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Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
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Many-objective ant lion optimizer (MaOALO): A new many-objective optimizer with its engineering applications
Kanak Kalita1,2, Sundaram B Pandya3, Robert Čep4
1Department of Mechanical Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Avadi, 600 062, India.
Heliyon
|July 18, 2024
Summary
A new Many-Objective Ant Lion Optimizer (MaOALO) balances convergence and diversity in complex engineering problems. This novel algorithm demonstrates superior performance over existing methods in extensive benchmark tests.
Area of Science:
- Engineering Optimization
- Computational Intelligence
Background:
- Many-objective optimization (MaO) presents challenges in balancing convergence and diversity.
- Existing many-objective optimization algorithms (MaOAs) struggle with increasing complexity as the number of objectives grows.
Purpose of the Study:
- Introduce a novel Many-Objective Ant Lion Optimizer (MaOALO).
- Enhance convergence and diversity in many-objective optimization problems.
Main Methods:
- The MaOALO integrates the Ant Lion Optimizer with a reference point, niche preservation, and information feedback mechanism (IFM).
- Evaluated on real-world problems (RWMaOP1-RWMaOP5) and standard test suites (MaF1-MaF15, DTLZ1-DTLZ7).
Main Results:
- MaOALO shows superior performance compared to ARMOEA, NSGA-III, MaOTLBO, RVEA, MaOABC-TA, DSAE, RL-RVEA, and MaOEA-IH.
- Improvements demonstrated across metrics including GD, IGD, SP, SD, HV, and RT.
Conclusions:
- The proposed MaOALO effectively addresses the convergence-diversity trade-off in many-objective optimization.
- MaOALO offers a promising advancement for tackling complex, multi-objective engineering challenges.
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