Genetic Algorithm Devleopment for Multi-Objective Optimization of Seismic Mixed-Constructued Structures With/ Without Control of Systems

9520804 Cheng该项目涉及智能工程中经常出现的一个基本问题，旨在优化涉及多个变量和加载条件的建筑物和民用基础设施系统的设计。优化设计意味着最大限度地延长使用寿命、安全性和功能性，同时最大限度地减少材料使用、劳动力和维护等。 在多目标优化设计过程中，一个关键步骤和要求是一个计算算法，它可以提供一致的，稳定的解决方案，复杂的优化配方。本计画将发展一种创新的最佳化技术，即基因演算法，以应用于土木结构在地震条件下的设计。 该算法基于达尔文的适者生存策略，通过消除不适合的特征并通过繁殖、重组（交叉）和变异进行随机交换。 重点放在制定的n-父均匀交叉算子和并行化，以实现一个新兴的计算技术的全局和鲁棒的最优解。 作为本研究的一部分，还进行了其他研究工作，包括混合建筑材料的建筑系统的结构建模，建筑结构的多目标函数和约束的制定，有/无控制系统的静态和地震荷载的基于优化的设计准则的发展，以及现有优化设计算法的评估和改进。 ***

9520804 Cheng This project deals with a fundamental problem which often arises in intelligent engineering aimed at optimum design of buildings and civil infrastructure systems where multiple variables and loading conditions are involved. Optimum design means, among other things, to maximize service life, safety and functionality while minimizing material use, labor, maintenance,etc. A critical step and requirement in the process of multi-objective optimal design is a computational algorithm which could give consistent, stable solutions to the complex optimization formulation. In this project, an innovative optimization technique, namely, a genetic algorithm will be developed for civil structural applications under seismic condition. This algorithm is based on the Darwinian survival-of-the-fittest strategy by eliminating unfit characteristics and using random exchange through reproduction, recombination (cross over), and mutation. Emphasis is placed on formulation of n-parent uniform cross- over operator and parallelization to achieve an emerging computing technique for both global and robust optimum solutions. Other research efforts also undertaken as a part of this research include structural modeling of building systems with mixed construction materials, formulation of multi- objective functions and constraints for building structures, development od optimum based design criteria for static and earthquake load with/without control systems, and assessment and improvement of current optimum design algorithms. ***