Particle Swarm Collaborative Practicability Algorithm Based on Partial Differential Exact Solution

For the particle swarm collaborative practicability algorithm, when extended from single objective to multiobjective problem, the storage and maintenance of the partial differential exact solution sets occurs. And the selection of global and personal exact solutions, balance between exploitation and exploration and other problems also occur. In this paper, the diversity and evolution state of population is evaluated on the distribution and differential entropy, in the new objective space called parallel lattice coordinate system, using the partial differential front end. It applies the information to design the evolutionary strategy, so that the algorithm can consider both the convergence and diversity of approximate partial derivative front end. Also, the concepts of lattice dominance and lattice distance density are introduced to evaluate the personal environmental adaptability of partial derivative exact solution. And the updating methods for external files and selection mechanism for global exact solution are established. Finally, the particle swarm collaborative practicability algorithm is formed based on partial differential exact solution. Experimental results show that: Compared with the other 8 peer-topeer algorithms, this algorithm has demonstrated concentrated significant performance superiority in 6 multiobjective test problems composted of ZDT and DTLZ series on IGD performance index.