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<p>This study introduces an improved variant of the Aquila optimizer (AO) called the modified adaptive weight approach Aquila optimizer (MAWA-AO), which integrates the adaptive weight mechanism to enhance performance. In this approach, the adaptive weight method replaces the expanded and narrowed exploitation processes of the original AO, thereby metigating computational complexity and improving efficiency. The suggested MAWA-AO is applied to a large scale time cost trade-off problem (TCTP) involving 630 activities, showing its ability to effectively achieve optimal or near optimal solutions. Comparative evaluations with advanced optimization algorithms, including teaching learning based optimization (TLBO), non-dominated sorting genetic algorithm (NSGA-II), particle swarm optimization (PSO), oppositional AO, and plain AO, show that MAWA-AO produces superior results in terms of the number of objective function evaluations (NFE) and the hypervolume (HV) indicator. The results indicate that MAWA-AO is a promising method for optimizing large scale construction projects in the field of construction management.</p>

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Modified adaptive-weight based-multi objective aquila optimizer for high-efficiency project scheduling in construction projects

  • 期刊论文

  • 发表日期：

    2025年9月3日

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  • Sudhanshu Maurya,
  • Bayram Ateş,
  • Amanullah Noori

摘要

This study introduces an improved variant of the Aquila optimizer (AO) called the modified adaptive weight approach Aquila optimizer (MAWA-AO), which integrates the adaptive weight mechanism to enhance performance. In this approach, the adaptive weight method replaces the expanded and narrowed exploitation processes of the original AO, thereby metigating computational complexity and improving efficiency. The suggested MAWA-AO is applied to a large scale time cost trade-off problem (TCTP) involving 630 activities, showing its ability to effectively achieve optimal or near optimal solutions. Comparative evaluations with advanced optimization algorithms, including teaching learning based optimization (TLBO), non-dominated sorting genetic algorithm (NSGA-II), particle swarm optimization (PSO), oppositional AO, and plain AO, show that MAWA-AO produces superior results in terms of the number of objective function evaluations (NFE) and the hypervolume (HV) indicator. The results indicate that MAWA-AO is a promising method for optimizing large scale construction projects in the field of construction management.