Convergence analysis of some multiobjective evolutionary algorithms when discovering motifs

作     者：Gonzalez-Alvarez, David L. Vega-Rodriguez, Miguel A. Rubio-Largo, Alvaro 

作者机构：Univ Extremadura Escuela Politecn ARCO Res Grp Dept Technol Comp & Commun Caceres 10003 Spain 

出 版 物：《SOFT COMPUTING》 (Soft Comput.)

年 卷 期：2014年第18卷第5期

页      面：853-869页

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Spanish Ministry of Economy and Competitiveness ERDF (European Regional Development Fund) [TIN2012-30685] Gobierno de Extremadura (Spain) [PRE09010] European Social Fund (ESF) 

主　　题：Evolutionary algorithms Multiobjective optimization Convergence study Problem solving DNA sequences 

摘      要：An important issue in multiobjective optimization is the study of the convergence speed of algorithms. An optimization problem must be defined as simple as possible to minimize the computational cost required to solve it. In this work, we study the convergence speed of seven multiobjective evolutionary algorithms: DEPT, MO-VNS, MOABC, MO-GSA, MO-FA, NSGA-II, and SPEA2;when solving an important biological problem: the motif discovery problem. We have used twelve instances of four different organisms as benchmark, analyzing the number of fitness function evaluations required by each algorithm to achieve reasonable quality solutions. We have used the hypervolume indicator to evaluate the solutions discovered by each algorithm, measuring its quality every 100 evaluations. This methodology also allows us to study the hit rates of the algorithms over 30 independent runs. Moreover, we have made a deeper study in the more complex instance of each organism. In this study, we observe the increase of the archive (number of non-dominated solutions) and the spread of the Pareto fronts obtained by the algorithm in the median execution. As we will see, our study reveals that DEPT, MOABC, and MO-FA provide the best convergence speeds and the highest hit rates.