论文信息 - A New Multi-objective Evolutionary Optimisation Algorithm: The Two-Archive Algorithm

A New Multi-objective Evolutionary Optimisation Algorithm: The Two-Archive Algorithm

Many multi-objective evolutionary algorithms (MOEAs) have been proposed in recent years. However, almost all MOEAs have been evaluated on problems with two to four objectives only. It is unclear how well these MOEAs will perform on problems with a large number of objectives. Our preliminary study (V. Khare et al., 2003) showed that performance of some MOEAs deteriorates significantly as the number of objectives increases. This paper proposes a new MOEA that performs well on problems with a large number of objectives. The new algorithm separates non-dominated solutions into two archives, and is thus called the two-archive algorithm. The two archives focused on convergence and diversity, respectively, in optimisation. Computational studies have been carried out to evaluate and compare our new algorithm against the best MOEA for problems with a large number of objectives. Our experimental results have shown that the two-archive algorithm outperforms existing MOEAs on problems with a large number of objectives

Xin Yao | Kata Praditwong | X. Yao | Kata Praditwong

[1] Martin J. Oates,et al. The Pareto Envelope-Based Selection Algorithm for Multi-objective Optimisation , 2000, PPSN.

[2] Xin Yao,et al. Parallel Problem Solving from Nature PPSN VI , 2000, Lecture Notes in Computer Science.

[3] Marco Laumanns,et al. SPEA2: Improving the strength pareto evolutionary algorithm , 2001 .

[4] Kalyanmoy Deb,et al. Running performance metrics for evolutionary multi-objective optimizations , 2002 .

[5] Kalyanmoy Deb,et al. A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[6] Xin Yao,et al. Performance Scaling of Multi-objective Evolutionary Algorithms , 2003, EMO.

[7] Gary B. Lamont,et al. AN INTRODUCTION TO MULTI-OBJECTIVE EVOLUTIONARY ALGORITHMS AND THEIR APPLICATIONS , 2004 .

[8] Gary B. Lamont,et al. Applications Of Multi-Objective Evolutionary Algorithms , 2004 .

[9] Marco Laumanns,et al. Scalable Test Problems for Evolutionary Multiobjective Optimization , 2005, Evolutionary Multiobjective Optimization.

[10] Joshua D. Knowles,et al. ParEGO: a hybrid algorithm with on-line landscape approximation for expensive multiobjective optimization problems , 2006, IEEE Transactions on Evolutionary Computation.