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 281 articles , 1 standard article )

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  1. Barbosa, Flávia; Berbert Rampazzo, Priscila C.; Yamakami, Akebo; Camanho, Ana S.: The use of frontier techniques to identify efficient solutions for the berth allocation problem solved with a hybrid evolutionary algorithm (2019)
  2. Eskandarpour, Majid; Ouelhadj, Djamila; Hatami, Sara; Juan, Angel A.; Khosravi, Banafsheh: Enhanced multi-directional local search for the bi-objective heterogeneous vehicle routing problem with multiple driving ranges (2019)
  3. Glover, Fred; Kochenberger, Gary; Du, Yu: Quantum bridge analytics. I: A tutorial on formulating and using QUBO models (2019)
  4. Kar, Mohuya B.; Kar, Samarjit; Guo, Sini; Li, Xiang; Majumder, Saibal: A new bi-objective fuzzy portfolio selection model and its solution through evolutionary algorithms (2019)
  5. Stefanello, Fernando; Aggarwal, Vaneet; Buriol, Luciana S.; Resende, Mauricio G. C.: Hybrid algorithms for placement of virtual machines across geo-separated data centers (2019)
  6. Vallada, Eva; Villa, Fulgencia; Fanjul-Peyro, Luis: Enriched metaheuristics for the resource constrained unrelated parallel machine scheduling problem (2019)
  7. Xu, Zhenxing; He, Kun; Li, Chu-Min: An iterative path-breaking approach with mutation and restart strategies for the MAX-SAT problem (2019)
  8. Ahmed, A. K. M. Foysal; Sun, Ji Ung: Bilayer local search enhanced particle swarm optimization for the capacitated vehicle routing problem (2018)
  9. Ghanbarzadeh, Ali; Pouladian, Majid; Shabestani Monfared, Ali; Mahdavi, Seied Rabi: The scatter search based algorithm for beam angle optimization in intensity-modulated radiation therapy (2018)
  10. Hare, Warren; Loeppky, Jason; Xie, Shangwei: Methods to compare expensive stochastic optimization algorithms with random restarts (2018)
  11. Irigoyen, Eloy; Barragán, Antonio Javier; Larrea, Mikel; Andújar, José Manuel: About extracting dynamic information of unknown complex systems by neural networks (2018)
  12. Martins, Daniel; Vianna, Gabriel M.; Rosseti, Isabel; Martins, Simone L.; Plastino, Alexandre: Making a state-of-the-art heuristic faster with data mining (2018)
  13. Muritiba, Albert Einstein Fernandes; Rodrigues, Carlos Diego; da Costa, Francíio Araùjo: A path-relinking algorithm for the multi-mode resource-constrained project scheduling problem (2018)
  14. Chen, Yujie; Cowling, Peter; Polack, Fiona; Remde, Stephen; Mourdjis, Philip: Dynamic optimisation of preventative and corrective maintenance schedules for a large scale urban drainage system (2017)
  15. de Souza Lima, Fátima M.; Pereira, Davi S. D.; da Conceição, Samuel V.; de Camargo, Ricardo S.: A multi-objective capacitated rural school bus routing problem with heterogeneous fleet and mixed loads (2017)
  16. González, Miguel A.; Palacios, Juan José; Vela, Camino R.; Hernández-Arauzo, Alejandro: Scatter search for minimizing weighted tardiness in a single machine scheduling with setups (2017)
  17. Hoff, Arild; Peiró, Juanjo; Corberán, Ángel; Martí, Rafael: Heuristics for the capacitated modular hub location problem (2017)
  18. Leyman, Pieter; Vanhoucke, Mario: Capital- and resource-constrained project scheduling with net present value optimization (2017)
  19. Marinakis, Yannis; Migdalas, Athanasios; Sifaleras, Angelo: A hybrid particle swarm optimization -- variable neighborhood search algorithm for constrained shortest path problems (2017)
  20. Máximo, Vinícius R.; Nascimento, Mariá C. V.; Carvalho, André C. P. L. F.: Intelligent-guided adaptive search for the maximum covering location problem (2017)

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