DMO-QPSO: A Multi-Objective Quantum-Behaved Particle Swarm Optimization Algorithm Based on Decomposition with Diversity Control

The decomposition-based multi-objective evolutionary algorithm (MOEA/D) has shown remarkable effectiveness in solving problems (MOPs). In this paper, we integrate the quantum-behaved particle swarm optimization (QPSO) with MOEA/D framework order to make QPSO be able solve MOPs effectively, advantage of being fully used. We also employ a diversity controlling mechanism avoid premature convergence especially at later stage search process, and thus further improve performance our proposed algorithm. addition, introduce number nondominated solutions generate global best for guiding other particles swarm. Experiments are conducted compare algorithm, DMO-QPSO, four algorithms one on 15 test functions, including both bi-objective tri-objective problems. results show that DMO-QPSO is better than five most these Moreover, study impact two different decomposition approaches, i.e., penalty-based boundary intersection (PBI) Tchebycheff (TCH) as well polynomial mutation operator algorithmic DMO-QPSO.