Scatter Search

Scatter search This chapter discusses the principles and foundations behind scatter search and its application to the problem of training neural networks. Scatter search is an evolutionary method that has been successfully applied to a wide array of hard optimization problems. Scatter search constructs new trial solutions by combining so-called reference solutions and employing strategic designs that exploit context knowledge. In contrast to other evolutionary methods like genetic algorithms, scatter search is founded on the premise that systematic designs and methods for creating new solutions afford significant benefits beyond those derived from recourse to randomization. Our implementation goal is to create a combination of the five elements in the scatter search methodology that proves effective when searching for optimal weight values in a multilayer neural network. Through experimentation, we show that our instantiation of scatter search can compete with the best-known training algorithms in terms of training quality while keeping the computational effort at a reasonable level. (Source:

References in zbMATH (referenced in 286 articles , 1 standard article )

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  1. Khalid, Ruzelan; Nawawi, Mohd. Kamal Mohd.; Kawsar, Luthful A.; Ghani, Noraida A.; Kamil, Anton A.; Mustafa, Adli: The evaluation of pedestrians’ behavior using (M/G/C/C) analytical, weighted distance and real distance simulation models (2016)
  2. Kreter, Stefan; Rieck, Julia; Zimmermann, Jürgen: Models and solution procedures for the resource-constrained project scheduling problem with general temporal constraints and calendars (2016)
  3. Lai, Xiangjing; Hao, Jin-Kao: A tabu search based memetic algorithm for the Max-Mean dispersion problem (2016)
  4. Sánchez-Oro, Jesús; Laguna, Manuel; Martí, Rafael; Duarte, Abraham: Scatter search for the bandpass problem (2016)
  5. Vojvodic, Goran; Jarrah, Ahmad I.; Morton, David P.: Forward thresholds for operation of pumped-storage stations in the real-time energy market (2016)
  6. Yang, Zhaoyang; Zhang, Guojun; Zhu, Haiping: Multi-neighborhood based path relinking for two-sided assembly line balancing problem (2016)
  7. Yin, Peng-Yeng; Chuang, Ya-Lan: Adaptive memory artificial bee colony algorithm for Green vehicle routing with cross-docking (2016)
  8. Basu, Sumanta; Sharma, Megha; Ghosh, Partha Sarathi: Metaheuristic applications on discrete facility location problems: a survey (2015)
  9. Camacho-Vallejo, José-Fernando; Muñoz-Sánchez, Rafael; González-Velarde, José Luis: A heuristic algorithm for a supply chain’s production-distribution planning (2015)
  10. de-los-Cobos-Silva, Sergio Gerardo; Gutiérrez-Andrade, Miguel Ángel; Mora-Gutiérrez, Roman Anselmo; Lara-Velázquez, Pedro; Rincón-García, Eric Alfredo; Ponsich, Antonin: An efficient algorithm for unconstrained optimization (2015)
  11. Dubois-Lacoste, Jérémie; López-Ibáñez, Manuel; Stützle, Thomas: Anytime Pareto local search (2015)
  12. Gevezes, Theodoros; Pitsoulis, Leonidas: A greedy randomized adaptive search procedure with path relinking for the shortest superstring problem (2015)
  13. González, Miguel A.; Oddi, Angelo; Rasconi, Riccardo; Varela, Ramiro: Scatter search with path relinking for the job shop with time lags and setup times (2015)
  14. Hiermann, Gerhard; Prandtstetter, Matthias; Rendl, Andrea; Puchinger, Jakob; Raidl, Günther R.: Metaheuristics for solving a multimodal home-healthcare scheduling problem (2015)
  15. Ivorra, Benjamin; Mohammadi, Bijan; Manuel Ramos, Angel: A multi-layer line search method to improve the initialization of optimization algorithms (2015)
  16. Li, Kun; Tian, Huixin: A DE-based scatter search for global optimization problems (2015)
  17. Martí, Rafael; Corberán, Ángel; Peiró, Juanjo: Scatter search for an uncapacitated (p)-hub median problem (2015)
  18. Moin, Noor Hasnah; Yuliana, Titi: Three-phase methodology incorporating scatter search for integrated production, inventory, and distribution routing problem (2015)
  19. Qi, Jian-Jun; Liu, Ya-Jie; Jiang, Ping; Guo, Bo: Schedule generation scheme for solving multi-mode resource availability cost problem by modified particle swarm optimization (2015)
  20. Raidl, Günther R.: Decomposition based hybrid metaheuristics (2015)

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