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- Title
Improving convergence of evolutionary multi-objective optimization with local search: a concurrent-hybrid algorithm.
- Authors
Sindhya, Karthik; Deb, Kalyanmoy; Miettinen, Kaisa
- Abstract
A local search method is often introduced in an evolutionary optimization algorithm, to enhance its speed and accuracy of convergence to optimal solutions. In multi-objective optimization problems, the implementation of local search is a non-trivial task, as determining a goal for local search in presence of multiple conflicting objectives becomes a difficult task. In this paper, we borrow a multiple criteria decision making concept of employing a reference point based approach of minimizing an achievement scalarizing function and integrate it as a search operator with a concurrent approach in an evolutionary multi-objective algorithm. Simulation results of the new concurrent-hybrid algorithm on several two to four-objective problems compared to a serial approach, clearly show the importance of local search in aiding a computationally faster and accurate convergence to the Pareto optimal front.
- Subjects
ECONOMIC convergence; MULTIPLE criteria decision making; MULTIDISCIPLINARY design optimization; INDUSTRIAL efficiency; ALGORITHMS; PARETO optimum; MATHEMATICAL programming
- Publication
Natural Computing, 2011, Vol 10, Issue 4, p1407
- ISSN
1567-7818
- Publication type
Article
- DOI
10.1007/s11047-011-9250-4