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Unveiling the Many-Objective Dragonfly Algorithm's (MaODA) efficacy in complex optimization

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Abstract

In the field of multi-objective optimization algorithms (MOAs), a primary challenge is identifying an optimal solution that effectively converges to the true Pareto Front while maintaining a high level of diversity, especially in the initial phases of evolution. To tackle this challenge, this study introduces a novel algorithm named Many-Objective Dragonfly Algorithm (MaODA). This method incorporates reference point, niche preserve and information feedback mechanism for maintaining better balance between convergence and diversity. Through this study, the MaODA effectiveness is established by using the WFG1-WFG9 test suite for 4, 6 and 8 objectives and five real-world (RWMaOP1- RWMaOP5) design problems. The findings demonstrate that MaODA outperforms its peer many-objective algorithms’ like MaOTLBO, MaOJAYA, MaOPSO and NSGA-III in most test scenarios in terms of GD, IGD, SP, SD, HV and RT metrices. It is shown that MaODA achieves better convergence, as well as enhanced diversity and spread, compared to peers.

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Authors and Affiliations

  1. Department of Mechanical Engineering, Vel Tech Rangarajan Dr, Sagunthala R&D Institute of Science and Technology, Avadi, 600 062, India

    Kanak Kalita

  2. University Centre for Research & Development, Chandigarh University, Mohali, 140413, India

    Kanak Kalita

  3. Department of Biosciences, Saveetha School of Engineering. Saveetha Institute of Medical and Technical Sciences, Chennai, 602105, India

    Pradeep Jangir

  4. Department of Electrical Engineering, Shri K.J. Polytechnic, Bharuch, 392 001, India

    Sundaram B. Pandya

  5. Department of Mechanical Engineering, Sri Sairam Institute of Technology, Chennai, 600044, India

    G. Shanmugasundar

  6. Computer Science Department, Al Al-Bayt University, Mafraq, 25113, Jordan

    Laith Abualigah

  7. Department of Electrical and Computer Engineering, Lebanese American University, Byblos, 13-5053, Lebanon

    Laith Abualigah

  8. Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Amman, 19328, Jordan

    Laith Abualigah

  9. MEU Research Unit, Middle East University, Amman, 11831, Jordan

    Laith Abualigah

  10. Applied Science Research Center, Applied Science Private University, Amman, 11931, Jordan

    Laith Abualigah

  11. School of Computer Sciences, Universiti Sains Malaysia, 11800, Pulau Pinang, Malaysia

    Laith Abualigah

  12. School of Engineering and Technology, Sunway University Malaysia, 27500, Petaling Jaya, Malaysia

    Laith Abualigah

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  1. Kanak Kalita
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  2. Pradeep Jangir
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  3. Sundaram B. Pandya
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  4. G. Shanmugasundar
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  5. Laith Abualigah
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Contributions

Conceptualization, Kanak Kalita, Pradeep Jangir, Sundaram B. Pandya;

Formal analysis, Kanak Kalita, Pradeep Jangir, Sundaram B. Pandya;

Investigation, Kanak Kalita, Pradeep Jangir, Sundaram B. Pandya;

Methodology, Kanak Kalita, Pradeep Jangir, Sundaram B. Pandya, G. Shanmugasundar, Laith Abualigah;

Software, Kanak Kalita, Pradeep Jangir, Sundaram B. Pandya, G. Shanmugasundar, Laith Abualigah;

Writing – Kanak Kalita, Pradeep Jangir, Sundaram B. Pandya, G. Shanmugasundar, Laith Abualigah;

Writing – review & editing, Kanak Kalita, Pradeep Jangir, Sundaram B. Pandya, G. Shanmugasundar, Laith Abualigah.

All authors have read and agreed to the published version of the manuscript.

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Correspondence to Kanak Kalita.

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Kalita, K., Jangir, P., Pandya, S.B. et al. Unveiling the Many-Objective Dragonfly Algorithm's (MaODA) efficacy in complex optimization. Evol. Intel. 17, 3505–3533 (2024). https://doi.org/10.1007/s12065-024-00942-7

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