الفهرس 
CHAPTER 1 : A Survey on Related TopicsOnly 14 pages are availabe for public view
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Abstract
A major thrust in algorithmic development is the design of algorithmic models to solve increasingly complex problems. Enormous successes have been achieved through the modeling of biological and natural intelligence, resulting in so-called “intelligent systems”. These intelligent algorithms include evolutionary computation and swarm intelligence. Together with logic, deductive reasoning, expert systems, case-based reasoning and symbolic machine learning systems, these intelligent algorithms form part of the field of artificial intelligence (AI). Just looking at this wide variety of AI techniques, AI can be seen as a combination of several research disciplines, for example, engineering and computer science. Multi-objective optimization has become an important research topic for both researchers and practitioners in science, engineering, government and industry. Also there is a strong interest in developing the optimization techniques for solving the multi-objective optimization problems. These problems are characterized by the existence of multiple, often conflicting, criteria. In the absence of information about the relative importance of each objective, such optimization problems typically allow numerous solutions to exist, known as Pareto-optimal solutions. These solutions are optimal in the wider sense, meaning that no other solutions in the search space are superior to them when all objectives are considered. For each of the solutions no further improvement in one objective can be obtained without sacrificing performance in other objectives. Searching through the Pareto front for a solution appropriate from the perspective of the designer is crucial for the effectiveness of the multi-objective design. The conventional optimization methods such as dynamic programming (DP), linear programming (LP), and non-linear programming (NLP) are not suitable for solving multi-objective optimization problems (MOPs), because these methods use a point by- point approach, and their outcome is a single optimal solution. For example, the weighted sum method will convert the MOP into a single objective optimization. By using a single pair of fixed weights, only one point on the Pareto front can be obtained. Therefore, if one would like to obtain the global Pareto optimum, all possible Pareto fronts must first be derived. Also conventional methods may face problems, if the optimal solution lies on non convex or disconnected regions of function space. The past few years have witnessed the development of a plethora of multiobjective evolutionary algorithms (EAs) to multi-objective optimization problems due to their ability to:
(i) find multiple solutions in a single run.
(ii) work without derivatives.
(iii) converge speedily to Pareto-optimal solutions with a high degree of accuracy.
(iv) handle both continuous and combinatorial optimization problems with ease, and
(v) be less susceptible to the shape or continuity of the Pareto front. These issues are real concerns for mathematical programming techniques. In recent past several nature-inspired meta-heuristics like the differential evolution (DE), particle swarm optimization (PSO), bacterial foraging optimization (BFO) , etc. have been applied to solve the multi-objective optimization problems. In this thesis, a hybrid optimization algorithm that integrates the merits of ant colony optimization (ACO) and firefly algorithm (FA) is systematically used to solve multi-objective optimization problems. Also the thesis is mainly focused on the original principle behind each of the algorithms and their applications are discussed. However, to our knowledge, hybridizing ant colony optimization and firefly algorithm has not yet been used for the same purpose till date. The ACO is an artificial intelligence technique to solve optimization problems that has been inspired by the ants’ social behaviors in finding shortest paths. Real ants walk randomly until they find food and return to their nest while depositing pheromone on the ground in order to mark their preferred path to attract other ants to follow. If other ants travel along the path and find food, they will deposit more pheromone as to reinforce the path for more ants to follow. Consequently, this strategy leads to discover the shortest paths. In the past decades substantial amount of research has been done to both develop the ACO algorithm itself and practical applications to solve the relevant problems in the real world. The initial ACO algorithm, ant system (AS), was proposed by Marco Dorigo in 1992 in his PhD thesis and in the early twenties.