This preprint is under consideration at Scientific Reports. A preprint is a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
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Research Article
Peng Gao
School of Electrical Engineering, Northeast Electric Power University
Corresponding Author
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Recent studies in many-objective optimization problems (MaOPs) have tended to employ some promising evolutionary algorithms with excellent convergence accuracy and speed. However, they will encounter difficulties in their scalability upon MaOPs because the most evolutionary algorithms are proposed for single-objective optimization, which will affect the selection of leaders and individual-updating mechanisms, thereby deteriorating the effectiveness of the algorithm. This paper proposes a many-objective optimization algorithm based on the improved Farmland Fertility algorithm (MOIFF). A novel bio-inspired meta heuristic method proposed in 2018, called Farmland Fertility algorithm (FF), is employed to serve as the optimization strategy of MOIFF. In order to handle MaOPs effectively, FF has been tailored from the following aspects: an individual fitness assessment approach based on cumulative ranking value has been proposed to distinguish the quality of each individual, updated the global memory and local memory of each individual based on individual cumulative ranking value, and a hybrid subspace search and full space search method has been designed to update individuals in the stages of soil optimization and soil fusion. In addition, adaptive environmental selection has been proposed. Finally, MOIFF is compared with four state-of-the art many-objective evolutionary algorithms on many test problems with various characteristics, including the DTLZ and WFG test suites. Experimental results demonstrate that the proposed algorithm has competitive convergence and diversity on many-objective optimization problems.
Keywords
evolutionary algorithms, diversity, algorithm, convergence, soil fusion
This preprint is available for download as a PDF.
No competing interests reported.
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This preprint is under consideration at Scientific Reports. A preprint is a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research Article
Peng Gao
School of Electrical Engineering, Northeast Electric Power University
Corresponding Author
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Under Review
Version 1
Posted 16 Nov, 2021
No community comments so far
Reviewers agreed
On 25 Nov, 2021
Reviewers invited
Invitations sent on 25 Nov, 2021
Editor assigned
On 25 Nov, 2021
Editor invited
On 14 Nov, 2021
Submission checks complete
On 14 Nov, 2021
First submitted
On 04 Nov, 2021
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Recent studies in many-objective optimization problems (MaOPs) have tended to employ some promising evolutionary algorithms with excellent convergence accuracy and speed. However, they will encounter difficulties in their scalability upon MaOPs because the most evolutionary algorithms are proposed for single-objective optimization, which will affect the selection of leaders and individual-updating mechanisms, thereby deteriorating the effectiveness of the algorithm. This paper proposes a many-objective optimization algorithm based on the improved Farmland Fertility algorithm (MOIFF). A novel bio-inspired meta heuristic method proposed in 2018, called Farmland Fertility algorithm (FF), is employed to serve as the optimization strategy of MOIFF. In order to handle MaOPs effectively, FF has been tailored from the following aspects: an individual fitness assessment approach based on cumulative ranking value has been proposed to distinguish the quality of each individual, updated the global memory and local memory of each individual based on individual cumulative ranking value, and a hybrid subspace search and full space search method has been designed to update individuals in the stages of soil optimization and soil fusion. In addition, adaptive environmental selection has been proposed. Finally, MOIFF is compared with four state-of-the art many-objective evolutionary algorithms on many test problems with various characteristics, including the DTLZ and WFG test suites. Experimental results demonstrate that the proposed algorithm has competitive convergence and diversity on many-objective optimization problems.
This preprint is available for download as a PDF.
No competing interests reported.
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