Meta-Heuristics: Advances and Trends in Local Search Paradigms for Optimization

By Stefan Voss, I. Osman, C. Roucairol. 2012

374 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Computer Vision and Pattern Recognition, Industrial and Manufacturing Engineering

View Paper: https://www.semanticscholar.org/paper/664506957bb2311608e49feec966d96fd0168c3a

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

1. Simon Lacoste-Julien
2. Teodor Gabriel Crainic
3. Michel Gendreau
4. François Soumis
5. Zahra Beheshti
6. Hokey Min

In Computer Vision and Pattern Recognition

1. Teodor Gabriel Crainic
2. Thibaut Vidal
3. Christian Prins
4. Simon Lacoste-Julien
5. Michel Gendreau
6. François Soumis
7. Hokey Min
8. Peter Richtárik

In Industrial and Manufacturing Engineering

1. Thibaut Vidal
2. Teodor Gabriel Crainic
3. Christian Prins
4. Simon Lacoste-Julien
5. Michel Gendreau
6. François Soumis
7. Hokey Min
8. Michael Schneider

Citations in the corpus

• Machine Learning in Airline Crew Pairing to Construct Initial Clusters for Dynamic Constraint Aggregation (2020, background): “Thirdly, the divergence from the optimal solution is significant, resulting in new solutions which can be far from the globally optimal solution [40].”
• Heuristics for multi-attribute vehicle routing problems: A survey and synthesis (2013, background): “Long-term memories for penalizing frequent solution elements can also be viewed as a basis of Guided Local Search (Voudouris and Tsang 1999), applied by Kilby et al. (1999), Tarantilis et al. (2007), Kytöjoki et al. (2007), and Zachariadis and Kiranoudis (2010a) to the CVRP.”
• A review of population-based meta-heuristic algorithm (2013, background): “According to Voss et al. [32], a meta-heuristic is: “an iterative master process that guides and modifies the operations of subordinate heuristics to efficiently produce high-quality solutions.”
• Artificial intelligence in supply chain management: theory and applications (2010, methodology): “This algorithm has been applied successfully to handle well-known network design problems such as the TSP, the vehicle routing problem, and the minimum spanning tree problem (Dorigo and Gambardella 1997, Bullnheimer et al. 1999, Shyu et al. 2003).”
• Flight-connection Prediction for Airline Crew Scheduling to Construct Initial Clusters for OR Optimizer (2019, background): “Thirdly, the divergence from the optimal solution is significant, resulting in new solutions which can be far from the global optimal solution (Voß, 1999).”