Particle swarm optimization method in multiobjective problems
Proceedings of the 2002 ACM symposium on Applied computing
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Recent approaches to global optimization problems through Particle Swarm Optimization
Natural Computing: an international journal
The particle swarm optimization algorithm: convergence analysis and parameter selection
Information Processing Letters
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
Evolutionary Computation
Multiobjective optimization using dynamic neighborhood particle swarm optimization
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
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
Computers and Operations Research
A non-dominated sorting particle swarm optimizer for multiobjective optimization
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
Handling multiple objectives with particle swarm optimization
IEEE Transactions on Evolutionary Computation
Learning-based ship design optimization approach
Computer-Aided Design
Hi-index | 0.00 |
In this paper, a multiple objective 'Hybrid Co-evolution based Particle Swarm Optimisation' methodology (HCPSO) is proposed. This methodology is able to handle multiple objective optimisation problems in the area of ship design, where the simultaneous optimisation of several conflicting objectives is considered. The proposed method is a hybrid technique that merges the features of co-evolution and Nash equilibrium with a @e-disturbance technique to eliminate the stagnation. The method also offers a way to identify an efficient set of Pareto (conflicting) designs and to select a preferred solution amongst these designs. The combination of co-evolution approach and Nash-optima contributes to HCPSO by utilising faster search and evolution characteristics. The design search is performed within a multi-agent design framework to facilitate distributed synchronous cooperation. The most widely used test functions from the formal literature of multiple objectives optimisation are utilised to test the HCPSO. In addition, a real case study, the internal subdivision problem of a ROPAX vessel, is provided to exemplify the applicability of the developed method.