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Raidl: hybrid metaheuristics—powerful tools for optimization. Springer international publishing, switzerland, 2016, 157 pp, isbn: 978-3-319-30882-1.
Hybrid metaheuristics, presenting the most important achievements and de-velopments in this domain. We have invited key experts in the field to supply chapters with the objective of providing an introduction to the themes of hybrid metaheuristics and discussing associated theoretical aspects or appli-cations.
Download hybrid metaheuristics: powerful tools for optimization (artificial intelligence: foundations, theory, and algorithms) - free epub, mobi, pdf ebooks download, ebook torrents download.
The best results found for many real-life or classical optimization problems are obtained by hybrid algorithms. Combinations of algorithms such as metaheuristics, mathematical programming, constraint programming and machine learning techniques have provided very powerful search algorithms.
Oct 26, 2018 in this paper, two hybrid metaheuristics, mamh (multiagent an important issue in the design of ensemble systems is the parameter setting.
Jan 25, 2018 indicated that the proposed hybrid algorithm is able to provide very competitive solution comparing to other state-of-the-art meta heuristics.
Recently, hybrid metaheuristics have been successfully used for many use of memory is considered one of the crucial elements of a powerful metaheuristic.
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Including different powerful conventional, emer ging and hybrid optimization metaheuristics techniques applied to solve the static, long-term tnep problem, using the ac model, considering both.
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The authors are among the leading researchers in the hybridization of metaheuristics with other techniques for optimization, and their work reflects the broad shift to problem-oriented rather than algorithm-oriented approaches, enabling faster and more effective implementation in real-life applications.
In collaboration with christian blum from the university of the basque country, günther raidl wrote the book hybrid metaheuristics - powerful tools for optimization. The book is published by springer in the series artificial intelligence: foundations, theory, and algorithms and available in printed form as well as e-book.
Hybrid metaheuristics: powerful tools for optimization jun 01, 2018, springer paperback 3319809075 9783319809076 zzzz.
This cross-fertilization is documented by a multitude of powerful hybrid algorithms that were obtained by combining components from several different optimization techniques. Hereby, hybridization is not restricted to the combination of different metaheuristics but includes, for example, the combination of exact algorithms and metaheuristics.
Within the field of solving complex optimization prob- lems such as reliability analysis, swarm intelligence algo- rithms are powerful due to their many advantages over the traditional deterministic methods and algorithms. De- spite many researchers are oriented toward adopting single metaheuristic, it generates some limitations.
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The application of metaheuristics techniques like ant colony optimization (aco), particle swarm optimization (pso), genetic algorithms (ga), hybrid ga-pso and quadrature programming (qp) optimization for support vector machines in developing real time multi-biometric authentication systems are discussed in this chapter.
Hybrid metaheuristics for dynamic and stochastic vehicle routing / ulrike ritzinger and jakob puchinger combining two search paradigms for multi-objective optimization: two-phase and pareto local search / jérémie dubois-lacoste, manuel lópez-ibáñez and thomas stützle.
One of the most interesting recent trends for what concerns research on metaheuristics is their hybridization with other techniques for optimization.
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), researchers interested in this topic can find useful contribu- proceedings of hm 2006—third international workshop on hybrid meta- tions in the literature about artificial.
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