AbYSS: Adapting Scatter Search to Multiobjective Optimization

By Antonio J. Nebro, Francisco Luna, Enrique Alba, Bernabé Dorronsoro, Juan José Durillo, Andreas Beham. IEEE Transactions on Evolutionary Computation, 2008

302 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/480a5ce68dc9782961f52d1d96274edba0d43443

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Citations in the corpus

The jMetal framework for multi-objective optimization: Design and architecture (2010, methodology): “Besides NSGA-II, jMetal includes the implementation of a number of both classic and modern multi-objective optimizers; some examples are: SPEA2 [27], PAES [26], OMOPSO [29], MOCell [30], AbYSS [ 31 ], MOEA/D [32], GDE3 [33], IBEA [34], and SMPSO [35].”
SMPSO: A new PSO-based metaheuristic for multi-objective optimization (2009, methodology): “The selected algorithms are: two genetic algorithms, NSGA-II [3] and SPEA2 [17]; a scatter search approach, AbYSS [13]; a cellular genetic algorithm, MOCell [12], and OMOPSO [14].”
A Survey on Multiobjective Evolutionary Algorithms for the Solution of the Portfolio Optimization Problem and Other Finance and Economics Applications (2013, methodology): “1) AbYSS [13]: This approach is based on scatter search with a small population, whose members are combined and subsequently improved by a (1+1)-evolution strategy (ES) to construct new individuals (applying SPEA2’s density estimator, which is based on a clustering algorithm [14]).”
Transfer Learning-Based Dynamic Multiobjective Optimization Algorithms (2016, background): “where acc(t) = ([HV(POF(t))]/[ max HV(POF)]) implies the accuracy rate of computing the POF at time t, and HV refers to the value of hypervolume [54].”