How to solve it: modern heuristics
How to solve it: modern heuristics
Artificial Immune Systems: A New Computational Intelligence Paradigm
Artificial Immune Systems: A New Computational Intelligence Paradigm
Multiobjective Optimization Using Evolutionary Algorithms - A Comparative Case Study
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Archiving With Guaranteed Convergence And Diversity In Multi-objective Optimization
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
Evolutionary Computation
Multicriteria Optimization
Multiobjective optimization problems with complicated Pareto sets, MOEA/D and NSGA-II
IEEE Transactions on Evolutionary Computation
The performance of a new version of MOEA/D on CEC09 unconstrained MOP test instances
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
A novel smart multi-objective particle swarm optimisation using decomposition
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part II
Performance assessment of multiobjective optimizers: an analysis and review
IEEE Transactions on Evolutionary Computation
MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition
IEEE Transactions on Evolutionary Computation
| Hi-index | 0.00 |
Since the early days of multi-objective particle swarm optimizers (MOPSOs), researchers have looked for appropriate mechanisms to define the set of leaders (or global best set) from the swarm. At the beginning, leaders were randomly selected from the set of nondominated solutions currently available. However, over the years, researchers realized that random selection schemes were not the best choice, and additional information was incorporated in the leader selection mechanism (namely, information related to density estimation). Here, we study the use of mathematical programming techniques for defining the leader selection mechanism of a MOPSO. The proposed approach decomposes a multi-objective optimization problem (MOP) into several single objective optimization problems by using traditional multi-objective mathematical programming techniques. Our preliminary results indicate that our proposed approach is a viable choice for solving MOPs, since it is able to outperform a state-of-the-art multi-objective evolutionary algorithm (MOEA).