mutation operator is applied only when the similarity of chromosomes in each population reaches a certain level. This intelligence prevents jumps in situations where they are not needed and reduces computational time. In order to measure the performance of the proposed algorithm, its performance is compared with the performance of conventional geneticalgorithms and refrigeration simulators. Computational results show that the adaptive genetic algorithm is able to achieve higher-quality solutions.
An adaptive genetic algorithm with diversity-guided mutation, which combines adaptive probabilities of crossover and mutation was proposed. By means of homogeneous finite Markov chains, it is proved that adaptive gene...
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An adaptive genetic algorithm with diversity-guided mutation, which combines adaptive probabilities of crossover and mutation was proposed. By means of homogeneous finite Markov chains, it is proved that adaptive genetic algorithm with diversity-guided mutation and geneticalgorithm with diversity-guided mutation converge to the global optimum if they maintain the best solutions, and the convergence of adaptive genetic algorithms with adaptive probabilities of crossover and mutation was studied. The performances of the above algorithms in optimizing several unimodal and multimodal functions were compared. The results show that for multimodal functions the average convergence generation of the adaptive genetic algorithm with diversity-guided mutation is about 900 less than that of (adaptive) geneticalgorithm with adaptive probabilities and geneticalgorithm with diversity-guided mutation, and the adaptive genetic algorithm with diversity-guided mutation does not lead to premature convergence. It is also shown that the better balance between overcoming premature convergence and quickening convergence speed can be gotten.
Emergency mobilization alliance partner selection process is longitudinally choice of the value chain to achieve a task. Value of subtasks coefficient has been discussed. Depending on the difficulty of the problem and...
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ISBN:
(纸本)9783038353126
Emergency mobilization alliance partner selection process is longitudinally choice of the value chain to achieve a task. Value of subtasks coefficient has been discussed. Depending on the difficulty of the problem and analytical perspective, the model of emergency mobilization alliance partner selection is given to maximize the overall effectiveness of the emergency mobilization. The choice of partner selection using adaptive genetic algorithm is made and the comparison with other methods has been analyzed.
With the emergence of call center and its wide applications in enterprises, the schedule of agents becomes a core problem for reasonably deploying the human resources in call center and improving the productive force ...
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ISBN:
(纸本)9780769548111
With the emergence of call center and its wide applications in enterprises, the schedule of agents becomes a core problem for reasonably deploying the human resources in call center and improving the productive force of the call center. This study uses improved adaptive genetic algorithm (IAGA) to solve scheduling problem for a 24-hours call center. This paper builds a mathematical model to describe the constraints of the agent scheduling problem with the object for minimizing the gap between demand forecast and actual work volume in each time period. In order to solve the defects of existing search algorithm, this paper uses IAGA to get the optimal solution of the optimization problem. Satisfactorily, the simulation results have turned out that the method possesses a better solving effect in faster test speed.
In this paper, an enhanced evolutionary computing algorithm has been attempted for photo voltaic (PV) design parameter extraction using adaptive genetic algorithm. The I-V curve fitting approach has been used to find ...
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In this paper, an enhanced evolutionary computing algorithm has been attempted for photo voltaic (PV) design parameter extraction using adaptive genetic algorithm. The I-V curve fitting approach has been used to find optimal photovoltaic parameters Unlike single objective function based approaches, multiple objective functions including, least mean square error and Pearson residual error optimization are considered to fit the I-V curve. A cumulative fitness function is derived using both objectives that alleviate computational complexity, local minima and convergence. Importantly, Pearson residual error optimization (PRO) optimizes least mean square error (LSE) reduction while alleviating the probability of under/over-fitting that ensures optimal PV design parameter identification
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