Current Issue : April - June Volume : 2014 Issue Number : 2 Articles : 4 Articles
Multiobjective optimization problem (MOP) is an important and challenging topic in the fields of industrial design and scientific\r\nresearch.Multi-objective evolutionary algorithm (MOEA) has proved to be one of the most efficient algorithms solving the multiobjective\r\noptimization. In this paper, we propose an entropy-based multi-objective evolutionary algorithm with an enhanced\r\nelite mechanism (E-MOEA), which improves the convergence and diversity of solution set in MOPs effectively. In this algorithm,\r\nan enhanced elite mechanism is applied to guide the direction of the evolution of the population. Specifically, it accelerates the\r\npopulation to approach the true Pareto front at the early stage of the evolution process. A strategy based on entropy is used to\r\nmaintain the diversity of population when the population is near to the Pareto front. The proposed algorithm is executed on widely\r\nused test problems, and the simulated results show that the algorithm has better or comparative performances in convergence and\r\ndiversity of solutions compared with two state-of-the-art evolutionary algorithms: NSGA-II, SPEA2 and the MOSADE....
We apply an interactive genetic algorithm (iGA) to generate product recommendations. iGAs search for a single optimum point\r\nbased on a user�s Kansei through the interaction between the user and machine. However, especially in the domain of product\r\nrecommendations, theremay be numerous optimum points. Therefore, the purpose of this study is to develop a new iGA crossover\r\nmethod that concurrently searches for multiple optimum points for multiple user preferences. The proposed method estimates the\r\nlocations of the optimumarea by a clustering method and then searches for the maximumvalues of the area by a probabilisticmodel.\r\nTo confirm the effectiveness of this method, two experiments were performed. In the first experiment, a pseudouser operated an\r\nexperiment system that implemented the proposed and conventional methods and the solutions obtained were evaluated using a\r\nset of pseudomultiple preferences.With this experiment, we proved that when there aremultiple preferences, the proposed method\r\nsearches faster andmore diversely than the conventional one.Thesecond experiment was a subjective experiment. This experiment\r\nshowed that the proposed method was able to search concurrently for more preferences when subjects had multiple preferences....
The particle swarm optimization (PSO) is a recently invented evolutionary computation technique which is gaining popularity\r\nowing to its simplicity in implementation and rapid convergence. In the case of single-peak functions, PSO rapidly converges to the\r\npeak; however, in the case of multimodal functions, the PSO particles are known to get trapped in the local optima. In this paper,\r\nwe propose a variation of the algorithm called parallel swarms oriented particle swarm optimization (PSO-PSO) which consists of\r\namultistage and a single stage of evolution. In themulti-stage of evolution, individual subswarms evolve independently in parallel,\r\nand in the single stage of evolution, the sub-swarms exchange information to search for the global-best. The two interweaved stages\r\nof evolution demonstrate better performance on test functions, especially of higher dimensions.The attractive feature of the PSOPSO\r\nversion of the algorithm is that it does not introduce any new parameters to improve its convergence performance.The strategy\r\nmaintains the simple and intuitive structure as well as the implemental and computational advantages of the basic PSO....
Drought forecasts can be an effective tool for mitigating some of the more adverse consequences of drought. Data-driven models\r\nare suitable forecasting tools due to their rapid development times, as well as minimal information requirements compared to the\r\ninformation required for physically based models. This study compares the effectiveness of three data-driven models for forecasting\r\ndrought conditions in the Awash River Basin of Ethiopia. The Standard Precipitation Index (SPI) is forecast and compared using\r\nartificial neural networks (ANNs), support vector regression (SVR), and wavelet neural networks (WN). SPI 3 and SPI 12 were\r\nthe SPI values that were forecasted. These SPI values were forecast over lead times of 1 and 6 months. The performance of all the\r\nmodels was compared using RMSE, MAE, and R2. The forecast results indicate that the coupled wavelet neural network (WN)\r\nmodels were the best models for forecasting SPI values over multiple lead times in the Awash River Basin in Ethiopia....
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