Multi‐objective sunflower optimization: A new hypercubic meta‐heuristic for constrained engineering problems

In order to solve challenging engineering problems, the state‐of‐the‐art in multi‐objective optimization shows a trend toward using meta‐heuristics and a posteriori decision‐making methods. This encourages the search for algorithms better able to find Pareto fronts with more convergence, coverage, a...

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Bibliographic Details
Published in:Expert systems 2023-09, Vol.40 (8), p.n/a
Main Authors: Pereira, João Luiz Junho, Gomes, Guilherme Ferreira
Format: Article
Language:English
Subjects:
Algorithms
Convergence
Evolutionary algorithms
Heuristic
meta‐heuristics
multi‐objective optimization
multi‐objective sunflower optimization
Optimization
Sunflowers
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Summary:In order to solve challenging engineering problems, the state‐of‐the‐art in multi‐objective optimization shows a trend toward using meta‐heuristics and a posteriori decision‐making methods. This encourages the search for algorithms better able to find Pareto fronts with more convergence, coverage, and lower computational cost. This work shows the creation and validation of the Multi‐objective Sunflower Optimization (MOSFO), a hypercubic and constrained multi‐objective meta‐heuristic inspired by the phototropic life cycle of sunflowers around the sun. Having a much simpler programming model than most evolutionary algorithms, MOSFO was validated using the most difficult set of test functions in the literature (CEC 2009) and applied to ten constrained multi‐objective optimization problems (CEC 2021). The proposed algorithm was compared with ten other powerful algorithms: MOGWO, MOPSO, NSGA‐II, MOEA/D, NSGA‐III, CCMO, ARMOEA, ToP, TiGE 2, and AnD. Inverted General Distance, Spacing, Maximum Spread, and Hyper volume were used as comparison metrics to evaluate the convergence and coverage capabilities of the algorithms. MOSFO had the best average IGD value in 8 of the 10 test functions when compared with the other algorithms. In terms of MS, MOSFO had the highest average value of MS for 7 of the test functions. In summary, MOSFO showed substantial convergence and coverage capabilities and proved to be very competitive among the algorithms used, which were carefully selected to be popular and recent. The method is even more promising for problems with three or more objectives.
ISSN:0266-4720
1468-0394
DOI:10.1111/exsy.13331