Adaptive heuristic search algorithm for discrete variables based multi-objective optimization

Authors:
Long Tang;Hu Wang;Guangyao Li;Fengxiang Xu
Affiliations:
State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, Hunan University, Changsha, China 410082;State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, Hunan University, Changsha, China 410082;State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, Hunan University, Changsha, China 410082;State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, Hunan University, Changsha, China 410082
Venue:
Structural and Multidisciplinary Optimization
Year:
2013

Citing 12
Cited 0

Stochastic discrete optimization

SIAM Journal on Control and Optimization
A method for discrete stochastic optimization

Management Science
A Simulated Annealing Algorithm with Constant Temperature for Discrete Stochastic Optimization

Management Science
Stochastic Comparison Algorithm for Discrete Optimization with Estimation

SIAM Journal on Optimization
Controlled Elitist Non-dominated Sorting Genetic Algorithms for Better Convergence

EMO '01 Proceedings of the First International Conference on Evolutionary Multi-Criterion Optimization
A combined procedure for optimization via simulation

ACM Transactions on Modeling and Computer Simulation (TOMACS)
A Pattern Search Filter Method for Nonlinear Programming without Derivatives

SIAM Journal on Optimization
Discrete Optimization via Simulation Using COMPASS

Operations Research
Muiltiobjective optimization using nondominated sorting in genetic algorithms

Evolutionary Computation
Multi-objective optimization for bus body with strength and rollover safety constraints based on surrogate models

Structural and Multidisciplinary Optimization
Multi-objective shape optimization of transonic airfoil sections using swarm intelligence and surrogate models

Structural and Multidisciplinary Optimization
A review of multiobjective test problems and a scalable test problem toolkit

IEEE Transactions on Evolutionary Computation

Quantified Score

Hi-index	0.00

Visualization

Abstract

Although metamodel technique has been successfully used to enhance the efficiency of the multi-objective optimization (MOO) with black-box objective functions, the metamodel could become less accurate or even unavailable when the design variables are discrete. In order to overcome the bottleneck, this work proposes a novel random search algorithm for discrete variables based multi-objective optimization with black-box functions, named as k-mean cluster based heuristic sampling with Utopia-Pareto directing adaptive strategy (KCHS-UPDA). This method constructs a few adaptive sampling sets in the solution space and draws samples according to a heuristic probability model. Several benchmark problems are supplied to test the performance of KCHS-UPDA including closeness, diversity, efficiency and robustness. It is verified that KCHS-UPDA can generally converge to the Pareto frontier with a small quantity of number of function evaluations. Finally, a vehicle frontal member crashworthiness optimization is successfully solved by KCHS-UPDA.