Multi-objective data clustering is an important issue in data mining, and the realization of data clustering using the multiobjective optimization technique is a significant topic. A combinatorial multi-objective pige...Multi-objective data clustering is an important issue in data mining, and the realization of data clustering using the multiobjective optimization technique is a significant topic. A combinatorial multi-objective pigeon inspired optimization(CMOPIO)with ring topology is proposed to solve the clustering problem in this paper. In the CMOPIO, a delta-locus based coding approach is employed to encode the pigeons. Thus, the length of pigeon representation and the dimension of the search space are significantly reduced. Thereby, the computational load can be effectively depressed. In this way, the pigeon inspired optimization(PIO) algorithm can be discretized with an auxiliary vector to address data clustering. Moreover, an index-based ring topology with the ability of contributing to maintain flock diversity is adopted to improve the CMOPIO performance. Comparative simulation results demonstrate the feasibility and effectiveness of our proposed CMOPIO for solving data clustering problems.展开更多
In this article, the formation control of quad-rotor unmanned aerial vehicle(UAV) via pigeon inspired optimization(PIO) is designed. The nonlinear mathematical model of the quad-rotor UAV is used by applying algebraic...In this article, the formation control of quad-rotor unmanned aerial vehicle(UAV) via pigeon inspired optimization(PIO) is designed. The nonlinear mathematical model of the quad-rotor UAV is used by applying algebraic graph theory and matrix analysis. A high order consistent formation control algorithm with fixed control topology is designed by using a position deviation matrix to describe its formation. To control the attitude of quad-rotor UAVs, it is difficult to obtain a set of optimal solutions, and hence a PIO based algorithm with variable weight hybridization is proposed. The algorithm is mainly composed of two parts. First, according to the distance between the particles in the iterative process, the inertia weight is dynamically changed,and the coefficient is adjusted to control the degree of influence on its inertia weight. Second, the overall scenario is designed by using MATLAB based simulations which show that the formation control of the quad-rotor UAV is achieved with the help of PIO.展开更多
We investigate a distributed game strategy for unmanned aerial vehicle(UAV)formations with external disturbances and obstacles.The strategy is based on a distributed model predictive control(MPC)framework and Levy fli...We investigate a distributed game strategy for unmanned aerial vehicle(UAV)formations with external disturbances and obstacles.The strategy is based on a distributed model predictive control(MPC)framework and Levy flight based pigeon inspired optimization(LFPIO).First,we propose a non-singular fast terminal sliding mode observer(NFTSMO)to estimate the influence of a disturbance,and prove that the observer converges in fixed time using a Lyapunov function.Second,we design an obstacle avoidance strategy based on topology reconstruction,by which the UAV can save energy and safely pass obstacles.Third,we establish a distributed MPC framework where each UAV exchanges messages only with its neighbors.Further,the cost function of each UAV is designed,by which the UAV formation problem is transformed into a game problem.Finally,we develop LFPIO and use it to solve the Nash equilibrium.Numerical simulations are conducted,and the efficiency of LFPIO based distributed MPC is verified through comparative simulations.展开更多
基金supported by the Science and Technology Innovation 2030-Key Project of “New Generation Artificial Intelligence”(Grant No.2018AAA0102303)the National Natural Science Foundation of China(Grant Nos. 91948204,U1913602.and U19B2033)。
文摘Multi-objective data clustering is an important issue in data mining, and the realization of data clustering using the multiobjective optimization technique is a significant topic. A combinatorial multi-objective pigeon inspired optimization(CMOPIO)with ring topology is proposed to solve the clustering problem in this paper. In the CMOPIO, a delta-locus based coding approach is employed to encode the pigeons. Thus, the length of pigeon representation and the dimension of the search space are significantly reduced. Thereby, the computational load can be effectively depressed. In this way, the pigeon inspired optimization(PIO) algorithm can be discretized with an auxiliary vector to address data clustering. Moreover, an index-based ring topology with the ability of contributing to maintain flock diversity is adopted to improve the CMOPIO performance. Comparative simulation results demonstrate the feasibility and effectiveness of our proposed CMOPIO for solving data clustering problems.
文摘In this article, the formation control of quad-rotor unmanned aerial vehicle(UAV) via pigeon inspired optimization(PIO) is designed. The nonlinear mathematical model of the quad-rotor UAV is used by applying algebraic graph theory and matrix analysis. A high order consistent formation control algorithm with fixed control topology is designed by using a position deviation matrix to describe its formation. To control the attitude of quad-rotor UAVs, it is difficult to obtain a set of optimal solutions, and hence a PIO based algorithm with variable weight hybridization is proposed. The algorithm is mainly composed of two parts. First, according to the distance between the particles in the iterative process, the inertia weight is dynamically changed,and the coefficient is adjusted to control the degree of influence on its inertia weight. Second, the overall scenario is designed by using MATLAB based simulations which show that the formation control of the quad-rotor UAV is achieved with the help of PIO.
基金Project supported by the Science and Technology Innovation 2030 Key Project of“New Generation Artificial Intelligence,”China(No.2018AAA0100803)the National Natural Science Foundation of China(Nos.T2121003,U1913602,U20B2071,91948204,and U19B2033)。
文摘We investigate a distributed game strategy for unmanned aerial vehicle(UAV)formations with external disturbances and obstacles.The strategy is based on a distributed model predictive control(MPC)framework and Levy flight based pigeon inspired optimization(LFPIO).First,we propose a non-singular fast terminal sliding mode observer(NFTSMO)to estimate the influence of a disturbance,and prove that the observer converges in fixed time using a Lyapunov function.Second,we design an obstacle avoidance strategy based on topology reconstruction,by which the UAV can save energy and safely pass obstacles.Third,we establish a distributed MPC framework where each UAV exchanges messages only with its neighbors.Further,the cost function of each UAV is designed,by which the UAV formation problem is transformed into a game problem.Finally,we develop LFPIO and use it to solve the Nash equilibrium.Numerical simulations are conducted,and the efficiency of LFPIO based distributed MPC is verified through comparative simulations.
