Hierarchicity-based (self-similar) hybrid genetic algorithm for the grey pattern quadratic assignment problem
| Autor: | Alfonsas Misevičius, Zvi Drezner, Gintaras Palubeckis |
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| Rok vydání: | 2021 |
| Předmět: |
0209 industrial biotechnology
Control and Optimization General Computer Science Computer science Quadratic assignment problem Computer Science::Neural and Evolutionary Computation Crossover Structure (category theory) 02 engineering and technology Object (computer science) Tabu search 020901 industrial engineering & automation Operator (computer programming) Iterated function Genetic algorithm 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Algorithm |
| Zdroj: | Memetic Computing. 13:69-90 |
| ISSN: | 1865-9292 1865-9284 |
| DOI: | 10.1007/s12293-020-00321-6 |
| Popis: | In this paper, we present a hierarchicity-based (self-similar) hybrid genetic algorithm for the solution of the grey pattern quadratic assignment problem. This is a novel hybrid genetic search-based heuristic algorithm with the original, hierarchical architecture and it is in connection with what is known as self-similarity—this means that an object (in our case, algorithm) is exactly or approximately similar to constituent parts of itself. The two main aspects of the proposed algorithm are the following: (1) the hierarchical (self-similar) structure of the genetic algorithm itself, and (2) the hierarchical (self-similar) form of the iterated tabu search algorithm, which is integrated into the genetic algorithm as an efficient local optimizer (local improvement algorithm) of the offspring solutions produced by the crossover operator of the genetic algorithm. |
| Databáze: | OpenAIRE |
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