期刊
NATURAL COMPUTING
卷 10, 期 4, 页码 1407-1430出版社
SPRINGER
DOI: 10.1007/s11047-011-9250-4
关键词
Multicriteria optimization; Multiple criteria decision making; Pareto optimality; Evolutionary algorithms; Hybrid algorithms; Achievement scalarizing functions; NSGA-II
类别
资金
- Academy of Finland [118319]
- Jenny and Antti Wihuri foundation
- Academy of Finland (AKA) [118319, 118319] Funding Source: Academy of Finland (AKA)
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.
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