A FAST FUZZY-TUNED MULTI-OBJECTIVE OPTIMIZATION FOR SIZING PROBLEMS
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Abstract:
The most recent approaches of multi-objective optimization constitute application of meta-heuristic algorithms for which, parameter tuning is still a challenge. The present work hybridizes swarm intelligence with fuzzy operators to extend crisp values of the main control parameters into especial fuzzy sets that are constructed based on a number of prescribed facts. Such parameter-less particle swarm optimization is employed as the core of a multi-objective optimization framework with a repository to save Pareto solutions. The proposed method is tested on a variety of benchmark functions and structural sizing examples. Results show that it can provide Pareto front by lower computational time in competition with some other popular multi-objective algorithms.
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Journal title
volume 8 issue 1
pages 53- 75
publication date 2018-01
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