Search Results: 1 - 10 of 100 matches for " "
All listed articles are free for downloading (OA Articles)
Page 1 /100
Display every page Item
An Improved Adaptive Genetic Algorithm for Job Scheduling Problem on Parallel Robots
Gohar Vahdati,Maryam Habibipour,Saeed Tousizadeh,Mahdi Yaghoubi
Majlesi Journal of Electrical Engineering , 2009, DOI: 10.1234/mjee.v3i3.105
Abstract: Minimizing mean tardiness by Job scheduling on parallel robots is very important in the scheduling domain. In this problem, there is a series of n-number independent jobs which are ready to be scheduled at the time of zero. Corresponding to each work, the processing time and duration date are determined. The aim of this approach is to find the order of jobs on the robots for minimizing the mean tardiness. This problem is in the class of NP-Hard combinational problems. Genetic algorithm is well known an effective tool for solving combinational optimization problems. In this study, an adaptive nonlinear genetic algorithm as well as two heuristic crossover and mutation operators are used. In the algorithm, there is a fitness function based on the mean tardiness. Therefore, the algorithm which can make the crossover and mutation probability adjusted adaptively and nonlinearly can avoid disadvantage such as premature convergence, low convergence speed and low stability. Experimental results demonstrate that the proposed genetic algorithm does not get stuck at a local optimum easily and yet it converges fast and is simple to implement.
Research on Grid Resources Schedule Based on an Adaptive Distribute Parallel Genetic Algorithm  [cached]
Guangyuan Liu,Jingjun Zhang,Sen Su
Journal of Computers , 2011, DOI: 10.4304/jcp.6.11.2285-2294
Abstract: In this paper an improved adaptive parallel genetic algorithm is proposed to solve problems of grid resources distribution and matching, comparing with the traditional genetic algorithms, a new adaptive selection operator is introduced, which can prevent the premature convergence of genetic algorithm efficiently. Besides, in this paper, the migration strategy of the parallel genetic algorithm can prevent the population trapped in the local extreme. And a pc-cluster containing eight computers is constructed to execute the coarse-grained parallel genetic algorithm and series genetic algorithm, and different scale resources and tasks are tested on the pc-cluster. Several examples are provided to be examined and the results illustrate that the proposed algorithm has higher global optimization capability, computational efficiency and stronger stability than the traditional genetic algorithm for the max time span. From these results, the parallel genetic algorithm reduced the searching time much more than series genetic algorithm for the same solutions. Moreover, compared with series genetic algorithm, the parallel genetic algorithm can get the more optimal solutions when the iteration is same.
Two improved normalized subband adaptive filter algorithms with good robustness against impulsive interferences  [PDF]
Yi Yu,Haiquan Zhao,Badong Chen,Zhengyou He
Computer Science , 2015,
Abstract: To improve the robustness of subband adaptive filter (SAF) against impulsive interferences, we propose two modified SAF algorithms with an individual scale function for each subband, which are derived by maximizing correntropy-based cost function and minimizing logarithm-based cost function, respectively, called MCC-SAF and LC-SAF. Whenever the impulsive interference happens, the subband scale functions can sharply drop the step size, which eliminate the influence of outliers on the tap-weight vector update. Therefore, the proposed algorithms are robust against impulsive interferences, and exhibit the faster convergence rate and better tracking capability than the sign SAF (SSAF) algorithm. Besides, in impulse-free interference environments, the proposed algorithms achieve similar convergence performance as the normalized SAF (NSAF) algorithm. Simulation results have demonstrated the performance of our proposed algorithms.
Set-membership versions of improved normalized subband adaptive filter algorithm for highly noisy system  [PDF]
Yi Yu,Haiquan Zhao,Badong Chen
Computer Science , 2015,
Abstract: In order to improve the performances of the recently-presented improved normalized subband adaptive filter (INSAF) algorithm for highly noisy system, this paper proposes a set-membership version of the INSAF algorithm (SM-INSAF) by exploiting the concept of the set-membership filtering. Apart from obtaining lower steady-state error under the same convergence rate, the proposed algorithm significantly reduces the overall computational complexity. In addition, to further reduce the steady-state error of the SM-INSAF, its smooth variant is developed by using smooth subband output errors to update the step sizes, called the SSM-INSAF algorithm. Simulation results in low signal-noise-ratio (SNR) environments, demonstrate the superiority of the proposed algorithms.
Parallel Dynamic Parameter Adaption of Adaptive Array Antennas Based on Nature Inspired Optimisation  [cached]
Gabriella Kókai,Martin B?hner,Tonia Christ,Hans Holm Frühauf
Journal of Computers , 2007, DOI: 10.4304/jcp.2.3.64-76
Abstract: The following paper describes and discusses the suitability of the particle swarm optimization(PSO), of the simulated annealing algorithm (SA) and of the genetic algorithm (GA) for the employment with blind adaptation of the directional characteristic of array antennas. By means of extensive simulations, it was confirmed that the suggested PSO and SA and the improved GA are able to follow dynamic changes in the environment. Based on these results a concept is discussed for a high-parallel optimizing procedure as distributed logic in Application-Specific Integrated Circuits (ASICs) or Field Programmable Gate Arrays (FPGAs). Thus an online procedure is available for time-critical applications of the adaptive beam forming.
Optimizing multi-instance neural networks based on an improved genetic algorithm

