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With the development of computer technology, more and more intelligent algorithms in the solution of different problems in many aspects of society are extensively used. Calculation of the minimum values of the function optimization and finding the optimal solution of combinatorial optimization problems in certain space are the two typical problems. In the fields of science and engineering, many optimization problems are constrained with different conditions. Due to the existence of constraints, the optimization problems are more difficult than the unconstrained optimization problem. Therefore, research on how to make the objective function find the optimal solution in the feasible region is of great significance As an important intelligent algorithm, particle swarm optimization (PSO) is widely used in the calculation of the optimal solution of the constraint problems. But due to the disadvantages of the PSO, its search efficiency is quite low when the particle is close to the optimal value. On the other side, it is easy to search the local optimal solution but difficult to get the global optimal solution. So, it is necessary to modify the algorithm to improve the performance. In the paper, we modify the algorithm and propose a modified algorithm (M-PSO). With the simulation, the results show the validity of the algorithm.

Investigation of Modified PSO Algorithm used in the Solution of Problems with Constraints and its Application