Many-objective ant lion optimizer (MaOALO): A new many-objective optimizer with its engineering applications
Kanak Kalita,
Sundaram B. Pandya,
Robert Čep,
Pradeep Jangir,
Laith Abualigah
Affiliations
Kanak Kalita
Department of Mechanical Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Avadi, 600 062, India; University Centre for Research & Development, Chandigarh University, Mohali, 140413, India; Corresponding author. Department of Mechanical Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Avadi 600 062, India.
Sundaram B. Pandya
Department of Electrical Engineering, Shri K.J. Polytechnic, Bharuch, 392 001, India
Robert Čep
Department of Machining, Assembly and Engineering Metrology, Faculty of Mechanical Engineering, VSB-Technical University of Ostrava, 70800, Ostrava, Czech Republic; Corresponding author.
Pradeep Jangir
Department of Biosciences, Saveetha School of Engineering. Saveetha Institute of Medical and Technical Sciences, Chennai, 602 105, India
Laith Abualigah
Computer Science Department, Al Al-Bayt University, Mafraq, 25113, Jordan; MEU Research Unit, Middle East University, Amman, 11831, Jordan; Applied Science Research Center, Applied Science Private University, Amman, 11931, Jordan; Jadara Research Center, Jadara University, Irbid, 21110, Jordan; Artificial Intelligence and Sensing Technologies (AIST) Research Center, University of Tabuk, Tabuk, 71491, Saudi Arabia
Many-objective optimization (MaO) is an important aspect of engineering scenarios. In many-objective optimization algorithms (MaOAs), a key challenge is to strike a balance between diversity and convergence. MaOAs employs various tactics to either enhance selection pressure for better convergence and/or implements additional measures for sustaining diversity. With increase in number of objectives, the process becomes more complex, mainly due to challenges in achieving convergence during population selection. This paper introduces a novel Many-Objective Ant Lion Optimizer (MaOALO), featuring the widely-popular ant lion optimizer algorithm. This method utilizes reference point, niche preserve and information feedback mechanism (IFM), to enhance the convergence and diversity of the population. Extensive experimental tests on five real-world (RWMaOP1- RWMaOP5) optimization problems and standard problem classes, including MaF1-MaF15 (for 5, 9 and 15 objectives), DTLZ1-DTLZ7 (for 8 objectives) has been carried out. It is shown that MaOALO is superior compared to ARMOEA, NSGA-III, MaOTLBO, RVEA, MaOABC-TA, DSAE, RL-RVEA and MaOEA-IH algorithms in terms of GD, IGD, SP, SD, HV and RT metrics. The MaOALO source code is available at: https://github.com/kanak02/MaOALO.
