Multi Objective Design Optimization of Two Bar Truss Using NSGA II and TOPSIS

Abstract

Arriving optimal solutions is one of the important tasks in engineering design. Many real-world design optimization problems involve multiple conflicting objectives. The design variables are of continuous or discrete in nature. In general, for solving Multi Objective Optimization methods weight method is preferred. In this method, all the objective functions are converted into a single objective function by assigning suitable weights to each objective functions. The main drawback lies in the selection of proper weights. Recently, evolutionary algorithms are used to find the nondominated optimal solutions called as Pareto optimal front in a single run. In recent years, Non-dominated Sorting Genetic Algorithm II (NSGA-II) finds increasing applications in solving multi objective problems comprising of conflicting objectives because of low computational requirements, elitism and parameter-less sharing approach. In this work, we propose a methodology which integrates NSGA-II and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) for solving a two bar truss problem. NSGA-II searches for the Pareto set where two bar truss is evaluated in terms of minimizing the weight of the truss and minimizing the total displacement of the joint under the given load. Subsequently, TOPSIS selects the best compromise solution.

An effective hybrid search mode for multi-objective optimization with constraints

“…Moreover, a real instance for two-bar truss design is also used to be tested [16]. The truss must carry on a certain load without elastic failure as shown in Figure 3.…”

Heat dissipation optimization of ventilated brake disc recirculation zone based on NSGA-II algorithm

“…20 In recent years, genetic algorithm has been widely concerned in the structural optimization design of brake discs. 21,22 Wang et al took the convective heat transfer coefficient of the brake disc surface as the optimization objective and then used the Non-dominated Sorting Genetic Algorithms II (NSGA-II) to solve the problem. Finally, they obtained the Pareto front and chose the compromise solution as the optimization result.…”