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      Hybrid multi-objective shape design optimization using Taguchi’s method and genetic algorithm

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          Approximating the nondominated front using the Pareto Archived Evolution Strategy.

          We introduce a simple evolution scheme for multiobjective optimization problems, called the Pareto Archived Evolution Strategy (PAES). We argue that PAES may represent the simplest possible nontrivial algorithm capable of generating diverse solutions in the Pareto optimal set. The algorithm, in its simplest form, is a (1 + 1) evolution strategy employing local search but using a reference archive of previously found solutions in order to identify the approximate dominance ranking of the current and candidate solution vectors. (1 + 1)-PAES is intended to be a baseline approach against which more involved methods may be compared. It may also serve well in some real-world applications when local search seems superior to or competitive with population-based methods. We introduce (1 + lambda) and (mu + lambda) variants of PAES as extensions to the basic algorithm. Six variants of PAES are compared to variants of the Niched Pareto Genetic Algorithm and the Nondominated Sorting Genetic Algorithm over a diverse suite of six test functions. Results are analyzed and presented using techniques that reduce the attainment surfaces generated from several optimization runs into a set of univariate distributions. This allows standard statistical analysis to be carried out for comparative purposes. Our results provide strong evidence that PAES performs consistently well on a range of multiobjective optimization tasks.
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            Balance between genetic search and local search in memetic algorithms for multiobjective permutation flowshop scheduling

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              Multi-objective Genetic Algorithms: Problem Difficulties and Construction of Test Problems

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                Author and article information

                Journal
                Structural and Multidisciplinary Optimization
                Struct Multidisc Optim
                Springer Nature
                1615-147X
                1615-1488
                August 21 2007
                December 12 2006
                : 34
                : 4
                : 317-332
                Article
                10.1007/s00158-006-0079-x
                24cc234d-8c1c-4c8d-86d3-d7c526734769
                © 2007
                History
                Product
                Self URI (article page): http://link.springer.com/10.1007/s00158-006-0079-x

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