Application of improved quantum particle swarm optimization algorithm in power network planning

In order to solve the conflicting objectives in power network planning, various evolutionary methods to multi-objective optimization have been developed, however, some studies of different methods are often restricted to the quantitatively and the approaches of Pareto front. In this paper the potential and effectiveness of Pareto front-based improved quantum particle swarm algorithm (IQPSO) for solving a real-world power system multi-objective optimization problem are comprehensively evaluated and discussed. IQPSO adopts the non-dominated storing relation method for solutions population and a new population diversity preserving strategy is used which is based on the turbulence pareto max-min distance, by using convergent factor K to accelerate the convergence rate of the particle who jump out the local optimal. Particularly, nondominated sorting genetic algorithm (NSGA) multi-objective evolutionary algorithm (MOEA) have been developed and successfully applied to the network planning problem, these methods have been examined and applied to the standard IEEE 33-bus test system. Compared to NSGA and MOEA methods, the results demonstrate the superiority of the IQPSO as a promising method to solve mulit-objective power network planning problem.