A multiobjective fleet location problem solved by adaptation of evolutionary algorithms NSGA-II and SMS-EMOA
The problem of locating naval platforms in the operation region with the aim of maximizing both total radar coverage and critical radar coverage is solved by using Multiobjective Evolutionary Algorithms (MOEA). Non-Dominated Sorting Genetic Algorithm-II (NSGA-II) and S-Metric Selection Evolutionary...
| Main Author: | |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Pamukkale University
2018-02-01
|
| Series: | Pamukkale University Journal of Engineering Sciences |
| Subjects: | |
| Online Access: | https://dergipark.org.tr/tr/pub/pajes/issue/35876/400819 |
| Summary: | The
problem of locating naval platforms in the operation region with the aim of
maximizing both total radar coverage and critical radar coverage is solved by
using Multiobjective Evolutionary Algorithms (MOEA). Non-Dominated Sorting
Genetic Algorithm-II (NSGA-II) and
S-Metric Selection Evolutionary Multiobjective Optimization Algorithm
(SMS-EMOA) procedures are implemented. Experiments show that evolutionary
algorithms provide good and diverse alternatives that are considered to be very
close to Pareto-optimal front. The performances of NSGA-II and SMS-EMOA
approaches are compared employing the hypervolume indicator technique. The
performance of NSGA-II is found better in terms of both convergence and
diversity |
|---|---|
| ISSN: | 1300-7009 2147-5881 |