Multi-objective particle swarm optimization algorithm using large scale variable decomposition

The multi-objective optimization of problems with large scale variable has become a focus in multi-objective evolutionary algorithm research field. Multi-objective particle swarm optimization algorithm is of better convergence, easier calculation and less parameter settings, yet "variable dimensionality" will be triggered as strategic variables increase. To solve the problem, this paper proposes random variable decomposition strategy, i.e. to promote the possibility of distributing associated variables into one group by random variable decomposition on the basis of variable groups, so as to realize better maintenance in association between variable groups. CCMOPSO is proposed through the integration of cooperative co-evolution evolutionary frame into the large scale variable decomposition. Comparative simulation experiment is conducted after the variable extension on typical standard functions of ZDT1, ZDT2, ZDT3, DTLZ1 and DTLZ2. Comparison between convergence and diversity of the algorithm with the binary addition index ε and hyper-volume indicator (HV), shows this algorithm is of better diversity, convergence and easiness in multi-objective function with large scale variable than MOPSO, NSGA-II, MOEA/D and GDE3, and computational complexity is decreased.