S. Shojaee, M. Arjomand, M. Khatibinia,
Volume 3, Issue 1 (3-2013)
Abstract
An efficient method for size and layout optimization of the truss structures is presented in this paper. In order to this, an efficient method by combining an improved discrete particle swarm optimization (IDPSO) and method of moving asymptotes (MMA) is proposed. In the hybrid of IDPSO and MMA, the nodal coordinates defining the layout of the structure are optimized with MMA, and afterwards the results of MMA are used in IDPSO to optimize the cross-section areas. The results show that the hybrid of IDPSO and MMA can effectively accelerate the convergence rate and can quickly reach the optimum design.
Kh. Soleymanian, S. M. Tavakkoli,
Volume 15, Issue 2 (4-2025)
Abstract
This study aims to deal with multi-material topology optimization problems by using the Methods of Moving Asymptotes (MMA) method. The optimization problem is to minimize the strain energy while a certain amount of material is used. Several types of structures, including plane, plate and shell structures, are considered and optimal materials distribution is investigated. To parametrize the topology optimization problem, the Solid Isotropic Material with Penalization (SIMP) method is utilized. Analytical sensitivity analysis is performed to obtain the derivatives of the objective function and volume constraints with respect to the design variables. Two types of material with different modulus of elasticities are considered and, therefore, each element has two design variables. The first design variable represents the presence or absence of material in an element, while the second design variable determines the type of material assigned to the element. In order to analyze the structures required during the optimization process, the ABAQUS software is employed. To integrate the topology optimization procedure with ABAQUS model, a Python script is developed. The obtained results demonstrate the performance of the proposed method in generating reasonable and effective topologies.