The Optimal Design For Crane Main Box Beam Based On Pareto Genetic Algorithms

Engineering design problems generally involve several design objectives. These objectives, related to the technical and economical performance of the engineering system, are potentially conflicting in nature. Multi-Objective Optimization (MOO) has grown to the point of gaining near universal recognition in its ability to lead to better designs, both in the areas of academic research and practical application.On the basis of studying the Genetic Algorithms (GA) and MOO theory and application, the Multi-Objective Genetic Algorithms (MOGA) program, used to deal with the Multi-Objective Optimal Design (MOOD) problem in the general project, is developed. Several important problems involved in MOGA, including the ranking of Pareto solutions and the measure of fitness degree etc, are discussed. How to set up mathematics model is studied. The optimal result for the design of an overhead traveling crane main box beam shows that MOGA program developed by author is feasible to deal with the MOOD problem in the general project. The main work and conclusions are as follows:1) Multi-Objective Optimization theoretical foundation is preliminary researched, and several traditional Multi-Objective Optimization methods are briefly introduced and compared.2) Genetic Algorithms are researched in detail, and some questions about how to use Genetic Algorithms in Multi-Objective Optimization are discussed.3) The process of Multi-Objective Pareto Genetic Algorithms on the basis of the Pareto optimal conception is presented, and several important problems involved in MOGA, including the ranking of Pareto solutions and the measure of fitness degree etc, are discussed.4) The application of this algorithms program in overhead traveling crane main box bean design shows that MOGA program is feasible to deal with the Multi-Objective Optimal Design problem.