An agent based approach for the implementation of cooperative proactive S-Metaheuristics
作者： Mailyn Moreno , Alejandro Rosete , Juan Pavón
DOI: 10.1016/J.ESWA.2016.07.013
关键词:
摘要: This paper introduces several cooperative proactive S-Metaheuristics.The proposal is based on two characteristics of agents: proactivity and cooperation.Proactive S-Metaheuristics avoid local optima by adjusting parameters operators.Simple forms cooperation are used to combine metaheuristics.The experiments consider binary problems, knapsack travelling salesman problems. S-Metaheuristics, i.e. single-solution metaheuristics, which implemented taking advantage singular the agent paradigm: cooperation. Proactivity applied improve traditional versions Threshold Accepting Great Deluge Algorithm metaheuristics. approach follows previous work for definition Record-to-Record Travel Local Search Proactive metaheuristics as agents that cooperate in environment optimization process with goal avoiding stagnation their parameters. Based environmental information about solutions, adjustment focused keeping a minimal level acceptance new solutions. In addition, simple competition develop combination four The proposed have been validated through experimentation 28 benchmark functions strings, instances problems
sci-hub.se 参考文章(63)
Juan R. González, Carlos Cruz, Ignacio G. del Amo, David A. Pelta, An Adaptive Multiagent Strategy for Solving Combinatorial Dynamic Optimization Problems Nature Inspired Cooperative Strategies for Optimization (NICSO 2011). pp. 41- 55 ,(2011) , 10.1007/978-3-642-24094-2_3
Dariusz Barbucha, Ireneusz Czarnowski, Piotr Jędrzejowicz, Ewa Ratajczak-Ropel, Izabela Wierzbowska, Team of A-Teams - A Study of the Cooperation between Program Agents Solving Difficult Optimization Problems Studies in Computational Intelligence. pp. 123- 141 ,(2013) , 10.1007/978-3-642-34097-0_6
Jenny Fajardo Calderín, Antonio D. Masegosa, Alejandro Rosete Suárez, David A. Pelta, Adaptation Schemes and Dynamic Optimization Problems: A Basic Study on the Adaptive Hill Climbing Memetic Algorithm NICSO. pp. 85- 97 ,(2014) , 10.1007/978-3-319-01692-4_7
Gönül Uludağ, Berna Kiraz, A. Şima Etaner-Uyar, Ender Özcan, A framework to hybridize PBIL and a hyper-heuristic for dynamic environments parallel problem solving from nature. pp. 358- 367 ,(2012) , 10.1007/978-3-642-32964-7_36
Boyang Li, Zhiqi Shen, Chunyan Miao, Han Yu, Evolutionary organizational search adaptive agents and multi agents systems. pp. 1329- 1330 ,(2009)
Juan Pavón, Jorge Gómez-Sanz, Agent oriented software engineering with INGENIAS Lecture Notes in Computer Science. pp. 394- 403 ,(2003) , 10.1007/3-540-45023-8_38
Mauro Birattari, Janusz Kacprzyk, None, Tuning Metaheuristics: A Machine Learning Perspective ,(2009)
Mario A. Muñoz, Yuan Sun, Michael Kirley, Saman K. Halgamuge, Algorithm selection for black-box continuous optimization problems Information Sciences. ,vol. 317, pp. 224- 245 ,(2015) , 10.1016/J.INS.2015.05.010
Mailyn Moreno, Alejandro Rosete, Juán Pavón, An Agent Based Implementation of Proactive S-Metaheuristics hybrid artificial intelligence systems. pp. 1- 10 ,(2013) , 10.1007/978-3-642-40846-5_1