AbYSS: Adapting Scatter Search to Multiobjective Optimization

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 (A...

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Bibliographic Details
Published in:IEEE transactions on evolutionary computation 2008-08, Vol.12 (4), p.439-457
Main Authors: Nebro, Antonio J., Luna, Francisco, Alba, Enrique, Dorronsoro, BernabÉ, Durillo, Juan J., Beham, Andreas
Format: Article
Language:English
Subjects:
Algorithms
Applied sciences
Artificial intelligence
Computer science
control theory
systems
Constraint optimization
Density
Evolutionary algorithms
Evolutionary computation
Exact sciences and technology
Genetic mutations
Hybrid metaheuristics
Learning and adaptive systems
Mathematical models
multiobjective optimization
Optimization
Pareto optimality
Pareto optimization
Proposals
Scatter
scatter search
Scattering
Search methods
Searching
Stochastic processes
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Summary: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.
ISSN:1089-778X
1941-0026
DOI:10.1109/TEVC.2007.913109