الفهرس 
The General Multiobjective Optimization ProblemOnly 14 pages are availabe for public view
 |  | from | 184 |  |  |
| | | from | 184 | | |
Abstract
Multiobjective optimization is with no doubt a very important research topic both for scientists and engineers, because of the multiobjective nature of most real-world problems. A solution may be best in one objective but worst in another. Therefore, there usually exist a set of solutions. For such solutions, called nondominated solutions or Pareto optimal solutions 0
Classical methods use a very different philosophy in solving these problems, mainly because of a lack of a suitable optimization methodology to find multiple optimal solutions efficiently. They usually require repetitive applications of an algorithm to find multiple Pareto optimal solutions and on some occasions such applications do not even guarantee finding certain Pareto optimal solutions. In contrast, the evolutionary algorithms (i.e., such as genetic algorithm, particle swarm optimization and ant colony optimization (ACO)) allow an efficient way to find multiple Pareto optimal solutions simultaneously in a single simulation run. This aspect has made the research and application in evolutionary multiobjective optimization popular in the past decade.
Marco Dorigo introduced the first ACO in the early 1990’s. ACO takes inspiration from the foraging behavior of some ant species. These ants deposit pheromone on the ground in order to mark some favorable path that should be followed by other members of the colony. Therefore, the principal goal of this thesis was to implement a specialized version of the ACO capable of finding a set of solutions for multiobjective optimization problems.
The thesis outlined is follows:
CHAPTER 1: summaries the basic concepts of multiobjective optimization problems, some traditional methods for solving multiobjective optimization and some of the evolutionary algorithms which handling the drawbacks in the traditional methods.
CHAPTER 2: introduces a survey on ant colony optimization for single and multiobjective optimization problems.
CHAPTER 3: proposes a multipheromone ant colony optimization, where different colonies of ants are adopted. This approach is used to solve the multiobjective resource allocation problems and the multiobjective human resource allocation problems. Convergence analysis is described.
CHAPTER 4 : presents a hybrid approach of ant colony optimization and steady state genetic algorithm for solving multiobjective optimization problems in the continuous domain.
CHAPTER 5: discusses the possibilities of the proposed approach to solve two applications in electrical problems. The first one involves an optimal design of a linear motor and the other involves an optimal design of air-cored solenoid.
CHAPTER 6: introduces the concluding remarks, recommendations and some points for future research.