Constraints handling in Nash/Adjoint optimization methods for multi-objective aerodynamic design

2014 ◽  
Vol 271 ◽  
pp. 130-143 ◽  
Author(s):  
Zhili Tang ◽  
Jacques Périaux ◽  
Jun Dong
Keyword(s):  
Optimization Methods ◽  
Aerodynamic Design ◽  
Multi Objective ◽  
Constraints Handling

Review: Multi-objective optimization methods and application in energy saving

Energy ◽  
2017 ◽  
Vol 125 ◽  
pp. 681-704 ◽  
Author(s):  
Yunfei Cui ◽  
Zhiqiang Geng ◽  
Qunxiong Zhu ◽  
Yongming Han
Keyword(s):  
Energy Saving ◽  
Optimization Methods ◽  
Multi Objective Optimization ◽  
Multi Objective

OPTIMAL PREDICTION AND DESIGN OF SURFACE ROUGHNESS FOR CNC TURNING OF AL7075-T6 BY USING THE TAGUCHI HYBRID QPSO ALGORITHM

2016 ◽  
Vol 40 (5) ◽  
pp. 883-895 ◽  
Author(s):  
Wen-Jong Chen ◽  
Chuan-Kuei Huang ◽  
Qi-Zheng Yang ◽  
Yin-Liang Yang
Keyword(s):  
Surface Roughness ◽  
Particle Swarm Optimization ◽  
Predictive Models ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Cutting Parameters ◽  
Optimization Methods ◽  
Swarm Optimization ◽  
Cnc Turning ◽  
Multi Objective

This paper combines the Taguchi-based response surface methodology (RSM) with a multi-objective hybrid quantum-behaved particle swarm optimization (MOHQPSO) to predict the optimal surface roughness of Al7075-T6 workpiece through a CNC turning machining. First, the Taguchi orthogonal array L27 (36) was applied to determine the crucial cutting parameters: feed rate, tool relief angle, and cutting depth. Subsequently, the RSM was used to construct the predictive models of surface roughness (Ra, Rmax, and Rz). Finally, the MOHQPSO with mutation was used to determine the optimal roughness and cutting conditions. The results show that, compared with the non-optimization, Taguchi and classical multi-objective particle swarm optimization methods (MOPSO), the roughness Ra using MOHQPSO along the Pareto optimal solution are improved by 68.24, 59.31 and 33.80%, respectively. This reveals that the predictive models established can improve the machining quality in CNC turning of Al7075-T6.

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

2014 ◽  
Vol 984-985 ◽  
pp. 419-424
Author(s):  
P. Sabarinath ◽  
M.R. Thansekhar ◽  
R. Saravanan
Keyword(s):  
Design Optimization ◽  
Optimization Problems ◽  
Optimization Methods ◽  
Optimal Solutions ◽  
Objective Functions ◽  
Nsga Ii ◽  
Multi Objective ◽  
Total Displacement ◽  
Design Variables ◽  
Conflicting Objectives

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.

scholarly journals What Can We Learn from Multi-Objective Meta-Optimization of Evolutionary Algorithms in Continuous Domains?

Mathematics ◽  
2019 ◽  
Vol 7 (3) ◽  
pp. 232 ◽  
Author(s):  
Roberto Ugolotti ◽  
Laura Sani ◽  
Stefano Cagnoni
Keyword(s):  
Evolutionary Algorithms ◽  
Fitness Function ◽  
Target Function ◽  
Optimization Methods ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm ◽  
Continuous Domains ◽  
Level Method ◽  
Meta Optimization ◽  
Function Time

Properly configuring Evolutionary Algorithms (EAs) is a challenging task made difficult by many different details that affect EAs’ performance, such as the properties of the fitness function, time and computational constraints, and many others. EAs’ meta-optimization methods, in which a metaheuristic is used to tune the parameters of another (lower-level) metaheuristic which optimizes a given target function, most often rely on the optimization of a single property of the lower-level method. In this paper, we show that by using a multi-objective genetic algorithm to tune an EA, it is possible not only to find good parameter sets considering more objectives at the same time but also to derive generalizable results which can provide guidelines for designing EA-based applications. In particular, we present a general framework for multi-objective meta-optimization, to show that “going multi-objective” allows one to generate configurations that, besides optimally fitting an EA to a given problem, also perform well on previously unseen ones.

GA-Based Multi-Objective Fuzzy Matter-Element Optimization

2002 ◽  
Author(s):  
Yanwei Zhao ◽  
Ertian Hua ◽  
Guoxian Zhang ◽  
Fangshun Jin
Keyword(s):  
Genetic Algorithms ◽  
Optimization Problem ◽  
Fuzzy Optimization ◽  
Optimization Methods ◽  
Optimization Criterion ◽  
Multi Objective ◽  
Simple Genetic Algorithms ◽  
Solving Strategy ◽  
Crossover And Mutation ◽  
Better Than

The solving strategy of GA-Based Multi-objective Fuzzy Matter-Element optimization is put forward in this paper to the kind of characters of product optimization such as multi-objective, fuzzy nature, indeterminacy, etc. Firstly, the model of multi-objective fuzzy matter-element optimization is created in this paper, and then it defines the matter-element weightily and changes solving multi-objective optimization into solving dependent function K(x) of the single objective optimization according to the optimization criterion. In addition, modified adaptive macro genetic algorithms (MAMGA) are adopted to solve the optimization problem. It emphatically modifies crossover and mutation operator. By the comparing MAMGA with adaptive macro genetic algorithms (AMGA), not only the optimization is a little better than the latter, but also it reaches the extent to which the effective iteration generation is 62.2% of simple genetic algorithms (SGA). Lastly, three optimization methods, namely fuzzy matter-element optimization, linearity weighted method and fuzzy optimization, are also compared. It certifies that this method is feasible and valid.

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