Metaheuristics - the metaphor exposed

By Kenneth Sörensen. International Transactions in Operational Research, 2015

1,011 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Industrial and Manufacturing Engineering

View Paper: https://www.semanticscholar.org/paper/9755c3e3054fb1bcf4f1ffe9598d5352b191648b

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Citations in the corpus

Novel meta-heuristic bald eagle search optimisation algorithm (2019, background): “In the third stage, the eagles choose and attack a prey (Sörensen 2015).”
RUN beyond the metaphor: An efficient optimization algorithm based on Runge Kutta method (2021, background): “Of pertinent mention, a study by Sörensen on the low-quality contributions in the optimization methods opened the eyes of many researchers (Sörensen, 2015).”
A survey on new generation metaheuristic algorithms (2019, background): “A comprehensive discussion about the research directions about the scientific rigor of metaheuristics can be found in a study by Sörensen (2015).”
The Social Engineering Optimizer (SEO) (2018, neutral): “By another point of view, it seems that although most of population-based meta-heuristics are good to make a balance between the phases, they have several steps and many parameters to tune ( e.g. ICA, FA and WWO etc.) (Sörensen, 2015).”
A survey on evolutionary computation for complex continuous optimization (2021, neutral): “…the performance of existing EC algorithms, such as not only making strong scientific, mathematical, and/or statistical foundations to enhance the EC algorithms, but also providing solid and formal insights on the search behavior of the EC algorithms on the search space of solutions (Sorensen 2015).”
A review of simheuristics: Extending metaheuristics to deal with stochastic combinatorial optimization problems (2015, background): “Finally, and following the criticism of Sörensen [16] regarding certain lack of innovative-ness in the field of metaheuristics, this paper also aims at ‘open-ing’ a new research line in this field by extending metaheuristics so they can solve – through their integration with different simulation tec”
An improved grey wolf optimizer for solving engineering problems (2021, background): “Heuristic and metaheuristic algorithms are recognized as one of the most practical branches of approximate algorithms, which are capable of solving complex problems (Sörensen, 2015).”
Bio-inspired computation: Where we stand and what's next (2019, background): “When addressing real-world optimization problems, achieving competitive results and a good searching behavior are the most important factors, not the originality of their source of inspiration [2].”
A Tutorial On the design, experimentation and application of metaheuristic algorithms to real-World optimization problems (2021, background): “Furthermore, in a previously cited paper, Sorensen [26] also provides some good research practices to follow in the implementation of novel algorithms.”
Iterated Greedy methods for the distributed permutation flowshop scheduling problem (2019, methodology): “Un63 like complex metaphor-based methods –currently subject to severe criticism, 64 (Sörensen, 2015)–, IG is simply an iterated search method with no memory 65 and few structures.”
Swarm Intelligence Algorithms for Feature Selection: A Review (2018, methodology): “Sörensen in his paper [108] pointed out that instead of introducing novel algorithms, which do not always contribute to the ﬁeld of meta-heuristics, the existing ones should be used in combination with some other techniques.”
INFO: An efficient optimization algorithm based on weighted mean of vectors (2022, methodology): “Considering the main challenges of creating a high-performance optimization algorithm and all critical points highlighted in the literature above [42-44], we introduce an efficient optimizer based on the concept of the weighted mean of vectors.”