Enhancing Hyperheuristics for the Knapsack Problem through Fuzzy Logic
Autor: | Frumen Olivas, Iván Amaya, Hugo Terashima-Marín, José Carlos Ortiz-Bayliss, Santiago Enrique Conant-Pablos |
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Rok vydání: | 2020 |
Předmět: |
0209 industrial biotechnology
Mathematical optimization Article Subject General Computer Science Computer science General Mathematics Computer applications to medicine. Medical informatics R858-859.7 Neurosciences. Biological psychiatry. Neuropsychiatry 02 engineering and technology Fuzzy logic 020901 industrial engineering & automation Fuzzy Logic Genetic algorithm 0202 electrical engineering electronic engineering information engineering Selection (genetic algorithm) Branch and bound General Neuroscience Particle swarm optimization General Medicine Fuzzy control system Dynamic programming Knapsack problem 020201 artificial intelligence & image processing Algorithms RC321-571 Research Article |
Zdroj: | Computational Intelligence and Neuroscience Computational Intelligence and Neuroscience, Vol 2021 (2021) |
ISSN: | 1687-5273 |
Popis: | Hyperheuristics rise as powerful techniques that get good results in less computational time than exact methods like dynamic programming or branch and bound. These exact methods promise the global best solution, but with a high computational time. In this matter, hyperheuristics do not promise the global best solution, but they promise a good solution in a lot less computational time. On the contrary, fuzzy logic provides the tools to model complex problems in a more natural way. With this in mind, this paper proposes a fuzzy hyperheuristic approach, which is a combination of a fuzzy inference system with a selection hyperheuristic. The fuzzy system needs the optimization of its fuzzy rules due to the lack of expert knowledge; indeed, traditional hyperheuristics also need an optimization of their rules. The fuzzy rules are optimized by genetic algorithms, and for the rules of the traditional methods, we use particle swarm optimization. The genetic algorithm will also reduce the number of fuzzy rules, in order to find the best minimal fuzzy rules, whereas traditional methods already use very few rules. Experimental results show the advantage of using our approach instead of a traditional selection hyperheuristic in 3200 instances of the 0/1 knapsack problem. |
Databáze: | OpenAIRE |
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