MOANA: Multi-Objective Ant Nesting Algorithm for Optimization Problems
- URL: http://arxiv.org/abs/2411.15157v1
- Date: Fri, 08 Nov 2024 18:31:53 GMT
- Title: MOANA: Multi-Objective Ant Nesting Algorithm for Optimization Problems
- Authors: Noor A. Rashed, Yossra H. Ali Tarik A. Rashid, Seyedali Mirjalili,
- Abstract summary: The Multi-Objective Ant Nesting Algorithm (MOANA) is a novel extension of the Ant Nesting Evolutionary Algorithm (ANA)
MOANA addresses key limitations of traditional evolutionary algorithms by improving scalability and diversity in multi-objective scenarios.
MOANA's applicability to real-world engineering optimization, such as welded beam design, showcases its ability to generate a broad range of optimal solutions.
- Score: 21.80971564725773
- License:
- Abstract: This paper presents the Multi-Objective Ant Nesting Algorithm (MOANA), a novel extension of the Ant Nesting Algorithm (ANA), specifically designed to address multi-objective optimization problems (MOPs). MOANA incorporates adaptive mechanisms, such as deposition weight parameters, to balance exploration and exploitation, while a polynomial mutation strategy ensures diverse and high-quality solutions. The algorithm is evaluated on standard benchmark datasets, including ZDT functions and the IEEE Congress on Evolutionary Computation (CEC) 2019 multi-modal benchmarks. Comparative analysis against state-of-the-art algorithms like MOPSO, MOFDO, MODA, and NSGA-III demonstrates MOANA's superior performance in terms of convergence speed and Pareto front coverage. Furthermore, MOANA's applicability to real-world engineering optimization, such as welded beam design, showcases its ability to generate a broad range of optimal solutions, making it a practical tool for decision-makers. MOANA addresses key limitations of traditional evolutionary algorithms by improving scalability and diversity in multi-objective scenarios, positioning it as a robust solution for complex optimization tasks.
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