Unifying The Evaluation Criteria Of Many Objectives Optimization Using Fuzzy Delphi Method

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Rawia Tahrir Mohammed
Razali Yaakob
Nurfadhlina Mohd Sharef
Rusli Abdullah


Many objective optimizations (MaOO) algorithms that intends to solve problems with many objectives (MaOP) (i.e., the problem with more than three objectives) are widely used in various areas such as industrial manufacturing, transportation, sustainability, and even in the medical sector.  Various approaches of MaOO algorithms are available and employed to handle different MaOP cases. In contrast, the performance of the MaOO algorithms assesses based on the balance between the convergence and diversity of the non-dominated solutions measured using different evaluation criteria of the quality performance indicators. Although many evaluation criteria are available, yet most of the evaluation and benchmarking of the MaOO with state-of-art algorithms perform using one or two performance indicators without clear evidence or justification of the efficiency of these indicators over others.  Thus, unify a set of most suitable evaluation criteria of the MaOO is needed. This study proposed a distinct unifying model for the MaOO evaluation criteria using the fuzzy Delphi method. The study followed a systematic procedure to analyze 49 evaluation criteria, sub-criteria, and its performance indicators, a penal of 23 domain experts, participated in this study. Lastly, the most suitable criteria outcomes are formulated in the unifying model and evaluate by experts to verify the appropriateness and suitability of the model in assessing the MaOO algorithms fairly and effectively.


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Mohammed RT, Yaakob R, Sharef NM, Abdullah R. Unifying The Evaluation Criteria Of Many Objectives Optimization Using Fuzzy Delphi Method. Baghdad Sci.J [Internet]. 2021Dec.20 [cited 2022Jan.20];18(4(Suppl.):1423. Available from: https://bsj.uobaghdad.edu.iq/index.php/BSJ/article/view/6649


Y. Zhang, D.-w. Gong, J.-y. Sun, and B.-y. Qu, "A decomposition-based archiving approach for multi-objective evolutionary optimization," Information Sciences, vol. 430–431, pp. 397-413, 2018.

X. Yu and Y. Lu, "Evaluation of Many-Objective Evolutionary Algorithms by Hesitant Fuzzy Linguistic Term Set and Majority Operator," Int. J. Fuzzy Syst. , vol. 20, no. 6, pp. 2043-2056, 2018.

J. Wang, J. Liu, H. Wang, and C. Mei, "Approaches to Multi-Objective Optimization and Assessment of Green Infrastructure and Their Multi-Functional Effectiveness: A Review," Water, vol. 12, no. 10, p. 2714, 2020.

C. A. C. Coello, S. G. Brambila, J. F. Gamboa, M. G. C. Tapia, and R. H. Gómez, "Evolutionary multiobjective optimization: open research areas and some challenges lying ahead," Complex & Intelligent Systems, vol. 6, no. 2, pp. 221-236, 2020.

Z. He and G. G. Yen, "Visualization and performance metric in many-objective optimization," IEEE Transactions on Evolutionary Computation, vol. 20, no. 3, pp. 386-402, 2016.

N. Riquelme, C. Von Lücken, and B. Baran, "Performance metrics in multi-objective optimization," in Computing Conference (CLEI), 2015 Latin American, 2015, pp. 1-11: IEEE.

T. Y. Pham, H. M. Ma, and G. T. Yeo, "Application of Fuzzy Delphi TOPSIS to locate logistics centers in Vietnam: The Logisticians’ perspective," The Asian Journal of Shipping and Logistics, vol. 33, no. 4, pp. 211-219, 2017.

E. Rahimianzarif and M. Moradi, "Designing integrated management criteria of creative ideation based on fuzzy delphi analytical hierarchy process," International Journal of Fuzzy Systems, vol. 20, no. 3, pp. 877-900, 2018.

S. K. Manakandan, I. Rosnah, R. J. Mohd, and R. Priya, "Pesticide applicators questionnaire content validation: A fuzzy delphi method," Med J Malaysia, vol. 72, no. 4, pp. 228-235, 2017.

