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Bat Intelligent Hunting Optimization with Application to Multiprocessor Scheduling

Kim, Hyun Soo
http://rave.ohiolink.edu/etdc/view?acc_num=case1274471808

Abstract Details

2010, Doctor of Philosophy, Case Western Reserve University, EECS - System and Control Engineering.
In this dissertation, we introduce a novel heuristic, Bat Intelligent Hunting, for the first time. Similar to many existing heuristics, the Bat Intelligent Hunting provides a framework for solving various optimization problems. As the name suggests, Bat Intelligent Hunting models the prey hunting behaviors of bats. Bats locate and capture preys without using their eyesight by utilizing echolocation and Constant Absolute Target Approach (CATD) techniques. Bat Intelligent Hunting implements these concepts to converge towards the optimal solution. To illustrate the performance of Bat Intelligent Hunting, we employ Bat Intelligent Hunting to solve the Multiprocessor Scheduling Problem (MSP). The MSP deals with assigning a given set of tasks to a set of processors as to optimize specified objective(s). We solve two different types of MSPs: a) MSP without voltage scaling and b) MSP with voltage scaling. In the first problem, energy is not considered as one of the objectives, but in the second problem, energy is included as one of the objectives. In both problems, we performed multiple objective optimization using the Normalized Weighted Additive Utility Function where we use a set of importance of objective values (weights) to identify a set of efficient solutions. In the first problem, we solve for two objectives of minimizing makespan and tardiness. We initially solved each objective separately, and then perform bi-objective optimization. In the single objective MSP, on average, the Bat Intelligent Hunting outperformed the list algorithm and the Genetic Algorithm by 11.12% and by 23.97% when solving for makespan and tardiness, respectively. In bi-objective MSP, the Bat Intelligent Hunting identified a set of efficient solutions. In the second problem, we first solve two cases of the bi-objective MSPs with objectives to: a) minimize makespand and energy; and b) minimize tardiness and energy. We then solve the tri-objective MSP with the objectives to: minimize makepsan, tardiness, and energy. In all of our simulations, Bat Intelligent Hunting identified a set of solutions that correspond to the assigned weights and showed the multi-objective behaviors of the multiple objective MSP.
Behnam Malakooti (Committee Chair)
Frank Merat (Committee Member)
Swarup Bhunia (Committee Member)
Vira Chankong (Committee Member)
123 p.
Computer Science; Systems Design
heuristic; multiprocessor scheduling, multiple objective optimization

Recommended Citations

Citations

  • Kim, H. S. (2010). Bat Intelligent Hunting Optimization with Application to Multiprocessor Scheduling [Doctoral dissertation, Case Western Reserve University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=case1274471808

    APA Style (7th edition)

  • Kim, Hyun. Bat Intelligent Hunting Optimization with Application to Multiprocessor Scheduling. 2010. Case Western Reserve University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=case1274471808.

    MLA Style (8th edition)

  • Kim, Hyun. "Bat Intelligent Hunting Optimization with Application to Multiprocessor Scheduling." Doctoral dissertation, Case Western Reserve University, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=case1274471808

    Chicago Manual of Style (17th edition)

case1274471808
932
© 2010, all rights reserved.
This open access ETD is published by Case Western Reserve University School of Graduate Studies and OhioLINK.