A Novel Multiobjective Particle Swarm Optimization Combining Hypercube and Distance

The multiobjective optimization problems are a common problem in various fields the real society. Therefore, solving one of important studied by many researchers recent years. From research years, it can be seen that there is still lot room for development particle swarm problems. This paper proposes novel combining hypercube and distance, called HDMOPSO. velocity update part this uses combination distance. In order to prevent algorithm from falling into local optimum, also nonlinear decreasing opposite mutation strategy, which enables particles explore more area. Finally, control strategy used external archive improve convergence diversity algorithm. has been simulated 22 test compared with algorithms (MOPSOs) evolutionary (MOEAs). results show HDMOPSO effectively diversity, so an effective improvement.