TRUSS SHAPE OPTIMIZATION USING EVOLUTIONARY ANT COLONY OPTIMIZATION

Abstract

Optimization is receiving attention in the capacity of engineering. The technique is applied to a variety of engineering problems such as Pile foundation layout on a footing of the residential house; trees layout; materials decision and structural problems. In structural engineering, morphogenesis by using structural optimization techniques is also receiving attention. There are three kinds of optimization problems: size optimization; topology optimization and shape optimization. In the structural problem, obtaining minimum compliance or minimum weight of structure subjected to constraints such as stress, displacement or volume are the main objectives. The methods by using heuristics such as Genetic Algorithm (GA) or Simulated Annealing (SA) to solve structural optimization problems have been reported recently. Swarm Intelligence (SI), simulating behaviors of biology group in nature, is also being reported. Ant Colony Optimization (ACO), simulating behaviors of ants in nature, is one kind of the SI approach. ACO is a multi-agent approach and ants, the agent, test design points based on random search technique. Hence, it is possible to test a huge design domain by using ACO. However, due to the randomness of ACO, since only the local information is considered, it is not efficient to solve structural optimizations by using ACO. On the other hand, a method of using an evolutionary strategy such as Evolutionary Structural Optimization (ESO) method or Bi-directional Evolutionary Structural Optimization (BESO) method are proved to be suitable for solving a variety of structural optimization problems. The strategy of these methods is updating design variables based on the sensitivity number, defined by the stress or particular displacement which are calculated by the finite element analysis. Sensitivity number is defined by considering the evolutionary mechanisms of the structure. The strategy has been applied for shape optimization of truss problems. By integrating the ESO strategy into the ACO, it is possible to test a huge design domain by using the ACO strategy and modifying the design variables to consider the behavior of structure following the evolutionary mechanisms to solve structural optimization problems. In this study, the Evolutionary Ant Colony Optimization (EACO) is proposed by integrating the ESO strategy into the ACO to solve shape optimization problems of trusses. The EACO is then tested by solving some shape optimization problems of trusses. The results obtained in this study are as follow: 1) By integrating the ESO strategy into the ACO, it is possible to test a huge design domain by using the ACO strategy and modifying the design variables to consider the behavior of truss structure. 2) In the shape optimization of trusses, the optimal shapes can be obtained efficiently by using the EACO. 3) The EACO is a new approach which has been proved to be suitable for solving the shape optimization of truss problems.