Metaheuristics in combinatorial optimization: Overview and conceptual comparison

By Christian Blum, Andrea Roli. CSUR, 2003

3,939 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications, Industrial and Manufacturing Engineering

View Paper: https://www.semanticscholar.org/paper/e6f9dba4300a6042252c40e5c26de9b2ebb915e9

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Citations in the corpus

Swarm Intelligence in Optimization (2008, background): “For books and surveys on metaheuristics see [144, 68, 18, 80].”
Coronavirus herd immunity optimizer (CHIO) (2020, methodology): “The traditional approximation-based methods were heuristic-based in which the optimization problem is constructively tackled element by element until a complete solution is reached [5].”
Beam-ACO - hybridizing ant colony optimization with beam search: an application to open shop scheduling (2005, methodology): “In the past 15–20 years, metaheuristics [5,6] emerged as alternative approximate methods for solving CO problems.”
Parallel metaheuristics: recent advances and new trends (2012, background): “Metaheuristics fall in two categories: trajectory-based metaheuristics and population-based metaheuristics (Blum and Roli, 2003).”
A survey on metaheuristics for stochastic combinatorial optimization (2009, background): “According to Blum and Roli (2003), metaheuristics are high level concepts for exploring search spaces by using different strategies.”
Cuckoo Search via Lévy flights (2009, methodology): “Numerically speaking, these can be translated into two crucial characteristics of the modern metaheuristics: intensification and diversification [ 3 ].”
Harmony Search as a Metaheuristic Algorithm (2010, background): “Any successful metaheuristic algorithm requires a good balance of these two important, seemingly opposite, components [6].”
Darwin or Lamarck? Future Challenges in Evolutionary Algorithms for Knowledge Discovery and Data Mining (2014, background): “A recent work on a Discrete Gravitational Search Algorithm (DGSA) to solve combinatorial optimization problems [99] can be found in [97].”
Nature-Inspired Optimization Algorithms: Challenges and Open Problems (2020, methodology): “For comprehensive reviews, please refer to some recent review articles (Blum and Roli, 2003; Reyes-Sierra and Coello Coello, 2006; Senthilnath et al., 2011; Yang and He, 2013; Yang and Deb, 2014; Alyasseri et al., 2018; Abdel-Basset and Shawky, 2019) and books (Kennedy et al., 2001; Yang et al.,…”
Heuristics for multi-attribute vehicle routing problems: A survey and synthesis (2013, background): “…constitute a core research domain in combinatorial optimization as illustrated by many literature reviews (e.g., Osman and Laporte 1996, Blum and Roli 2003, Gendreau and Potvin 2005) and books (e.g., Corne et al. 1999, Glover and Kochenberger 2003, Dréo et al. 2003, Gendreau and Potvin…”
Multiobjective cuckoo search for design optimization (2013, background): “On the other hand, metaheuristic algorithms are very powerful in dealing with this kind of optimization, and there are many review articles and excellent textbooks [6,9,11,36,38,39,42].”
Ant colony optimization theory: A survey (2005, methodology): “ACO belongs to the class of metaheuristics [8,32,40], which are approximate algorithms used to obtain good enough solutions to hard CO problems in a reasonable amount of computation time.”
Ant colony optimization: Introduction and recent trends (2005, background): “For books and surveys on metaheuristics see [13,47,56,81].”
Software Architecture Optimization Methods: A Systematic Literature Review (2013, background): “to guide the search [34], such as evolutionary algorithms”
Evolutionary algorithms and other metaheuristics in water resources: Current status, research challenges and future directions (2014, methodology): “Blum and Roli (2003) proposed an operator classification framework that categorizes operators on a continuum based on their propensity to intensify (e.g. exploit) or diversify (e.g. explore) an algorithm's search.”