AbYSS: Adapting scatter search to multiobjective optimization

Antonio J. Nebro, Francisco Luna, Enrique Alba, Bernabé Dorronsoro, Juan J. Durillo, Andreas Beham

Research output: Contribution to journalArticlepeer-review

210 Citations (Scopus)

Abstract

We propose the use of a new algorithm to solve multiobjective optimization problems. Our proposal adapts the well-known scatter search template for single-objective optimization to the multiobjective domain. The result is a hybrid metaheuristic algorithm called Archive-Based hYbrid Scatter Search (AbYSS), which follows the scatter search structure but uses mutation and crossover operators from evolutionary algorithms. AbYSS incorporates typical concepts from the multiobjective field, such as Pareto dominance, density estimation, and an external archive to store the nondominated solutions. We evaluate AbYSS with a standard benchmark including both unconstrained and constrained problems, and it is compared with two state-of-the-art multiobjective optimizers, NSGA-II and SPEA2. The results obtained indicate that, according to the benchmark and parameter settings used, AbYSS outperforms the other two algorithms as regards the diversity of the solutions, and it obtains very competitive results according to the convergence to the true Pareto fronts and the hypervolume metric.

Original languageEnglish
Pages (from-to)439-457
Number of pages19
JournalIEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
Volume12
Issue number4
DOIs
Publication statusPublished - 2008

Keywords

  • Hybrid metaheuristics
  • Multiobjective optimization
  • Scatter search

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