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

Öz

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.

Anahtar Kelimeler

• Filo konumlandırma,Optimal sensör yerleşimi,Çok amaçlı evrimsel algoritmalar

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

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

Keywords

• Fleet location,Optimal sensor placement,Multiobjective evolutionary algorithms

References

1. Ball MG, Qela B, Wesolkowski S. A Review of the Use of Computational Intelligence in the Design of Military Surveillance Networks. Editors: Abielmona R, Falcon R, Zincir-Heywood N, Abbass HA. Recent Advances in Computational Intelligence in Defense and Security,663-693, Berlin, Springer, 2016.
2. Deb K, Pratap A, Agrawal S, Meyarivan T. “A fast and elitist multiobjective genetic algorithm: NSGA-II”. IEEE Transactions on Evolutionary Computation, 6(2), 849-858, 2002.
3. Beume N, Naujoks B, Emmerich M. “SMS-EMOA: multiobjective selection based on dominated hypervolume”. European Journal of Operational Research, 181(3), 1653-1669, 2007.
4. Sakr Z, Wesolkowski S. “Sensor network management using multiobjective evolutionary optimization”. IEEE Symposium on Computational Intelligence for Security and Defense Applications (CISDA), Paris, France, 11-15 April 2011.
5. Oh SC, Tan CH, Kong FW, Tan YS, Ng KH, Ng GW, Tai K. “Multiobjective optimization of sensor network deployment by a genetic algorithm”. IEEE Congress on Evolutionary Computation(CEC), Singapore, 25-28 September 2007.
6. Han JK, Park BS, Choi YS, Park HK. “Genetic approach with a new representation for base station placement in mobile communications”. Vehicular Technology Conference, Atlantic City, USA, 07-11 October 2001.
7. Jiang X, Chen YP, Yu T. “Localized distributed sensor deployment via coevolutionary computation”. 3rd International Conference Communications and Networking, Hangzou, China, 13-16 September 2008.
8. Fei Z, Li B, Yang S, Xing C, Chen H, Hanzo L. “A Survey of Multi-objective Optimization in Wireless Sensor Networks: Metrics, Algorithms and Open Problems”. IEEE Communications Surveys & Tutorials, 19(1), 550-586, 2016.

9. Bugajska MD, Schultz AC. “Co-Evolution of form and function in the design of autonomous agents: Micro air vehicle project”. Genetic and Evolutionary Computation Conference, Washington DC, USA, 08-12 July 2000.
10. Bugajska MD, Schultz AC. “Coevolution of form and function in the design of micro air vehicles”. NASA/DoD Conference on Evolvable Hardware, Alexandria, VA, USA, USA, 15-18 July 2002.
11. Chaudhry SB, Hung VC, Guha RK, Stanley KO. “Pareto-based evolutionary computational approach for wireless sensor placement”. Engineering Applications of Artificial Intelligence, 24(3), 409-425, 2011.
12. Martins FV, Carrano EG, Wanner EF, Takahashi RH, Mateus GR. “A hybrid multiobjective evolutionary approach for ımproving the performance of wireless sensor networks”. IEEE Sensors Journal, 11(3), 545-554, 2011.
13. Khalesian M, Delavar MR. “Wireless sensors deployment optimization using a constrained pareto-based multi-objective evolutionary approach”. Engineering Applications of Artificial Intelligence, 53, 126-139, 2016.
14. Jameii SM, Faez K, Dehghan M. “Multiobjective optimization for topology and coverage control in wireless sensor networks”. International Journal of Distributed Sensor Networks, 11(2), 1-11, 2015.
15. Sengupta S, Das S, Nasir M, Vasilakos AV, Pedrycz W. “An evolutionary multiobjective sleep-scheduling scheme for differentiated coverage in wireless sensor networks”. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 42(6), 1093-1102, 2012.
16. Sengupta S, Das S, Nasir MD, Panigrahi BK. “Multi-objective node deployment in wsns: in search of an optimal trade-off among coverage, lifetime, energy consumption, and connectivity”. Engineering Applications of Artificial Intelligence, 26(1), 405-416, 2013.
17. Bara’a AA, Khalil EA, Cosar A. “Multi-objective evolutionary routing protocol for efficient coverage in mobile sensor networks”. Soft Computing, 19(10), 2983-2995, 2015.
18. Ball MG, Wesolkowski S. “Sensor network placement for maximizing detection of vehicle tracks and minimizing disjoint coverage areas”. IEEE International Symposium on Systems Engineerinf (ISSE), Rome, Italy, 28-30 September, 2015.
19. Küçükali B. Fleet Location and Fuel Consumption Simulation in Naval Combat. MS Thesis, Middle East Technical University, Ankara, Turkey, 2002.
20. Knowles JD, Corne DW, Fleischer M. “Bounded archiving using the lebesgue measure”. Congress on Evolutionary Computation (CEC'03), Canberra, ACT, Australia, 8-12 December 2003.
21. Knowles J, Corne D. “Properties of an adaptive archiving algorithm for storing nondominated vectors”. Evolutionary Computation, IEEE Transactions, 7(2), 100-116, 2003.
22. Zitzler E, Thiele L. “Multiobjective optimization using evolutionary algorithms-a comparative case study”. International Conference on Parallel Problem Solving from Nature, Amsterdam, The Netherlands, 27-30 September 1998.
23. Fleischer M. “The measure of pareto optima applications to multi-objective metaheuristics”. International Conference on Evolutionary Multi-Criterion Optimization, Faro, Portugal, 08-11 April 2003.