A multilevel genetic algorithm for the optimum design of structural control systems

Abstract

SUMMARYA multilevel genetic algorithm (MLGA) is proposed in this paper for solving the kind of optimization problems which are multilevel structures in nature and have features of mixed-discrete design variables, multi-modal and non-continuous objective functions, etc. Firstly, the formulation of the mixed-discrete multilevel optimization problems is presented. Secondly, the architecture and implementation of MLGA are described. Thirdly, the algorithm is applied to two multilevel optimization problems. The ÿrst one is a three-level optimization problem in which the optimization of the number of actuators, the positions of actuators and the control parameters are considered in di erent levels. An actively controlled tall building subjected to strong wind action is considered to investigate the e ectiveness of the proposed algorithm. The second application considers a combinatorial optimization problem in which the number and conÿguration of actuators are optimized simultaneously, an actively controlled building under earthquake excitations is adopted for this case study. Finally, some results and discussions about the application of the proposed algorithm are presented.

Optimization of Support Structures for Offshore Wind Turbines Using Genetic Algorithm with Domain‐Trimming

“…This shortcoming inspired researchers to explore using the relatively new and innovative evolutionarybased optimization techniques. For example, these methods have been used recently to address structural engineering optimization problems: Shuffled Complex Evolution Optimizer (SCEO), Ant Colony Optimization (ACO), the Genetic Algorithms (GA), and Particle Swarm Optimizer (PSO) (e.g., [16][17][18][19][20][21][22][23][24]). …”

Simultaneous Topology and Size Optimization of 2D and 3D Trusses Using Evolutionary Structural Optimization with regard to Commonly Used Topologies

“…Genetic algorithms (GA) have also been implied to solve problems regarding topology optimization [5,12,[24][25][26][27]. The genetic algorithm is based on the evolutionary method of natural selection, survival of the fittest, and adaption.…”

Genetic evolutionary structural optimization

“…It was showed that GA can probably converge to global optimality [14][15][16][17]. The principles of GA are individual intercompetition and survival-of-the-fittest in population.…”

“…The GA search is based on the principles of "survival-of-thefittest" and adaptation. Structural optimizations and parameter determinations are two essential applications of GA [14][15][16][17][18]. In this paper, the performance of ESO is improved significantly by introducing the concept of probability and survival-of-thefittest of GA to ESO to develop a new method called Genetic Evolutionary Structural Optimization (GESO).…”