AbYSS

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 (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


References in zbMATH (referenced in 11 articles )

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  1. Arrondo, Aránzazu Gila; Redondo, Juana L.; Fernández, José; Ortigosa, Pilar M.: Parallelization of a non-linear multi-objective optimization algorithm: application to a location problem (2015)
  2. Lin, Qiuzhen; Zhu, Qingling; Huang, Peizhi; Chen, Jianyong; Ming, Zhong; Yu, Jianping: A novel hybrid multi-objective immune algorithm with adaptive differential evolution (2015)
  3. Ortigosa, P.M.; Hendrix, E.M.T.; Redondo, J.L.: On heuristic bi-criterion methods for semi-obnoxious facility location (2015)
  4. Li, Ke; Kwong, Sam; Wang, Ran; Tang, Kit-Sang; Man, Kim-Fung: Learning paradigm based on jumping genes: a general framework for enhancing exploration in evolutionary multiobjective optimization (2013)
  5. Lin, Qiuzhen; Chen, Jianyong: A novel micro-population immune multiobjective optimization algorithm (2013)
  6. Li, Ke; Kwong, Sam; Cao, Jingjing; Li, Miqing; Zheng, Jinhua; Shen, Ruimin: Achieving balance between proximity and diversity in multi-objective evolutionary algorithm (2012)
  7. Pardalos, Panos M.; Steponavičė, Ingrida; Žilinskas, Antanas: Pareto set approximation by the method of adjustable weights and successive lexicographic goal programming (2012)
  8. Chen, Wang; Shi, Yan-Jun; Teng, Hong-Fei; Lan, Xiao-Ping; Hu, Li-Chen: An efficient hybrid algorithm for resource-constrained project scheduling (2010)
  9. Durillo, J.J.; Nebro, A.J.; Luna, F.; Coello, C.A.Coello; Alba, E.: Convergence speed in multi-objective metaheuristics: efficiency criteria and empirical study (2010)
  10. Luna, Francisco; Durillo, Juan J.; Nebro, Antonio J.; Alba, Enrique: A scatter search approach for solving the automatic cell planning problem (2010)
  11. Eskandari, Hamidreza; Geiger, Christopher D.: A fast Pareto genetic algorithm approach for solving expensive multiobjective optimization problems (2008)