Rashed Noor A, Ali Yossra H, Rashid Tarik A, Mirjalili Seyedali
Computer Sciences Dept., Univ. of Technology, Baghdad, Iraq.
Computer Sciences & Engineering Dept., Artificial Intelligence Centre and Innovation, Univ. of Kurdistan Hewler, Iraq.
Heliyon. 2024 Nov 12;11(1):e40087. doi: 10.1016/j.heliyon.2024.e40087. eCollection 2025 Jan 15.
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.
本文提出了多目标蚁巢算法（MOANA），它是蚁巢算法（ANA）的一种新颖扩展，专门设计用于解决多目标优化问题（MOP）。MOANA纳入了诸如沉积权重参数等自适应机制，以平衡探索和利用，同时多项式变异策略确保了多样且高质量的解决方案。该算法在包括ZDT函数和2019年IEEE进化计算大会（CEC）多模态基准在内的标准基准数据集上进行了评估。与MOPSO、MOFDO、MODA和NSGA - III等先进算法的对比分析表明，MOANA在收敛速度和帕累托前沿覆盖率方面具有卓越性能。此外，MOANA在诸如焊接梁设计等实际工程优化中的适用性，展示了其生成广泛最优解的能力，使其成为决策者的实用工具。MOANA通过提高多目标场景下的可扩展性和多样性，解决了传统进化算法的关键局限性，使其成为复杂优化任务的强大解决方案。
