The Pareto Archived Evolution Strategy (PAES) is a multiobjective optimizer which uses a simple (1+1) local search evolution strategy. Nonetheless, it is capable of finding diverse solutions in the Pareto optimal set because it maintains an archive of nondominated solutions which it exploits to estimate accurately the quality of new candidate solutions. Three versions, (1+1), (1+lambda) and (mu+lambda)-PAES have been developed. Each of these versions has been tested against two well known multiobjective evolutionary algorithms - the Niched Pareto Genetic Algorithm (NPGA) and a nondominated sorting GA (NSGA). Tests were carried out using five test functions (f2-f6) and results have been processed using statistical techniques introduced by Fonseca and Fleming. C code for each of the test functions and the statistical techniques are available below. Please drop me an email if you use any of these resources.

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  1. Duan, Qibin; Kroese, Dirk P.: Splitting for multi-objective optimization (2018)
  2. Leung, Chris S. K.; Lau, Henry Y. K.: Multiobjective simulation-based optimization based on artificial immune systems for a distribution center (2018)
  3. Sui, Liqi; Feissel, Pierre; Denoeux, Thierry: Identification of elastic properties in the belief function framework (2018)
  4. Ventresca, Mario; Harrison, Kyle Robert; Ombuki-Berman, Beatrice M.: The bi-objective critical node detection problem (2018)
  5. Redondo, J. L.; Fernández, J.; Ortigosa, P. M.: FEMOEA: a fast and efficient multi-objective evolutionary algorithm (2017)
  6. Martí, Luis; García, Jesús; Berlanga, Antonio; Molina, José M.: MONEDA: scalable multi-objective optimization with a neural network-based estimation of distribution algorithm (2016)
  7. Thanos, Aristotelis E.; Celik, Nurcin; Sáenz, Juan P.: An evolutionary sequential sampling algorithm for multi-objective optimization (2016)
  8. Liu, Linzhong; Mu, Haibo; Yang, Juhua: Generic constraints handling techniques in constrained multi-criteria optimization and its application (2015)
  9. Long, Qiang; Wu, Changzhi; Wang, Xiangyu; Jiang, Lin; Li, Jueyou: A multiobjective genetic algorithm based on a discrete selection procedure (2015)
  10. Shang, Ronghua; Wang, Yuying; Wang, Jia; Jiao, Licheng; Wang, Shuo; Qi, Liping: A multi-population cooperative coevolutionary algorithm for multi-objective capacitated arc routing problem (2014)
  11. Akay, Bahriye: Synchronous and asynchronous Pareto-based multi-objective artificial bee colony algorithms (2013)
  12. Idoumghar, Lhassane; Chérin, Nicolas; Siarry, Patrick; Roche, Robin; Miraoui, Abdellatif: Hybrid ICA-PSO algorithm for continuous optimization (2013)
  13. Ortega, Fernando; Sánchez, José-Luis; Bobadilla, Jesús; Gutiérrez, Abraham: Improving collaborative filtering-based recommender systems results using Pareto dominance (2013) ioport
  14. Zhou, Aimin; Gao, Feng; Zhang, Guixu: A decomposition based estimation of distribution algorithm for multiobjective traveling salesman problems (2013)
  15. Ali, Musrrat; Siarry, Patrick; Pant, Millie: An efficient differential evolution based algorithm for solving multi-objective optimization problems (2012)
  16. Claro, João; Pinho de Sousa, Jorge: A multiobjective metaheuristic for a mean-risk multistage capacity investment problem with process flexibility (2012)
  17. Lust, Thibaut; Teghem, Jacques: The multiobjective multidimensional knapsack problem: a survey and a new approach (2012)
  18. Salazar-Aguilar, M. Angélica; Ríos-Mercado, Roger Z.: Multiobjective scatter search for a commercial territory design problem (2012)
  19. Zhang, Jingling; Wang, Wanliang; Zhao, Yanwei; Cattani, Carlo: Multiobjective quantum evolutionary algorithm for the vehicle routing problem with customer satisfaction (2012)
  20. Zinflou, Arnaud; Gagné, Caroline; Gravel, Marc: GISMOO: A new hybrid genetic/immune strategy for multiple-objective optimization (2012)

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