A. Morovati Sharifabadi, A. Naser Sadrabadi, F. Dehghani Bezgabadi, and S. Peirow, "Presenting a model for evaluation and selecting suppliers using interpretive structure modeling (ISM)," International Journal of Industrial Engineering & Production Research, vol. 27, no. 2, pp. 109-120, 2016.

I. Sultana, I. Ahmed, and A. Azeem, "An integrated approach for multiple criteria supplier selection combining Fuzzy Delphi, Fuzzy AHP & Fuzzy TOPSIS," Journal of Intelligent & Fuzzy Systems, vol. 29, no. 4, pp. 1273-1287, 2015.

N. Kamarulzaman, N. Jomhari, N. M. Raus, and M. Z. M. Yusoff, "Applying the fuzzy delphi method to analyze the user requirement for user centred design process in order to create learning applications," Indian Journal of Science and Technology, vol. 8, no. 32, pp. 1-7, 2015.

K. H. Abdulkareem et al., "A new standardisation and selection framework for real-time image dehazing algorithms from multi-foggy scenes based on fuzzy Delphi and hybrid multi-criteria decision analysis methods," Neural Computing and Applications, vol. 33, pp. 1029-1054, 2021.

K. A. Dawood, K. Y. Sharif, A. A. Ghani, H. Zulzalil, A. Zaidan, and B. Zaidan, "Towards a unified criteria model for usability evaluation in the context of open source software based on a fuzzy Delphi method," Information and Software Technology, vol. 130, p. 106453, 2021.

L.-C. Wu, K.-L. Chang, and S.-K. Liao, "A hybrid MCDM model to select optimal hosts of variety shows in the social media era," Symmetry, vol. 12, no. 1, p. 125, 2020.

R. Mohammed et al., "Determining Importance of Many-Objective Optimisation Competitive Algorithms Evaluation Criteria Based on a Novel Fuzzy-Weighted Zero-Inconsistency Method," International Journal of Information Technology & Decision Making, pp. 1-47, 2021.

M. Adler and E. Ziglio, Gazing into the oracle: The Delphi method and its application to social policy and public health. Jessica Kingsley Publishers, 1996.

H. Jones and B. C. Twiss, "Forecasting technology for planning decisions," 1978.

C. Powell, "The Delphi technique: myths and realities," Journal of advanced nursing, vol. 41, no. 4, pp. 376-382, 2003.

C. Duffield, "The Delphi technique: a comparison of results obtained using two expert panels," International journal of nursing studies, vol. 30, no. 3, pp. 227-237, 1993.

J. B. B. Abdullah and S. I. B. M. Yusof, "A Fuzzy Delphi Method-Developing High-Performance Leadership Standard For Malaysian School Leaders," Journal of Education and Social Sciences, 2018.

C.-H. Cheng and Y. Lin, "Evaluating the best main battle tank using fuzzy decision theory with linguistic criteria evaluation," European journal of operational research, vol. 142, no. 1, pp. 174-186, 2002.

H.-C. Chu and G.-J. Hwang, "A Delphi-based approach to developing expert systems with the cooperation of multiple experts," Expert systems with applications, vol. 34, no. 4, pp. 2826-2840, 2008.

S. Bodjanova, "Median alpha-levels of a fuzzy number," Fuzzy Sets and Systems, vol. 157, no. 7, pp. 879-891, 2006.

J. Shi, X. Mo, and Z. Sun, "Content validity index in scale development," Zhong nan da xue xue bao. Yi xue ban= Journal of Central South University. Medical sciences, vol. 37, no. 2, pp. 152-155, 2012.

I. B. Rodrigues, J. D. Adachi, K. A. Beattie, and J. C. MacDermid, "Development and validation of a new tool to measure the facilitators, barriers and preferences to exercise in people with osteoporosis," BMC Musculoskeletal disorders, vol. 18, no. 1, pp. 1-9, 2017.

H. Wang, Y. Jin, and X. Yao, "Diversity assessment in many-objective optimization," IEEE transactions on cybernetics, vol. 47, no. 6, pp. 1510-1522, 2017.