CAI Zi-xing,SUN Guo-rong,LI Mei-yi,

计算机应用 , 2005,
Abstract: In order to achieve higher predictive accuracy, an improved genetic algorithm for optimizing multi-instance neural networks was presented. Convergence rate was increased and premature convergence was overcome by means of local search operator, suppress operator and adaptive calculations of probabilities for operators. Some experiments on well-known test data show that multi-instance neural networks that are optimized by the improved genetic algorithm heighten significantly predictive accuracy and computational expensiveness of the algorithm is less than other algorithms.
Improved adaptive weight approach GA based rolling schedules multi-objective optimization of tandem cold rolling

LI Yong,LIU Jian-chang,WANG Yu,

控制理论与应用 , 2009,
Abstract: To select correct weighting factors for the weight-sum multi-objective method, we propose an improved adaptive weight-selection approach. On the basis of the genetic algorithm(GA), this approach is applied to optimize the multi-objective rolling schedules in a tandem cold rolling. In the optimization process of rolling schedules, the power distribution, the rolling energy consumption and the slip rate are selected as objective functions from them the multiobjective model of rolling schedules is established. Applying the improved adaptive weight approach GA to optimize rolling schedules for strips with different specifications, we reduce the values of the above three objective functions simultaneously, in comparison with the conventional rolling schedules. It also provides better pertinence and faster convergence for objects of higher priority than those of the adaptive weight approach GA.
Parallel simulated annealing genetic algorithm for optimizing BP neural network

LIU Yue-e,HE Dong-jian,LI Zheng-rong,

计算机应用 , 2006,
Abstract: A Parallel Simulated Annealing Genetic Algorithm(PSAGA) was given for the optimization of 3 levels BP neural network.Simulated annealing(SA) method was applied in fitness scaling,genetic operator was improved by ranking selection which copied the fittest,heuristic crossover and multi nonuniform mutation,and SA was used as the state generator.The idea of parallel evolution was combined into PSAGM.Simulation to recognition of English letters proved PSAGM was better than simple genetic algorithm in global search,local search and speed of convergence.
Adaptive SAGA Based on Mutative Scale Chaos Optimization Strategy  [PDF]
Haichang Gao,Boqin Feng,Yun Hou,Bin Guo
Information Technology Journal , 2006,
Abstract: A hybrid adaptive SAGA based on mutative scale chaos optimization strategy (CASAGA) is proposed to solve the slow convergence, incident getting into local optimum characteristics of the Standard Genetic Algorithm (SGA). The algorithm combined the parallel searching structure of Genetic Algorithm (GA) with the probabilistic jumping property of Simulated Annealing (SA), also used adaptive crossover and mutation operators. The mutative scale Chaos optimization strategy was used to accelerate the optimum seeking. Compared with SGA and MSCGA on some complex function optimization and several TSP combination optimization problems, the CASAGA improved the global convergence ability and enhanced the capability of breaking away from local optimal solution.
Parallel strategies for a multi-criteria GRASP algorithm
Vianna, Dalessandro Soares;Arroyo, José Elias Claudio;Vieira, Pedro Sampaio;Azeredo, Thiago Ribeiro de;
Produ??o , 2007, DOI: 10.1590/S0103-65132007000100006
Abstract: this paper proposes different strategies of parallelizing a multi-criteria grasp (greedy randomized adaptive search problem) algorithm. the parallel grasp algorithm is applied to the multi-criteria minimum spanning tree problem, which is np-hard. in this problem, a vector of costs is defined for each edge of the graph and the goal is to find all the efficient or pareto optimal spanning trees (pareto-optimal solutions). each process finds a subset of efficient solutions. these subsets are joined using different strategies to obtain the final set of efficient solutions. the multi-criteria grasp algorithm with the different parallel strategies are tested on complete graphs with n = 20, 30 and 50 nodes and r = 2 and 3 criteria. the computational results show that the proposed parallel algorithms reduce the execution time and the results obtained by the sequential version were improved.
Page 1 /100
Display every page Item

Copyright © 2008-2017 Open Access Library. All rights reserved.