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Particle swarm optimization (PSO) is swarm-based stochastic optimization originating from artificial life and evolutionary computation. The algorithm completes the optimization through following the personal best solution of each particle and the global best value of the whole swarm. This paper improved the standard PSO's evolution equation on the(More)
—Wireless multimedia sensor networks (WMSNs) differ from the traditional wireless sensor networks (WSNs) due to their characteristic of directivity. In this work, by analyzing the virtual potential field-based algorithm, which can not really describe the coverage overlap of the node, we propose an electrostatic field-based coverage-enhancing algorithm(More)
Due to the design knowledge discrepancy during collaborative design, conflicts can be revealed from the process of collaborative design decision. A critical element of collaborative design would be conflict resolution. The conflict resolution is correlative with both of the knowledge granulation and specific method provided. In this paper, granularity is(More)
The use of evolutionary algorithms to solve unconstraint multi-objective problems (MOPs) has attracted much attention recently. However, research on constraint multi-objective algorithms is relatively less. The authors introduce a novel evolutionary paradigm of artificial physics optimization (APO) into constraint multi-objective optimization domain and(More)
Through mechanism analysis of simple genetic algorithm(SGA),every genetic operator can be considered as a linear transform. So some disadvantages of SGA may be solved if genetic operators are modified to nonlinear transforms. According to the above method, nonlinear genetic algorithm is introduced, and different nonlinear genetic operators with some(More)