Reliability-Based Multi-objective Optimization Using Evolutionary Algorithms Export

@incollection{citeulike:4313437, abstract = {Uncertainties in design variables and problem parameters are inevitable and must be considered in an optimization task including multi-objective optimization, if reliable optimal solutions are to be found. Sampling techniques become computationally expensive if a large reliability is desired. In this paper, first we present a brief review of statistical reliability-based optimization procedures. Thereafter, for the first time, we extend and apply multi-objective evolutionary algorithms for solving two different reliability-based optimization problems for which evolutionary approaches have a clear niche in finding a set of reliable, instead of optimal, solutions. The use of an additional objective of maximizing the reliability index in a multi-objective evolutionary optimization procedure allows a number of trade-off solutions to be found, thereby allowing the designers to find solutions corresponding to different reliability requirements. Next, the concept of single-objective reliability-based optimization is extended to multi-objective optimization of finding a reliable frontier, instead of an optimal frontier. These optimization tasks are illustrated by solving test problems and a well-studied engineering design problem. The results should encourage the use of evolutionary optimization methods to more such reliability-based optimization problems.}, author = {Deb, Kalyanmoy and Padmanabhan, Dhanesh and Gupta, Sulabh and Mall, Abhishek}, citeulike-article-id = {4313437}, citeulike-linkout-0 = {http://dx.doi.org/10.1007/978-3-540-70928-2_9}, citeulike-linkout-1 = {http://www.springerlink.com/content/6976h54044553240}, doi = {10.1007/978-3-540-70928-2_9}, journal = {Evolutionary Multi-Criterion Optimization}, keywords = {engineering, evolutionary-computation, metaheuristic, multi-objective, reliability, statistic}, pages = {66--80}, posted-at = {2009-04-15 14:50:19}, priority = {3}, title = {Reliability-Based Multi-objective Optimization Using Evolutionary Algorithms}, url = {http://dx.doi.org/10.1007/978-3-540-70928-2_9}, year = {2007} }

TY - CHAP ID - citeulike:4313437 L3 - citeulike-article-id:4313437 N2 - Uncertainties in design variables and problem parameters are inevitable and must be considered in an optimization task including multi-objective optimization, if reliable optimal solutions are to be found. Sampling techniques become computationally expensive if a large reliability is desired. In this paper, first we present a brief review of statistical reliability-based optimization procedures. Thereafter, for the first time, we extend and apply multi-objective evolutionary algorithms for solving two different reliability-based optimization problems for which evolutionary approaches have a clear niche in finding a set of reliable, instead of optimal, solutions. The use of an additional objective of maximizing the reliability index in a multi-objective evolutionary optimization procedure allows a number of trade-off solutions to be found, thereby allowing the designers to find solutions corresponding to different reliability requirements. Next, the concept of single-objective reliability-based optimization is extended to multi-objective optimization of finding a reliable frontier, instead of an optimal frontier. These optimization tasks are illustrated by solving test problems and a well-studied engineering design problem. The results should encourage the use of evolutionary optimization methods to more such reliability-based optimization problems. JF - Evolutionary Multi-Criterion Optimization EP - 80 TI - Reliability-Based Multi-objective Optimization Using Evolutionary Algorithms SP - 66 KW - engineering KW - evolutionary-computation KW - metaheuristic KW - multi-objective KW - reliability KW - statistic AU - Deb, Kalyanmoy AU - Padmanabhan, Dhanesh AU - Gupta, Sulabh AU - Mall, Abhishek PY - 2007/// UR - http://dx.doi.org/10.1007/978-3-540-70928-2_9 ER -