Abstract.
A variety of methods and algorithms have been developed to solve NP-Hard problems in recent decades. In this paper, we are concerned with a relatively new algorithm based on animal behavioral adaptability and evolutionary computation, namely predatory search. When first introduced, the algorithm was implemented with restrictions based on solution cost as a simplification of distance adopted by search-intensive predators. Our research concentrates on exploring the possibility of using distance to restrict search area. Based on the research of Boese et al. (1994), we propose a type of predatory search algorithm restricted by solution distance (particularly bond distance), and compare it with the original algorithm based on three benchmark traveling salesman problems. The results indicate that both algorithms are suitable for solving the traveling salesman problems, while our proposed algorithm either outperforms or at least matches its predecessor with respect to both the running time and the quality of solutions. In addition, further experiments suggest that there exists a certain relationship between the two algorithms.
Similar content being viewed by others
References
R Agarwala DL Applegate D Maglott GD Schuler AA Schaffler (2000) ArticleTitleA fast and scalable radiation hybrid map construction and integration strategy Genome Res 10 350–364 Occurrence Handle1:CAS:528:DC%2BD3cXitVWkt7k%3D Occurrence Handle10720576
Applegate DL, Bixby R, Chvátal V, Cook W (2001) Applications of TSP. http://www.tsp.gatech.edu//apps/scan.html
WJ Bell (1990) ArticleTitleSearching behavior patterns in insects Annu Rev Entomol 35 447–467
R Bellman (1957) Dynamic programming Princeton University Press Princeton
Bland RE, Shallcross DF (1987) Large traveling salesman problem arising from experiments in X-ray crystallography: a preliminary report on computation.Technical Report No. 730, School of OR/IE, Cornell University, Ithaca
KD Boese AB Kahng S Muddu (1994) ArticleTitleA new adaptive multi-start technique for combinatorial global optimizations Oper Res Lett 16 101–113
APS Braga AFR Araujo (2003) ArticleTitleA topological reinforcement learning agent for navigation Neural Comput Appl 12 220–236
A Caprara (1990) ArticleTitleSorting permutations by reversals and eulerian cycle decompositions SIAM J Discrete Math 12 IssueID1 91–110
E Curio (1976) The ethology of predation Springer Berlin Heidelberg New York
M Dorigo V Maniezzo A Colorni (1996) ArticleTitleThe ant system: Optimization by a colony of cooperating agents IEEE Trans Syst Man Cybern 26 29–41
YA Gan F Tian WZ Li et al. (1990) The principle of operational research Tsinghua University Press Beijing 247–248
MR Garey DS Johnson (1979) Computers and intractability: a guide to the theory of NP-completeness WH Freeman New York
F GLOVER (1989) ArticleTitleTabu search – Part I ORSA J Comput 1 190–206
F GLOVER (1990) ArticleTitleTabu search – Part II ORSA J Comput 2 4–32
JH Holland (1975) Adaptation in natural and artificial systems University of Michigan Press Ann Arbor
JJ Hopfield DW Tank (1985) ArticleTitleNeural computation of decisions in optimization problems Biol Cybern 52 141–152 Occurrence Handle1:STN:280:BiqB1c%2FmtFY%3D Occurrence Handle4027280
TC Hu AB Kahng CWA Tsao (1995) ArticleTitleOld bachelor acceptance: a new class of non-monotone threshold accepting methods INFORMS J Comput 7 IssueID4 417–425
RB Huey ER Pianka (1981) ArticleTitleEcological consequences of foraging mode Ecology 62 991–999
DS Johnson LA McGeoch (1997) Local search in combinatorial optimization Wiley London 215–310
P Kareiva G Odell (1987) ArticleTitleSwarms of predators exhibit ‘preytaxis’ if individual predators use area-restricted search Am Nat 130 233–270
J Kennedy RC Eberhart (1995) ArticleTitleParticle swarm optimization Proc IEEE Int Conf Neural Netw 4 1942–1948
S Kirkpatrick JR Gellat MP Vecchi (1983) ArticleTitleOptimization by simulated annealing Science 220 671–680
AH Land AG Doig (1960) ArticleTitleAn automatic method for solving discrete programming problems Econometrica 28 497–520
A Linhares (1998a) ArticleTitleState-space search strategies gleaned from animal behavior: a traveling salesman experiment Biol Cybern 78 167–173
A Linhares (1998b) ArticleTitlePreying on optima: a predatory search strategy for combinatorial problems Proc IEEE Int Conf Sys Man Cybern 3 2974–2978
A Linhares (1999) ArticleTitleSynthesizing a predatory search strategy for VLSI layouts IEEE Trans Evol Comput 3 147–152
A Linhares (2004) ArticleTitleThe Structure of Local Search Diversity WSEAS Trans Math 3 IssueID1 216–220
K Nakamuta (1985) ArticleTitleMechanism of the switchover from extensive to area concentrated search behavior of the ladybird beetle, Coccinella septempuctata bruckii J Insect Physiol 31 849–856
CR Reeves (1999) ArticleTitleLandscapes, operators, and heuristic search Ann Oper Res 86 473–490 Occurrence HandleMR1683466
G Reinelt (1991) ArticleTitleTSPLIB: a traveling salesman problem library ORSA J Comput 3 376–384
JNM Smith (1974) ArticleTitleThe food searching behavior of two European thrushes II. The adaptiveness of the search patterns Behavior 59 1–61
L Wang (2001) AI-based optimization approaches and their application Tsinghua University Press and Springer Beijing, China 11
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Liu, C., Wang, D. Predatory search algorithm with restriction of solution distance. Biol Cybern 92, 293–302 (2005). https://doi.org/10.1007/s00422-005-0550-6
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00422-005-0550-6