PAES

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.


References in zbMATH (referenced in 91 articles )

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  1. Abouhawwash, Mohamed; Jameel, Mohammed; Deb, Kalyanmoy: A smooth proximity measure for optimality in multi-objective optimization using Benson’s method (2020)
  2. Grotti, Ewerton; Mizushima, Douglas Makoto; Backes, Artur Dieguez; de Freitas Awruch, Marcos Daniel; Gomes, Herbert Martins: A novel multi-objective quantum particle swarm algorithm for suspension optimization (2020)
  3. Selçuklu, Saltuk Buğra; Coit, David W.; Felder, Frank A.: Pareto uncertainty index for evaluating and comparing solutions for stochastic multiple objective problems (2020)
  4. Das, Amit Kumar; Das, Debasish; Pratihar, Dilip Kumar: Multi-objective optimization and cluster-wise regression analysis to establish input-output relationships of a process (2018)
  5. Duan, Qibin; Kroese, Dirk P.: Splitting for multi-objective optimization (2018)
  6. Leung, Chris S. K.; Lau, Henry Y. K.: Multiobjective simulation-based optimization based on artificial immune systems for a distribution center (2018)
  7. Seada, Haitham; Deb, Kalyanmoy: Non-dominated sorting based multi/many-objective optimization: two decades of research and application (2018)
  8. Sui, Liqi; Feissel, Pierre; Denoeux, Thierry: Identification of elastic properties in the belief function framework (2018)
  9. Ventresca, Mario; Harrison, Kyle Robert; Ombuki-Berman, Beatrice M.: The bi-objective critical node detection problem (2018)
  10. Redondo, J. L.; Fernández, J.; Ortigosa, P. M.: FEMOEA: a fast and efficient multi-objective evolutionary algorithm (2017)
  11. Martí, Luis; García, Jesús; Berlanga, Antonio; Molina, José M.: MONEDA: scalable multi-objective optimization with a neural network-based estimation of distribution algorithm (2016)
  12. Thanos, Aristotelis E.; Celik, Nurcin; Sáenz, Juan P.: An evolutionary sequential sampling algorithm for multi-objective optimization (2016)
  13. Liu, Linzhong; Mu, Haibo; Yang, Juhua: Generic constraints handling techniques in constrained multi-criteria optimization and its application (2015)
  14. Long, Qiang; Wu, Changzhi; Wang, Xiangyu; Jiang, Lin; Li, Jueyou: A multiobjective genetic algorithm based on a discrete selection procedure (2015)
  15. 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)
  16. Akay, Bahriye: Synchronous and asynchronous Pareto-based multi-objective artificial bee colony algorithms (2013)
  17. Idoumghar, Lhassane; Chérin, Nicolas; Siarry, Patrick; Roche, Robin; Miraoui, Abdellatif: Hybrid ICA-PSO algorithm for continuous optimization (2013)
  18. Ortega, Fernando; Sánchez, José-Luis; Bobadilla, Jesús; Gutiérrez, Abraham: Improving collaborative filtering-based recommender systems results using Pareto dominance (2013) ioport
  19. Zhou, Aimin; Gao, Feng; Zhang, Guixu: A decomposition based estimation of distribution algorithm for multiobjective traveling salesman problems (2013)
  20. Ali, Musrrat; Siarry, Patrick; Pant, Millie: An efficient differential evolution based algorithm for solving multi-objective optimization problems (2012)

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