Generalized Dynamic Constraint Satisfaction Based on Extension Particle Swarm Optimization Algorithm for Collaborative Simulation

A novel adaptive mutation particle swarm optimization (AMPSO) algorithm based on Fuzzy matter-element analysis for generalized dynamic constraints satisfaction (GDCS) was presented to resolve the coupling domain level and knowledge level constraints introduced by collaborative simulation results. Firstly, the Fuzzy relation-element optimization method (FREOM) was used to change the solution space into the optimization space by establishing the formalized model of fuzzy relation-element for GDCS, and the regulated correlation function was regarded as the fitness function judging the stand and fall of particle; Then, in the implementation process of PSO algorithm, the mutation mechanics was introduced to mutate the inactive particle and the particle with the smallest fitness according to mutation probability, which is intended to make the algorithm converge faster and respond better to changes in dynamic optimization problems; Finally, a design example is illustrated to show effectiveness of this proposed method.