Evolutionary algorithm and decisional DNA for multiple travelling salesman problem
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 knowle...
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Veröffentlicht in: | Neurocomputing (Amsterdam) 2015-02, Vol.150, p.50-57 |
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creator | Wang, Peng Sanin, Cesar Szczerbicki, Edward |
description | 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. |
doi_str_mv | 10.1016/j.neucom.2014.01.075 |
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subjects | Algorithms Data processing Decisional DNA (DDNA) Deoxyribonucleic acid Evolutionary Evolutionary algorithm Evolutionary algorithms Genetic algorithm Heuristics Hybrid structures Mathematical models Optimization Optimization problem Set of experience knowledge structure (SOEKS) |
title | Evolutionary algorithm and decisional DNA for multiple travelling salesman problem |
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