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.

Applied Science Research Center Applied Science Private University Amman 11931 Jordan

Artificial Intelligence and Sensing Technologies Research Center University of Tabuk Tabuk 71491 Saudi Arabia

Computer Science Department Al Al Bayt University Mafraq 25113 Jordan

Department of Biosciences Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai 602 105 India

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

Department of Machining Assembly and Engineering Metrology Faculty of Mechanical Engineering VSB Technical University of Ostrava 70800 Ostrava Czech Republic

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

Jadara Research Center Jadara University Irbid 21110 Jordan

MEU Research Unit Middle East University Amman 11831 Jordan

University Centre for Research and Development Chandigarh University Mohali 140413 India

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Adaptive predator prey algorithm for many objective optimization