Sindhya, Karthik ; Deb, Kalyanmoy ; Miettinen, Kaisa (2011) Improving convergence of evolutionary multi-objective optimization with local search: a concurrent-hybrid algorithm Natural Computing, 10 (4). pp. 1407-1430. ISSN 1567-7818
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Official URL: http://www.springerlink.com/content/5120n36n4543x0...
Related URL: http://dx.doi.org/10.1007/s11047-011-9250-4
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.
Item Type: | Article |
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Source: | Copyright of this article belongs to Springer. |
Keywords: | Multicriteria Optimization; Multiple Criteria Decision Making; Pareto Optimality; Evolutionary Algorithms; Hybrid Algorithms; Achievement Scalarizing Functions; NSGA-II |
ID Code: | 81006 |
Deposited On: | 03 Feb 2012 11:56 |
Last Modified: | 03 Feb 2012 11:56 |
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