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A Hybrid Computational Intelligence Approach for the VRP Problem
Gang PENG,Kehan ZENG,Xiong YANG 한국정보기술융합학회 2013 JoC Vol.4 No.2
PGQ, a novel hybrid computational intelligence approach, in which Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and quantum computation are integrated, is proposed to solve the Vehicle Routing Problem (VRP). In PSO, a quantum approach called QUP is proposed to update the particles. GA operators are employed to improve population quality. The simulation results indicate that the PGQ algorithm is very effective and is better than simple PSO and GA as well as PSO and GA mixed algorithm.
Occluded and Low Resolution Face Detection with Hierarchical Deformable Model
Xiong Yang,Gang Peng,Zhaoquan Cai,Kehan Zeng 한국정보기술융합학회 2013 JoC Vol.4 No.2
This paper presents a hierarchical deformable model for robust human face detection, especially with occlusions and under low resolution. By parsing, we mean inferring the parse tree (a configuration of the proposed hierarchical model) for each face instance. In modeling, a three-layer hierarchical model is built consisting of six nodes. For each node, an active basis model is trained, and their spatial relations such as relative locations and scales are modeled using Gaussian distributions. In computing, we run the learned active basis models on testing images to obtain bottom-up hypotheses, followed by explicitly testing the compatible relations among those hypotheses to do verification and construct the parse tree in a top-down manner. In experiments, we test our approach on CMU+MIT face test set with improved performance obtained.