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OPPOSITIONAL BIOGEOGRAPHY-BASED OPTIMIZATION

ergezer, mehmet
http://rave.ohiolink.edu/etdc/view?acc_num=csu1392301939

Abstract Details

2014, Doctor of Engineering, Cleveland State University, Fenn College of Engineering.
This dissertation outlines a novel variation of biogeography-based optimization (BBO), which is an evolutionary algorithm (EA) developed for global optimization. The new algorithm employs opposition-based learning (OBL) alongside BBO migration to create oppositional BBO (OBBO). Additionally, a new opposition method named quasi-reflection is introduced. Quasi-reflection is based on opposite numbers theory and we mathematically prove that it has the highest expected probability of being closer to the problem solution among all OBL methods that we explore. Performance of quasi-opposition is validated by mathematical analysis for a single-dimensional problem and by simulations for higher dimensions. Experiments are performed on benchmark problems taken from the literature as well as real-world optimization problems provided by the European Space Agency. Empirical results demonstrate that with the assistance of quasi-reflection, OBBO significantly outperforms BBO in terms of success rate and the number of fitness function evaluations required to find an optimal solution for a set of standard continuous domain benchmarks. The oppositional algorithm is further revised by the addition of fitness-dependent quasi-reflection which gives a candidate solution that we call xkr. In this algorithm, the amount of reflection is based on the fitness of the individual and can be non-uniform. We find that for small reflection weights, xkr has a higher probability of being closer to the solution, but only by a negligible amount. As the reflection weight increases, xkr gets closer (on average) to the solution of an optimization problem as the probability of being closer decreases. In addition, we extend the idea of opposition to combinatorial problems. We introduce two different methods of opposition to solve two types of combinatorial optimization problems. The first technique, open-path opposition, is suited for combinatorial problems where the final node in the graph does not have be connected to the first node such as the graph-coloring problem. The latter technique, circular opposition, can be employed for problems where the endpoints of a graph are linked such as the well-known traveling salesman problem (TSP). Both discrete opposition methods have been hybridized with biogeography-based optimization (BBO). Simulations on standard graph-coloring and TSP benchmarks illustrate that incorporating opposition into BBO improves performance.
Dan Simon (Committee Chair)
Murad Hizlan (Committee Member)
Hanz Richter (Committee Member)
Iftikhar Sikder (Committee Member)
Sailai Shao (Committee Member)
198 p.
Computer Engineering; Computer Science; Electrical Engineering; Operations Research
evolutionary algorithms, optimization, opposition

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Citations

  • ergezer, M. (2014). OPPOSITIONAL BIOGEOGRAPHY-BASED OPTIMIZATION [Doctoral dissertation, Cleveland State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=csu1392301939

    APA Style (7th edition)

  • ergezer, mehmet. OPPOSITIONAL BIOGEOGRAPHY-BASED OPTIMIZATION. 2014. Cleveland State University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=csu1392301939.

    MLA Style (8th edition)

  • ergezer, mehmet. "OPPOSITIONAL BIOGEOGRAPHY-BASED OPTIMIZATION." Doctoral dissertation, Cleveland State University, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=csu1392301939

    Chicago Manual of Style (17th edition)

csu1392301939
724
© 2014, all rights reserved.
This open access ETD is published by Cleveland State University and OhioLINK.