Abstract
In this paper, we present the bat intelligence search for the first time. Bat intelligence is a novel and unique heuristic that models two major prey hunting behaviors of bats: (a) utilization of echolocation to observe the environment and (b) employment of constant absolute target direction approach to pursue preys. In order to illustrate the performance of bat intelligence, we implement this heuristic to solve two types of multiprocessor scheduling problems (MSP): single objective MSP and multi-objective MSP. In single objective MSP, we independently solve for minimization of makespan and minimization of tardiness. In multiple objective MSP, these two objectives are optimized simultaneously. In the single objective MSP, on average, the bat intelligence outperformed the list algorithm and the genetic algorithm by 11.12% when solving for minimization of makespan and by 23.97% when solving for the minimization of tardiness. In comparison to the genetic algorithm, the bat intelligence produces better results for the same computational effort. In multiple objective MSP, bat intelligence is combined with normalized weighted additive utility function to generate a set of efficient solutions by varying the weights of importance. The results demonstrate that the bat intelligence finds a set of Pareto optimal solutions on bi-objective optimization of MSP.
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References
Ahmad I, Dhodhi M (1996) Multiprocessor scheduling in a genetic paradigm. Parallel Computing 22:395–406
Blazewicz J, Ecker KH, Pesch E, Schmidt G, Weglarz J (2007) Handbook on scheduling: from theory to applications, 1st edn. Springer, Berlin
Bhunia S, Datta A, Banerjee N (2005) GAARP: a power-aware GALS architecture for real-time algorithm-specific tasks. IEEE Trans Comput 54(6):752–766
Boctor FF, Renaud J, Ruiz A, Tremblay S (2009) Optimal and heuristic solution methods for a multiprocessor machine scheduling problem. Comput Oper Res 36(10):2822–2828
Chena Y, Marc Kilgoura D, Hipel KW (2008) A case-based distance method for screening in multiple-criteria decision aid. Omega 36(3):373–383
Chena R-M, Wub C-L, Wanga C-M, Lo S-T (2010) Using novel particle swarm optimization scheme to solve resource-constrained scheduling problem in PSPLIB. Expert Systems with Applications 37(3):1899–1910
Corrêa R, Ferreira A (1999) Scheduling multiprocessor tasks with genetic algorithms. IEEE Transactions on Parallel and Distributed Systems 10(8):825–837
Deb K, Pratap A, Moitra S (2000) Mechanical component design for multiple objectives using elitist non-dominated sorting GA. Lecture Notes in Computer Science, pp 859–868
Dukas R, Ratcliffe JM (2009) Cognitive ecology II. The University of Chicago Press, Chicago
Fang H, Wang Q, Tu Y, Horstemeyer F (2008) An efficient non-dominated sorting method for evolutionary algorithms. Evol Comput 16(3):355–384
Fenton MB (1990) The foraging behavior and ecology of animal-eating bats. Can J Zool 86:411–422
Ghose K, Horiuchi T, Krishnaprasad PS, Moss C (2004) Echolocating bats use a nearly time-optimal strategy to intercept prey. PLos Biol 4(5):865–873
Glover F (1989) Tabu search—part I, first comprehensive description of tabu search. ORSA J Comput 1(3):190–206
Glover F, Kelly J, Laguna M (1995) Genetic algorithms and tabu search: hybrid for optimization. Comput Oper Res 22(1):111–134
Goldberg DE (1989) Genetic algorithm in search, optimization, and machine learning. Addison-Wesley, Reading
Grinnell AD (1995) Hearing in bats: an overview. In: Popper AN, Fay RR (eds) Hearing by bats. Springer, Berlin
Horn J, Nafpliotis N, Goldberg DE (1994) A niched Pareto genetic algorithm for multiobjective optimization. In: The proceedings of 1st CIEC, pp 82–87
Hou E, Ansari N, Ren H (1994) A genetic algorithm for multiprocessor scheduling. IEEE Transaction on Parallel and Distributed Systems 5(2):113–120
Hwang R, Gen M, Katayama H (2008) A comparison of multiprocessor task scheduling algorithms with communication costs. Comput Oper Res 35(3):976–993
Kalko EKV (1995) Insect pursuit, prey capture and echolocation in pipistrelle bats. Anim Behav 50:861–880
Kirpatrick S, Gelatt CD, Vecchi MP (1983) Optimization by simulated annealing. Science 220:671–681
Korte B, Vygen J (2005) Combinatorial optimization: theory and algorithms (algorithms and combinatorics), 3rd edn. Springer, Berlin
Loa S-T, Chenb R-M, Huang Y-M, Wu C-L (2008) Multiprocessor system scheduling with precedence and resource constraints using an enhanced ant colony system. Expert Syst Appl 34(3):2071–2081
Malakooti B, Duckstein L, Ravindran A (1994) Screening discrete alternatives with imprecisely assessed additive multi-attribute functions. Applied Mathematical Computations 60:69–89
Malakooti B (2009) Systematic decision process for intelligent decision making. J Intell Manufact 22:627–642. doi:10.1007/s10845-009-0327-1
Malakooti B (2000) Ranking and screening multiple criteria alternatives with partial information and use of ordinal and cardinal strength of preferences. IEEE Trans. on Systems, Man, and Cybernetics Part A 30(3):355–369
Malakooti B (2010) Independent, convergent, and divergent decision behavior for interactive multiple objectives linear programming. Eng Optim 42:325–3464
McCreary C, Gill H (1989) Automatic determination of grain size for efficient parallel processing. Comm of ACM 32(9):1073–1078
Merritt JF (2010) The biology of small mammals. John Hopkins University Press, Baltimore
Mtibaa A, Ouni B, Abid M (2007) An efficient list scheduling algorithm for time placement problem. Comput Electr Eng 33(4):285–298
Neuweiler G (1989) Foraging ecology and audition in echolocating bats. Trends Ecol Evol 4:160–166
Ono S, Yoshitake Y, Nakayama S (2009) Robust optimization using multi-objective particle swarm optimization. Comput Oper Res 14(2):174–177
Ratcliffe JM, Elisabeth LJ, Kalko KV, Surlykke A (2011) Frequency alternation and an offbeat rhythm indicate foraging behavior in the echolocating bat, Saccopteryx bilineata. J Comp Physiol A Neuroethol Sens Neural Behav Physiol 197:413–423. doi:10.1007/s00359-011-0630-0
Tarasewich P, McMullen PR (2002) Swarm intelligence: power in numbers. Commun ACM 45(8):62–67
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Malakooti, B., Kim, H. & Sheikh, S. Bat intelligence search with application to multi-objective multiprocessor scheduling optimization. Int J Adv Manuf Technol 60, 1071–1086 (2012). https://doi.org/10.1007/s00170-011-3649-z
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DOI: https://doi.org/10.1007/s00170-011-3649-z