Multiobjective Fitness Landscape Analysis and the Design of Effective Memetic

J. Deon Garrett. Ph.D. The University of Memphis. February, 2008. Multiobjective Fitness Landscape Analysis and the Design of Effective Memetic Algorithms. Major Professor: Dipankar Dasgupta, Ph.D. For a wide variety of combinatorial optimization problems, no efficient algorithms exist to exactly solve the problem unless P=NP. For these problems, metaheuristics have come to dominate the landscape. Encompassing several widely used techniques such as simulated annealing, tabu search, evolutionary algorithms, ant colony optimization, and other methods, metaheuristics provide the state of the art for many such problems. In recent years, researchers in these areas have begun to to consider problems with multiple objectives. As the number of algorithms proposed to solve multiobjective optimization problems has increased, it has become apparent that there is a general lack of understanding of the issues governing the performance of different types of algorithms. The primary motivation for this work is to improve the state of multiobjective optimization by providing a means by which practitioners may obtain a better understanding of the important issues governing multiobjective optimization algorithm performance. The goal of this work is thus to provide a framework for multiobjective fitness landscape analysis that can be used to gain a greater understanding of the interaction between problem structure and search algorithm performance in multiobjective optimization. Under the umbrella of multiobjective fitness landscape analysis, a number of techniques are proposed that provide researchers with important information concerning particular types of problems and how to effectively solve them. The utility of the methods described within is demonstrated using a set of three benchmark classes of assignment problems: the Quadratic Assignment