Research On Particle Swarm Optimization And Its Engineering Application

Particle Swarm Optimization (PSO) algorithm is a typical swarm intelligence optimization algorithm. Comparing with traditional optimization methods, PSO algorithm has many advantages, such as simple structure, less parameter, easy to implement and strong global optimization capability. However, the theoretical basis of PSO is still far from mature. PSO has problems of premature convergence and easy to fall into local optimum trap, there is still a lot of room for improvement when applying PSO to engineering practice.The paper proposes several distinct improved algorithms, including the parameters control strategy improvement and new implementation framework. These improved PSO versions are appled into PID tunning and array antenna pattern synthesis.PSO is an intelligent optimization technique based on swarm intelligence, its swarm convergence directly relations with the optimization performance. After analyzing the convergence performance of PSO, the paper improves PSO in the aspects of parameter control strategy improvement and enhancing the balanced search capacity of algorithm:(1) The paper proposes an improved PSO on the basis of convergence control, so the swarm convergence becomes controllable. When swarm is converged in a local optimum trap, the algorithm makes swarm diverge again.In this way, the global search ability of the algorithm can be improved a lot.(2) The paper establishes an algorithm framework based on inheritance learning strategy, and single PSO process is incorporates into this framework as a basic unit. Under the new framework, several parallel PSO processes construe one cycle. The better part of the results of last cycle and the randomly generated results composed the whole initial swarm positions of next cycle. New algorithm has greatly reduced the impact of randomness, and has good performance in multidimensional optimization problems. The improved algorithm also has great flexibility, and the algorithm frame is easy to apply in other intelligent optimization algorithms.In the control system, PID parameter tunning is a classical research direction. If3parameters of PID controller are deemed as promising optimization variants, and the controller’s response is used as fitness value, then optimization algorithm can be applied to handle PID parameter tunning. To solve this problem, the paper makes relevant studies:(1) The paper establishes PSO optimization model for Maglev train controller PID tunning and the improved PSO algorithm is used. The simulation and experimental results show that PID tunning by PSO algorithm has good feasibility and applicability.(2) In practical application, sometimes it is impossible to obtain accurate model for the control objects, so it is hard to tune PID parameter and the feasible way is to use the experience tuning method. To improve this situation, PSO algorithm is applied in model identification of paver’s control system. The identification paver model is used to PID tunning and the experimental results show that this method is very effective.Array antenna pattern synthesis is a key application field of intelligent algorithm. The paper also studies this issue, mainly consisting of pattern synthesis with continuous variants and discrete variants. The details are as below:(1) As for multi-objective pattern optimization, the paper proposes the strategy of stage fitness function. For new strategy, different indicators increase by stages, and it is much easier to balance each optimization objective in different stage. Compared with fixed fitness function strategy, new strategy can avoid a certain indicators being satisfied convergence conditions while the others far from the convergence conditions. As a result, local optimum trap’s depth becomes shallower in the solution space, which is helpful for the PSO swarm to escape the local optimum trap and can help PSO swarm realize global convergence. In pattern optimization applications, the fitness function can be divided into two or more stages.(2) The paper applies stagnation detecting PSO algorithm and PSO algorithm based on convergence control into uniform linear array’s low sidelobe pattern synthesis. The simulation results show that these two improved algorithms can effectively applicate in multi-null and low sidelobe pattern synthesis.(3) The paper applies ILPSO into unequal spaced linear array’s low sidelobe pattern synthesis. Simulation results show that ILPSO obtains results equal to or better than the results of the latest domestic and foreign researches by using less computation.(4) In order to solve the discrete optimization problems in antenna array pattern synthesis, the paper proposes an optimization strategy based on the combination of PSO algorithm and particle position rounding. Therefore, it effectively handles the pattern synthesis of4bit digital phase shifter array, the pattern synthesis of thinned linear array, and the pattern synthesis of non-uniform thinning linear array. Simulation results show that this strategy can effectively apply real number PSO algorithm into the discrete optimization in pattern synthesis, and the optimization results are better than existed binary PSO and other intelligent optimization algorithms. In order to handle the pattern synthesis of large thinned plannar array with thinned element proportion constraint, the probability adjustment strategy is added, and the optimization results are superior to results of other discrete particle swarm optimization algorithms.In the end, the paper makes final conclusion and proposes further research directions.