원문정보
초록
영어
To further enhance the distribution uniformity and extensiveness of the solution sets and to ensure effective convergence of the solution sets to the Pareto front, we proposed a MOEA approach based on a clustering mechanism. We named this approach improved multi-objective evolutionary algorithm (LMOEA). This algorithm uses a clustering technology to compute and maintain the distribution and diversity of the solution sets. A fuzzy C-means clustering algorithm is used for clustering individuals. Finally, the LMOEA is applied to solve the classical multi-objective knapsack problems. The algorithm performance was evaluated using convergence and diversity indicators. The proposed algorithm achieved significant improvements in terms of algorithm convergence and population diversity compared with the classical NSGA-II and the MOEA/D.
목차
1. Introduction
2. Key Concepts
2.1 Multi-objective Optimization
2.2 Classical Literature Review
3. An Improved Multi-objective Evolutionary Algorithm Framework
3.1 Clustering Individuals in the Population to Form Solution Clusters
3.2 Overall Algorithm Framework
4. Experimental Results and Analysis
5. Conclusion
References