Abstract:
Multi-objective optimal evolutionary algorithms (MOEA) are effective algorithms to solve multi-objective optimal problem (MOP). Because ranking which used by most MOEAs h...Show MoreMetadata
Abstract:
Multi-objective optimal evolutionary algorithms (MOEA) are effective algorithms to solve multi-objective optimal problem (MOP). Because ranking which used by most MOEAs has some disadvantages, We propose a new method that uses better function to compare candidate solutions and tree structure to express the relationship of solutions. Experiments show that the new algorithm can converge to the Pareto front, and maintains the diversity of population. When the algorithm is extended to a MOP with constraints, it can also get a good result. Most important of all, the algorithm is simple but highly efficient.
Date of Conference: 08-12 December 2003
Date Added to IEEE Xplore: 24 May 2004
Print ISBN:0-7803-7804-0