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An evolutionary algorithm (EA)-assisted spatial sampling methodology is proposed to assist decision makers in sensor network (SN) deployments. We incorporated an interpolation technique with leave-one-out cross-validation (LOOCV) to assess the representativeness of a particular SN design. For the validation of our method, we utilized Tasmania's South Esk Hydrological Model developed by the Commonwealth...
With the explosive growth number of services in cloud computing environment, how to accurately and rapidly discover the services that can meet user's functional and nonfunctional requirements is a challenging subject. Aiming at issues of service inefficiencies and low precision in the existing service discovery methods, a model for service discovery based on functions and QoS clustering is proposed...
With the advance of computing and electronic technology, quantitative data, for example, continuous data (i.e., sequences of floating point numbers), become vital and have wide applications, such as for analysis of sensor data streams and financial data streams. However, existing association rule mining generally discover association rules from discrete variables, such as boolean data (‘O’ and ‘l’)...
Scheduling for the flexible job-shop is very important in both fields of production management and combinatorial optimization. However, it is quite difficult to achieve an optimal solution to this problem with traditional optimization approaches owing to the high computational complexity. The combining of several optimization criteria induces additional complexity and new problems. Particle swarm...
An adaptive discrete particle swarm optimization (PSO) method is presented to solve the generalized traveling salesman problem (GTSP). The generalized vertex employed to represent the problem, by which the GTSP and TSP can be handled in uniform style. An uncertain searching strategy and local searching techniques are also employed to accelerate the convergent speed. Numerical results show the effectiveness...
In order to obtain better attribute reduction of the continuous dataset, genetic algorithm and fuzzy rough set were used. By making use of this method, the discretization process of continuous attributes was avoided, and the information loss was reduced, the reduction was quickened, the decision dependency was raised in comparison with the traditional rough set. Here a simulation example was used...
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