Smart Topology Optimization Using Adaptive Neighborhood Simulated Annealing

Abstract

Topology optimization (TO) of engineering products is an important design task to maximize performance and efficiency, which can be divided into two main categories of gradient-based and non-gradient-based methods. In recent years, significant attention has been brought to the non-gradient-based methods, mainly because they do not demand access to the derivatives of the objective functions. This property makes them well compatible to the structure of knowledge in the digital design and simulation domains, particularly in Computer Aided Design and Engineering (CAD/CAE) environments. These methods allow for the generation and evaluation of new evolutionary solutions without using the sensitivity information. In this work, a new non-gradient TO methodology using a variation of Simulated Annealing (SA) is presented. This methodology adaptively adjusts newly-generated candidates based on the history of the current solutions and uses the crystallization heuristic to smartly control the convergence of the TO problem. If the changes in the previous solutions of an element and its neighborhood improve the results, the crystallization factor increases the changes in the newly random generated solutions. Otherwise, it decreases the value of changes in the recently generated solutions. This methodology wisely improves the random exploration and convergence of the solutions in TO. In order to study the role of the various parameters in the algorithm, a variety of experiments are conducted and results are analyzed. In multiple case studies, it is shown that the final results are well comparable to the results obtained from the classic gradient-based methods. As an additional feature, a density filter is added to the algorithm to remove discontinuities and gray areas in the final solution resulting in robust outcomes in adjustable resolutions.

Design of surface micro-machined inertial MEMS sensor with meta-heuristic topology optimization.

“…But still, the values of minimized compliance in Table 3 and optimized shapes in Fig. 3.13 show a good agreement between the proposed method and the GTO in the literature [93].…”

“…The number of iterations should be high enough to make sure the domain is sampled enough to find the global optima independent of the initial solution. The number of iterations is dependent on the physics of the optimization problem, domain, constraints, objective of optimization, and method of generating new solutions [93]. A practical rule of thumb to select a high enough number of iterations is to generate 20N new solutions in each temperature, where N is the number of discrete elements in the design domain [80].…”

Open-Source Codes of Topology Optimization: A Summary for Beginners to Start Their Research

“…Najafabadi et al [137] presented an NGTO method using an adaptive neighborhood simulated annealing (SA), which adaptively adjusted new design variables based on the history of the current solutions and used the crystallization heuristic to smartly control the convergence of the TO problem. The new design variables x are updated by…”

A review of the application of the simulated annealing algorithm in structural health monitoring (1995-2021)

“…Over the past two decades, successful applications of the SA have been reported several times. For example, Bureerat and Limtragool [79], Lamberti [80], Sonmez and Tan [81], Tejani et al [82], Kurtuluş et al [83], Najafabadi et al [84], and Goto et al [85] are a few to be noted. It is necessary to define an objective function based on measured and calculated modal characteristics to solve the model-based inverse problem of damage detection using optimization algorithms [86].…”