Evolutionary algorithm and decisional DNA for multiple travelling salesman problem

Peng Wang; Cesar Sanin; Edward Szczerbicki

Evolutionary algorithm and decisional DNA for multiple travelling salesman problem

Abstrakt

In the real world, it is common to face optimization problems that have two or more objectives that must be optimized at the same time, that are typically explained in different units, and are in conflict with one another. This paper presents a hybrid structure that combines set of experience knowledge structures (SOEKS) and evolutionary algorithms, NSGA-II (Non-dominated Sorting Genetic Algorithm II), to solve multiple optimization problems. The proposed structure uses experience that is derived from a former decision event to improve the evolutionary algorithm’s ability to find optimal solutions rapidly and efficiently. It is embedded in a Smart Experience-based Data Analysis System (SEDAS) introduced in the paper. Experimental illustrative results of SEDAS application to solve a travelling salesman problem show that our new proposed hybrid model can find optimal or close to true Pareto-optimal solutions in a fast and efficient way.

Autorzy (3)

Peng Wang
- University of Newcastle School of Engineering
Cesar Sanin
- University of Newcastle School of Engineeriing
Edward Szczerbicki prof. dr hab. inż.

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Informacje szczegółowe

Kategoria:: Publikacja w czasopiśmie
Typ:: artykuł w czasopiśmie wyróżnionym w JCR
Opublikowano w:: NEUROCOMPUTING nr 150, strony 50 - 57,
ISSN: 0925-2312
Język:: angielski
Rok wydania:: 2014
Opis bibliograficzny:: Wang P., Sanin C., Szczerbicki E.: Evolutionary algorithm and decisional DNA for multiple travelling salesman problem// NEUROCOMPUTING. -Vol. 150, nr. Part A (2014), s.50-57
Weryfikacja:: Politechnika Gdańska