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Journals / Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi / 2018 / Cilt: 24 - Sayı: 1

A multiobjective fleet location problem solved by adaptation of evolutionary algorithms NSGA-II and SMS-EMOA

Bir çokamaçlı filo konuşlandırma probleminin NSGA-II ve SMS-EMOA evrimsel algoritmalarının uyarlanması ile çözümü

Ertan YAKICI
Pages
94–100
Publication Date
2018-02-27
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The performance of NSGA-II is found better in terms of both convergence and diversity

📌 3 Key Takeaways

🎯
Objective & Hypothesis: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).
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Key Findings & Data:The performances of NSGA-II and SMS-EMOA approaches are compared employing the hypervolume indicator technique.
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Conclusion & Impact:The performance of NSGA-II is found better in terms of both convergence and diversity
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Filo konumlandırma · Optimal sensör yerleşimi · Çok amaçlı evrimsel algoritmalar

Abstract

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

Özet

Donanma platformlarının, radar toplam kapsama alanları ve radar kritik kapsama alanlarının ençoklanması amacı ile harekât bölgesinde konuşlandırılması problemi çok amaçlı evrimsel algoritmalar kullanılarak çözülmüştür. Bu kapsamda, literatürde Non-Dominated Sorting Genetic Algorithm-II (NSGA-II) ve S-Metric Selection Evolutionary Multiobjective Optimization Algorithm (SMS-EMOA) adı verilen yöntemler kullanılmıştır. Deney uygulamasında, bu yöntemlerin Pareto-optimal cepheye oldukça yakın olduğu değerlendirilen iyi ve istendiği gibi birbirinden farklı çözümler ürettiği görülmüştür. Kullanılan yöntemlerin performansları hipervolüm gösterge tekniği kullanılarak karşılaştırılmış, NSGA-II yönteminin daha iyi performans gösterdiği tespit edilmiştir.

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Cite This Article

YAKICI, E. (2018). A multiobjective fleet location problem solved by adaptation of evolutionary algorithms NSGA-II and SMS-EMOA. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, Cilt: 24 - Sayı: 1, 94–100. https://ajindex.com/articles/42408

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