Current Issue : January - March Volume : 2015 Issue Number : 1 Articles : 6 Articles
Metamodels have been widely used in engineering design to facilitate analysis and optimization of complex systems that involve\ncomputationally expensive simulation programs.The accuracy of metamodels is directly related to the experimental designs used.\nOptimal Latin hypercube designs are frequently used and have been shown to have good space-filling and projective properties.\nHowever, the high cost in constructing them limits their use. In this paper, a methodology for creating novel Latin hypercube designs\nvia translational propagation and successive local enumeration algorithm (TPSLE) is developed without using formal optimization.\nTPSLE algorithm is based on the inspiration that a near optimal Latin Hypercube design can be constructed by a simple initial block\nwith a few points generated by algorithm SLE as a building block. In fact, TPSLE algorithm offers a balanced trade-off between the\nefficiency and sampling performance. The proposed algorithm is compared to two existing algorithms and is found to be much\nmore efficient in terms of the computation time and has acceptable space-filling and projective properties....
Aiming at the static task scheduling problems in heterogeneous environment, a heuristic task scheduling algorithm named\nHCPPEFT is proposed. In task prioritizing phase, there are three levels of priority in the algorithm to choose task. First, the critical\ntasks have the highest priority, secondly the tasks with longer path to exit task will be selected, and then algorithm will choose tasks\nwith less predecessors to schedule. In resource selection phase, the algorithm is selected task duplication to reduce the interresource\ncommunication cost, besides forecasting the impact of an assignment for all children of the current task permits better decisions to\nbe made in selecting resources. The algorithm proposed is compared with STDH, PEFT, and HEFT algorithms through randomly\ngenerated graphs and sets of task graphs. The experimental results show that the new algorithm can achieve better scheduling\nperformance....
Modification of the intensification and diversification approaches in the recently developed cuckoo search algorithm (CSA)\nis performed. The alteration involves the implementation of adaptive step size adjustment strategy, and thus enabling faster\nconvergence to the global optimal solutions.The feasibility of the proposed algorithm is validated against benchmark optimization\nfunctions, where the obtained results demonstrate a marked improvement over the standard CSA, in all the cases....
Evolutionary algorithms have been widely used to solve large and complex optimisation problems. Cultural algorithms (CAs) are\nevolutionary algorithms that have been used to solve both single and, to a less extent,multiobjective optimisation problems. In order\nto solve these optimisation problems, CAs make use of different strategies such as normative knowledge, historical knowledge,\ncircumstantial knowledge, and among others. In this paper we present a comparison among CAs that make use of different\nevolutionary strategies; the first one implements a historical knowledge, the second one considers a circumstantial knowledge, and\nthe third one implements a normative knowledge. These CAs are applied on a biobjective uncapacitated facility location problem\n(BOUFLP), the biobjective version of the well-known un capacitated facility location problem. To the best of our knowledge, only\nfew articles have applied evolutionary multiobjective algorithms on the BOUFLP and none of those has focused on the impact\nof the evolutionary strategy on the algorithm performance. Our biobjective cultural algorithm, called BOCA, obtains important\nimprovements when compared to other well-known evolutionary biobjective optimisation algorithms such as PAES and NSGA-II.\nThe conflicting objective functions considered in this study are cost minimisation and coverage maximisation. Solutions obtained\nby each algorithm are compared using a hyper volume S metric....
A precise mathematical model plays a pivotal role in the simulation, evaluation, and optimization of photo voltaic (PV) power\nsystems. Different from the traditional linear model, them odel of PV module has the features of non linearity and multi parameters.\nSince conventional methods are incapable of identifying the parameters of PV module, an excellent optimization algorithm is\nrequired. Artificial fish swarm algorithm (AFSA), originally inspired by the simulation of collective behavior of real fish swarms,\nis proposed to fast and accurately extract the parameters of PV module. In addition to the regular operation, a mutation operator\n(MO) is designed to enhance the searching performance of the algorithm. The feasibility of the proposed method is demonstrated\nby various parameters of PV module under different environmental conditions, and the testing results are compared with other\nstudied methods in terms of final solutions and computational time. The simulation results show that the proposed method is\ncapable of obtaining higher parameters identification precision....
The objective of this study is to design rubber bushing at desired level of stiffness characteristics in order to achieve the ride quality\nof the vehicle. A differential evolution algorithm based approach is developed to optimize the rubber bushing through integrating\na finite element code running in batch mode to compute the objective function values for each generation. Two case studies were\ngiven to illustrate the application of proposed approach. Optimum shape parameters of 2D bushing model were determined by\nshape optimization using differential evolution algorithm....
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