Three Heuristic Optimization Algorithms And Their Applications In Several Control Problems

Heuristic optimization algorithms and their applications have obtained widespread attention in recent years and they have been applied in many practical fields.Some of them and their applications are studied including differential particle swarm optimization algorithm,harmony search algorithm and learning search algorithm.And the main work is as follows.Multi-population random differential particle swarm optimization(MPPSO)is proposed.The proposed algorithm randomly divides swarm into several groups and uses the attraction probability to determine the possible search direction.When the expected optimization effect is not obvious,this algorithm uses random differential evolution to generate new solution in the sub-population,which aims to increase the randomness and diversity of searching direction.Meanwhile,a new constraint handling method is proposed to dynamically divide population,and a part of particles implements velocity and position update for improving search speed.Finally,the proposed algorithm is applied to numerical optimization problem and LQ control problem.A new design method of state feedback controller is proposed for LQ linear time-invariant system under a finite-time terminal.In this method,LQ control problem is transformed into constrained optimization problem,and a feedback gain matrix is designed by using heuristic intelligent optimization algorithm.In order to improve the control effect and reduce the conservativeness,the design method of the piecewise controller and the periodic switching controller are proposed.Simulations and experiments are presented to illustrate the correctness and effectiveness of the proposed method.An improved global particle swarm optimization is proposed.Based on a balance between exploitation and exploration ability,global neighborhood search strategy and disturbance strategy is proposed to reduce the possibility that algorithm fall into local minima.Meanwhile,a perturbation operation with probabilities is implemented in the global best particle,which aims at accelerating the convergence speed.The test results demonstrated the effectiveness of IGPSO algorithm in terms of accuracy,convergence speed,and nonparametric statistical significance when compared with other state-of-the-art intelligent algorithms(PSO,APSO,CLPSO,DE,JADE,GHS,GABC,CS).The algorithm is employed in the robust pole placement problems,and it is easy to implement arbitrary pole placement for linear control systems,and the convex conversion process in conventional condition number optimizations is not needed.The simulation result demonstrates the better robustness of the close-loop system gained by the proposed algorithm.A hybrid index of fuel-time on the basis of C-W equations was built for the spacecraft optimal rendezvous problem,and an amended harmony search(AHS)algorithm was proposed to solve this problem.In the AHS algorithm.the guidance of the current global best harmony was utilized and the pitch adjusting operation was replaced,resulting in the enhancement of the balance between the global search and local search.The PAR was dynamically adjusted to adapt the search process of algorithm.Several optimal rendezvous cases were used to test the effectiveness of AHS algorithm,and it was verified by the numerical results that correct satisfied results could be obtained with the proposed AHS algorithm,which is better than that of the other algorithms.A modified one with global crossover(MHSgc)was proposed.In the MHSgc,the collaborative improvisation of multiple harmony memory was applied.A neighbor learning strategy adjusting,and thus the population diversity was increased.The global crossover operation was introduced into the MHSgc algorithm to avoid getting stuck into local minima.Simulations were carried out based on several benchmark functions.The results show that the proposed algorithm outperforms eight intelligent algorithms(IHS,GHS,NGHS,EHS,ITHS,MPSO,DE,ABC).The algorithm is applied to cost control of transmission congestion management in electricity systems.A new meta-heuristic intelligent optimization algorithm is proposed and named learning search algorithm(LSA).LSA designs positive pattern,all the students actively learn from the excellent student.LSA designs negative pattern.Every student absorbs the merit of the worst student to enhance the comprehensiveness of study.These two patterns are combined to enhance LSA exploration competence.Several classic benchmark functions are carried out to be tested and the result demonstrated that the proposed algorithm has better optimization potential than the other some promising algorithms.Finally,the proposed algorithm is applied to numerical optimization problem and switching tracking control of a class of linear systems subject to given transient performances of output problem.For the linear systems which can not use a single output feedback controller to achieve the output tracking of the transient performance,the periodic switching strategy of the controller is presented.The switching strategy of the controller and the design method of the output feedback controller are given based on a learning search algorithm(LSA).Simulation results show that the method presented in this chapter is better than the existing methods in the literature,which can not only improve the control effect,but also facilitate the realization of the project.