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An adaptive genetic algorithm for solving bilevel linear programming problem

Applied Mathematics and Mechanics(English Edition) 2007年第12期28卷 1605-1612页

作者：王广民王先甲万仲平贾世会 School of Management China University of Geosciences Institute of Systems Engineering Wuhan University School of Mathematics and Statistics Wuhan University School of Science Wuhan University of Science and Technology

Bilevel linear programming, which consists of the objective functions of the upper level and lower level, is a useful tool for modeling decentralized decision problems. Various methods are proposed for solving this problem. Of all the algorithms, the ge- netic algorithm is an alternative to conventional approaches to find the solution of the bilevel linear programming. In this paper, we describe an adaptive genetic algorithm for solving the bilevel linear programming problem to overcome the difficulty of determining the probabilities of crossover and mutation. In addition, some techniques are adopted not only to deal with the difficulty that most of the chromosomes maybe infeasible in solving constrained optimization problem with genetic algorithm but also to improve the efficiency of the algorithm. The performance of this proposed algorithm is illustrated by the examples from references.

关键词： bilevel linear programming genetic algorithm fitness value adaptive operator probabilities crossover and mutation

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Research on Rao-Blackwillised Particle Filter SLAM algorithm based on QPSO

Research on Rao-Blackwillised Particle Filter SLAM algorithm...

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第37届中国控制会议

作者： MengYuan Chen ChengYuan Bian Yong Jian Wu Department of Precision Machinery and Precision Instrumentation University of Science and Technology of China Key Lab of Electric Drive and Control of Anhui Province Anhui Polytechnic University

In order to solve some problems of the traditional Rao-Blackwillised particle filter（RBPF）,which the low precision of the proposed distribution and the particle degeneracy and loss of diversity in the resampling process,a Rao-Blackwillised Particle Filter SLAM algorithm based on Quantum-behaved Particle Swarm Optimization（QPSO） is *** fusion of robot motion model and observation model is proposed as a hybrid proposal distribution to improve the accuracy of the proposed distribution;The introduction of QPSO algorithm update the pose of particles in the process of resampling,according to the weight measurement of particle type adaptive crossover and mutation operation,optimizing and adjusting the particle set,effectively prevent the particle degradation and maintain the diversity of *** algorithm not only carry out in the Matlab simulation,and the use of Voyageer-Ⅱ mobile robot in robot operating system（ROS） to realize the actual *** results show that the proposed algorithm can accurately estimate the position and pose of the robot and a high precision map,and error and running time are also greatly reduced.

关键词： RBPF simultaneous localization and map building proposed distribution QPSO crossover and mutation

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