Incorporating ϵ-dominance in AMOSA: Application to multiobjective 0/1 knapsack problem and clustering gene expression data

Recently, a new model of multiobjective simulated annealing, AMOSA, was developed which was found to provide improved performance for several multi objective optimization problems especially for problems with many objectives. In this article, we aim to further improve the performance of AMOSA by incorporating the concept of ϵ-dominance which is a more generalized form of conventional dominance. This strategy is referred to as ϵ-AMOSA. The result of ϵ-AMOSA is compared with those of AMOSA, NSGA-II and ϵ-MOEA and AMOSA for several test problems with number of objectives varying from two to fifteen and the number of variables varying from one to thirty. The performance of ϵ-AMOSA is also compared with other strategies for multiobjective 0/1 knapsack problem. A real life application of ϵ-AMOSA for clustering genes from gene expression data is also demonstrated. The results demonstrate the effectiveness of ϵ-AMOSA.

Figure optionsDownload as PowerPoint slideHighlights
► We incorporate ϵ-dominance in AMOSA, termed as ϵ-AMOSA.
► We found that ϵ-AMOSA outperforms popular algorithms.
► We applied ϵ-AMOSA on one real and one synthetic applications.