| Optimization technology is a technology based on mathematics and applied to all kinds of optimization solution to engineering problems. As an important branch of science, it is always attached wide importance to and rapidly extended and applied to industry and economy, and so on. Because of complication, large-scale, multi- extremum and difficulties of modeling in engineering optimization, it has been a main research object and direction that finds an intelligent and general-purpose global optimization method for large-scale parallelization.Classification of global optimization methods is always divided into deterministic and probabilistic methods. The former has the strictness and integrity in mathematics, but they are difficult to apply in engineering. Typical probabilistic method is difficultly applied to large-scale and complicated global optimization problems. Heuristics algorithms become a new development phase of deterministic and probabilistic methods and global optimization methods, especially metaheuristics algorithms.Since 80's a of 20th century, some novel heuristics algorithms "metaheuristics algorithms" are created by simulating or showing some natural phenomena or process, such as Ant Colony Optimization, Simulated Annealing, Genetic Algorithms and Taboo Search. The particular advantage and mechanism of these algorithms have brought a hot-spot of research, especially Ant Colony Optimization developed recent ten years.Ant Colony Optimization is the main content of this project. Because of its simple mechanism, strong lustiness and effective parallelization, it has been attracting more and more researchers and successfully applied to production problem such as scheduling problems and routing problems. At present the research results of Ant Colony Optimization are not concentrated and the development of theory is not matured. In this thesis the research results of Ant Colony Optimization at home and abroad are summarized and new Improved Ant Colony Optimization algorithms are put forward. Its successful application in combination optimization problems makes our focus on its application in continuous optimization problems. Now studies of Ant Colony Optimization for continuous optimization problems are not sufficient, but primary studies have shown its good performance. Multi- extremum global optimization problems are emphasized in this thesis, and after much testing by using a new Ant Colony Optimization "Ant Colony Optimization based on Grid methods", the good performance of the new algorithm isproved. It is expected that the applications of Ant Colony Optimization to continuous optimization problems are more and more successful with improvement of its theory. The following is the main research contents and results of this thesis:(1) The basic frame and study progress of global optimization methods is summarized. The characteristics, construction principle, classification and disadvantage of global optimization methods are systematically discussed respectively.(2) The theories and methods of heuristics search algorithms developed rapidly recently are studied systematically. The study includes the generation, construction methods and basic types of heuristics search algorithms. Some metaheuristics algorithms are summarized: Ant Colony Optimization, Simulated Annealing, Genetic Algorithms, Taboo Search and Chaos Algorithms.(3) Studying the development of Ant Colony Optimization in detail, characteristics of all kinds of Improved Ant Colony Optimization are compared and analyzed. By program testing, the results show that the new algorithm's performance is satisfying. Based on study of Ant Colony Optimization for combination optimization problems, a new algorithm for continuous optimization problems is put forward-Improved Ant Colony Optimization based grid methods. After testing many functions, it is found that this algorithm can well solve some optimization problems of multi-extremum functions.(4) Based on detailedly describing and analyzing generalized neighbourhood algorithm and its unifi...
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