Multi-objective fitness-dependent optimizer algorithm

Neural Computing and Applications - Tập 35 - Trang 11969-11987 - 2023
Jaza M. Abdullah1, Tarik A. Rashid2, Bestan B. Maaroof1,3, Seyedali Mirjalili4,5
1Information Technology, College of Commerce, University of Sulaimani, Sulaymaniyah, Iraq
2Computer Science and Engineering Department, University of Kurdistan Hewler, Erbil, Iraq
3School of Information Technology, Fanshawe College, London, Canada
4Centre for Artificial Intelligence Research and Optimisation, Torrens University, Adelaide, Australia
5Yonsei Frontier Lab, Yonsei University, Seoul, Korea

Tóm tắt

This paper proposes the multi-objective variant of the recently-introduced fitness dependent optimizer (FDO). The algorithm is called a multi-objective fitness dependent optimizer (MOFDO) and is equipped with all five types of knowledge (situational, normative, topographical, domain, and historical knowledge) as in FDO. MOFDO is tested on two standard benchmarks for the performance-proof purpose: classical ZDT test functions, which is a widespread test suite that takes its name from its authors Zitzler, Deb, and Thiele, and on IEEE Congress of Evolutionary Computation benchmark (CEC-2019) multi-modal multi-objective functions. MOFDO results are compared to the latest variant of multi-objective particle swarm optimization, non-dominated sorting genetic algorithm third improvement (NSGA-III), and multi-objective dragonfly algorithm. The comparative study shows the superiority of MOFDO in most cases and comparative results in other cases. Moreover, MOFDO is used for optimizing real-world engineering problems (e.g., welded beam design problems). It is observed that the proposed algorithm successfully provides a wide variety of well-distributed feasible solutions, which enable the decision-makers to have more applicable-comfort choices to consider.

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Neural Computing and Applications

Tập 35

11969-11987

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