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A novel many-objective symbiotic organism search algorithm for industrial engineering problems

Authors:
Kanak Kalita at Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology
Kanak Kalita
Pradeep Jangir at Gujarat Technological University
Pradeep Jangir
Ajay Kumar at JECRC University
Ajay Kumar
Pandya Sundaram at Shri K .J. Polytechnic
Pandya Sundaram
  • Shri K .J. Polytechnic
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Abstract and Figures

The focus of multi-objective optimization is to derive a set of optimal solutions in scenarios with multiple and often conflicting objectives. However, the ability of multi-objective evolutionary algorithms in approaching the Pareto front and sustaining diversity within the population tends to diminish as the number of objectives grows. To tackle this challenge, this research introduces a novel Many-Objective Symbiotic Organism Search (MaOSOS) for many-objective optimization. In this method the concept of reference point, niche preservation and information feedback mechanism (IFM) are incorporated. Niche preservation aims to enhance selection pressure while preserving diversity by splitting the objective space. Reference point adaptation strategy effectively accommodates various Pareto front models to improve convergence. The IFM mechanism augments the likelihood of selecting parent solutions that exhibit both strong convergence and diversity. The efficacy of MaOSOS was validated through WFG1-WFG9 benchmark problems (with varied number of objectives ranging from 5 to 7) and five real-world engineering problems. Several metrics like GD, IGD, SP, SD, HV and RT metrics were used to assess the MaOSOS’s efficacy. The extensive experiments establish the superior performance of MaOSOS in managing many-objective optimization tasks compared to MaOGBO, MaOJAYA, MaOTLBO and MaOSCA.
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This content is subject to copyright. Terms and conditions apply.
International Journal on Interactive Design and Manufacturing (IJIDeM)
https://doi.org/10.1007/s12008-024-02143-z
ORIGINAL ARTICLE
A novel many-objective symbiotic organism search algorithm
for industrial engineering problems
Kanak Kalita1,2 ·Pradeep Jangir3,4,5,6 ·Ajay Kumar7·Sundaram B. Pandya8·Laith Abualigah9
Received: 22 April 2024 / Accepted: 5 October 2024
© The Author(s), under exclusive licence to Springer-Verlag France SAS, part of Springer Nature 2024
Abstract
The focus of multi-objective optimization is to derive a set of optimal solutions in scenarios with multiple and often conflicting
objectives. However, the ability of multi-objective evolutionary algorithms in approaching the Pareto front and sustaining
diversity within the population tends to diminish as the number of objectives grows. To tackle this challenge, this research
introduces a novel Many-Objective Symbiotic Organism Search (MaOSOS) for many-objective optimization. In this method
the concept of reference point, niche preservation and information feedback mechanism (IFM) are incorporated. Niche
preservation aims to enhance selection pressure while preserving diversity by splitting the objective space. Reference point
adaptation strategy effectively accommodates various Pareto front models to improve convergence. The IFM mechanism
augments the likelihood of selecting parent solutions that exhibit both strong convergence and diversity. The efficacy of
MaOSOS was validated through WFG1-WFG9 benchmark problems (with varied number of objectives ranging from 5 to 7)
and five real-world engineering problems. Several metrics like GD, IGD, SP, SD, HV and RT metrics were used to assess the
MaOSOS’s efficacy. The extensive experiments establish the superior performance of MaOSOS in managing many-objective
optimization tasks compared to MaOGBO, MaOJAYA, MaOTLBO and MaOSCA.
Keywords Symbiotic organism search ·Many-objective ·Multi-objective ·Convergence ·Real world
BKanak Kalita
drkanakkalita@veltech.edu.in; kanakkalita02@gmail.com
Pradeep Jangir
pkjmtech@gmail.com
Ajay Kumar
dr.ajaykumarphd@gmail.com
Sundaram B. Pandya
sundarampandya@gmail.com
Laith Abualigah
aligah.2020@gmail.com
1Department of Mechanical Engineering, Vel Tech Rangarajan
Dr. Sagunthala R&D Institute of Science and Technology,
Avadi 600 062, India
2Jadara University Research Center, Jadara University, Irbid,
Jordan
3Department of Biosciences, Saveetha School of Engineering,
Saveetha Institute of Medical and Technical Sciences,
Chennai 602 105, India
4Applied Science Research Center, Applied Science Private
University, Amman 11931, Jordan
1 Introduction
In the field of optimization, numerous real-world challenges
encompass multiple objectives that often conflict with each
other. Such challenges are commonly identified as mul-
tiobjective optimization problems (MOPs). When MOPs
encompass four or more conflicting objectives, they are clas-
sified as many-objective optimization problems (MaOPs).
The field of many-objective optimization has garnered signif-
icant attention, owing to its extensive practical applications,
5Department of CSE, Graphic Era Hill University, Graphic Era
Deemed To Be University, Dehradun 248002, Uttarakhand,
India
6University Centre for Research and Development, Chandigarh
University, Gharuan 140413, Mohali, India
7Department of Mechanical Engineering, JECRC University,
Jaipur 303905, India
8Department of Electrical Engineering, Shri K.J. Polytechnic,
Bharuch 392 001, India
9Computer Science Department, Al al-Bayt University,
Mafraq 25113, Jordan
123
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