Modelling and realisation of multi-sensors mission planning problem based on fuzzy chance-constrained bi-level programming in anti-TBM combat

The Multi-Sensors Mission Planning MSMP optimisation model based on Fuzzy Chance-Constrained Bi-Level Programming FCCBLP is presented on the basis of analysing the deficiency of the existing MSMP model in anti-TBM combat. Firstly, employing the mission reliability and detecting advantage as the upper and the lower objective function of the model based on taking the model constraints in complex battlefield environment into consideration, respectively. Secondly, particle coding scheme with hierarchical structure for multi-constrained bi-level MSMP problem is constructed. On this basis, an Improved Fuzzy Particle Swarm Optimisation IFPSO algorithm is proposed with fuzzy simulation technique and cloud self-adaptive mutation operator. Finally, the simulation results show that the proposed algorithm has a strong global searching ability and fast convergence speed which meet the high requirements about the timeliness of the large-scale MSMP problem.

[1]  Zhang Lan Quantum-behaved Particle Swarm Optimization for Solving Bi-level Programming Problem , 2013 .

[2]  Zhongping Wan,et al.  A hybrid intelligent algorithm by combining particle swarm optimization with chaos searching technique for solving nonlinear bilevel programming problems , 2013, Swarm Evol. Comput..

[3]  Hu Weidong Space-air Resources Multi-phase Cooperation Task Planning Approach Based on Heterogeneous MAS Model , 2013 .

[4]  Xu Xue-feng Research on Conception Representation of Operational Mission Break , 2007 .

[5]  Jing Jie,et al.  Three-dimensional fluorescence spectra model optimisation for water quality analysis based on particle swarm optimisation , 2014, Int. J. Wirel. Mob. Comput..

[6]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[7]  Tong Jun Solving sensor-target assignment problem based on cooperative memetic PSO algorithm , 2013 .

[8]  Xu Xiao-lai,et al.  Cooperative mission assignment optimization of unmanned combat aerial vehicles based on bilevel programming , 2010 .

[9]  Nima Hamta,et al.  A hybrid PSO algorithm for a multi-objective assembly line balancing problem with flexible operation times, sequence-dependent setup times and learning effect , 2013 .

[10]  Fan Cheng-l Particle swarm optimization and variable neighborhood search algorithm with convergence criterions , 2014 .

[11]  Luo De-xiang Adaptive particle swarm optimization algorithm based on cloud theory , 2009 .

[12]  Ibrahim A. Baky,et al.  TOPSIS for bi-level MODM problems , 2013 .

[13]  Xiong Jie,et al.  Application of Fuzzy Chance Constrained Programming in Optimal Distribution of Radar Jamming Resource , 2010 .

[14]  Christoph Stasch,et al.  New Generation Sensor Web Enablement , 2011, Sensors.

[15]  Chen Yu-zhong Research on dynamic alliance of task allocation and its algorithm in wireless sensor network , 2009 .

[16]  Saman K. Halgamuge,et al.  Sensor Scheduling For Target Tracking Using Particle Swarm Optimization , 2006, 2006 IEEE 63rd Vehicular Technology Conference.

[17]  Jianchao Zeng,et al.  Research on task allocation of multi-target search with swarm robots , 2014, Int. J. Wirel. Mob. Comput..