Sage Journals: Discover world-class research

Abstract

This study is to investigate the nonlinearly constrained various signal detector location allocation problems in which the types of detectors and the corresponding numbers and locations can be determined at the same time so as to minimize the maximum detecting failure rate in a specified area. In other words, the objective of the detector location allocation problem is to minimize the maximum failure rate by determining the best possible conjunction of three types of decision variables, i.e., the type of detector, the numbers of each detector type and where to build up each of them with a limited resources. So, the quality of reliability of event detecting can be assured and consistent. By the way, the signal intensity usually disintegrates proportionally to some power of the distance from the detector. That makes the longer distance far away the detector, the bigger failure rate in detection of the event. The signal detector allocation problem is described as a mixed-integer nonlinear programming model, usually using math programming or heuristic optimization methods for finding the optimal solution or near optimal solution. While using the both methods, the difficulties encountered are the amount of decision variables and the difficulty of not violating the constraints. In this study, a two-phase evolutionary computation approach based on the immune algorithm and particle swarm optimization has been developed for overcoming the difficulties and finding the optimal solutions for the detector allocation problems effectively. Finally, the performance of the proposed methodology has been evaluated with the commercial optimization software. Numerical results illustrate that our approach is with well performance for the constrained detector allocation problems considered in this paper. As reported, solutions acquired by using our approach are as well as or better than those found by using LINGO^®.

Keywords

Location allocation mixed-integer nonlinear programming evolutionary approach

Get full access to this article

View all access options for this article.

References

T.-C.

Chen and

P.-S.

You , Immune algorithm based approach for redundant reliability problems, Computers in Industry 56 (2005), 195–205.

L.N.

de Castro and

Timmis , Artificial Immune Systems: A new Computational Intelligence Approach, Springer, New York, 2000.

L.N.

de Castro and

F.J.

Von Zuben , The clonal selection algorithm with engineering applications, In Workshop Proceedings of the GECCO 2000 (2000), 36–37.

Drezner and

G.O.

Wesolowsky , On the best location of signal detectors, IIE Transactions 29 (1997), 1007–1015.

N.K.

Jerne , The immune system, Scientific America 229(1) (1973), 52–60.

N.K.

Jerne , Clonal selection in lymphocyte network, in: G.M.

Edelman

, ed., Cellular Selection and Regulation in the Immune Response, Raven Press, New York, 1974.

Y.C.

Hsieh ,

Y.C.

Lee ,

P.S.

You and

T.C.

Chen , An immune based two-phase approach for the multiple-type surveillance camera location problem, Expert Systems with Applications 36(7) (2009), 10634–10639.

Kennedy , The particle swarm: Social adaptation of knowledge, Proceedings of IEEE International Conference on Evolutionary Computation, 1997, pp. 303–308.

Kennedy and

R.C.

Eberhart , Particle swarm optimization, in Proceedings of IEEE International Conference on Neural Networks, Perth, Australia, IV, 1995, pp. 1942–1948.

10.

J.D.

Farmer ,

N.H.

Packard and

A.S.

Perelson , The immune system, adaptation, and machine learning, Physica 22 (1986), 187–204.

11.

S.J.

Huang , An immune-based optimization method to capacitor placement in a radial distribution system, IEEE Transaction on Power Delivery 15(2) (2000), 744–749.

12.

R.F.

Love ,

J.G.

Morris and

G.O.

Wesolowsky , Facilities Locaton: Models and Methods, North-Holland, New York, 1988.

13.

Plastria , Continuous location problems, in Facility Location: A Survey of Applications and Methods, Z.

Drezner

, ed., Springer-Verlag, New York, 1995, pp. 225–262.

14.

Shi and

R.C.

Eberhart , A modified particle swarm optimizer, in Proceeding of IEEE Congress on Evolutionary Computation Anchorage, Alaska, 1998, pp. 69–73.

15.

Michalewicz , Genetic Algorithms + Data Structures = Evolution Programs, 3rd Edition, Springer-Verlag Berlin Heidelberg, NY, 1996.

16.

D.W.

Coit and

A.E.

Smith , Reliability optimization of series-parallel systems using a genetic algorithm, IEEE Transaction on Reliability 45 (1996), 254–260.

17.

Michalewicz , Genetic Algorithms + Data Structures = Evolution Programs, 3rd edition, Springer-Verlag, Berlin Heidelberg, New York, 1996.

18.

Drezner ,

Brimberg ,

Mladenovic and

Salhi , New heuristic algorithms for solving the planar p-median problem, Computers & Operations Research 62 (2015), 296–304.

19.

R.J.

Kuo ,

Y.H.

Lee ,

F.E.

Zulvia and

F.C.

Tien , Solving bi-level linear programming problem through hybrid of immune genetic algorithm and particle swarm optimization algorithm, Applied Mathematics and Computation 266 (2015), 1013–1026.

Using two-phase evolutionary computation approach for nonlinear constrained signal detectors allocation problems

Abstract

Keywords

Get full access to this article

References