Combining convergence and diversity in evolutionary multiobjective optimization
Evolutionary Computation
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
Evolutionary Computation
MOPSO: a proposal for multiple objective particle swarm optimization
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
A non-dominated sorting particle swarm optimizer for multiobjective optimization
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
Improving PSO-Based multi-objective optimization using crowding, mutation and ∈-dominance
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
Evolutionary multi-objective optimization: a historical view of the field
IEEE Computational Intelligence Magazine
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients
IEEE Transactions on Evolutionary Computation
Handling multiple objectives with particle swarm optimization
IEEE Transactions on Evolutionary Computation
Acoustic sensor network node self-localization based on adaptive particle swarm optimization
AICI'12 Proceedings of the 4th international conference on Artificial Intelligence and Computational Intelligence
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Multi-Objective Particle Swarm Optimizers (MOPSOs) are often trapped in local optima, converge slowly, and need more function evaluations when applied to solve Multi-objective Optimization Problems (MOPs). A hybrid Vertical Mutation and self-Adaptation based MOPSO (VMAPSO) is proposed to overcome the disadvantages of existing MOPSOs. Firstly, a hybrid vertical mutation operator is carefully designed, which can escape local optima and conduct a local search by uniform distribution mutation and Gaussian distribution mutation, respectively. Secondly, the adaptation ratio models of two mutations are fully analyzed and compared. Thirdly, the velocity update equations proposed by Clerc are improved to reduce the randomness of MOPSOs, and @e-dominance based archive strategy is adopted in the proposed algorithm. Finally, the VMAPSO is tested on several classical MOP benchmark functions. The simulation results show that the VMAPSO can be used to solve both simple and complex MOPs and that the VMAPSO is superior to other MOPSOs in solving complex MOPs. In particular, the self-adaptation VMAPSO can be applied to problems that you have no knowledge about.