Scholarlink

<p>The Giant Pacific Octopus Optimizer (GPOO)&#xa0;is combined with the mutation and crossover (MC) approach in this research&#xa0;to create the Crossover &amp; Mutation Giant Pacific Octopus Optimizer (XMGPOO), a novel hybrid model that attempts to tackle the issue. In construction management, find solutions to challenges with multi-objective problems&#xa0;such as time, cost,&#xa0;quality, and&#xa0;environment trade-off (TCQET). The GPOO model seeks to enhance discovery and raise the likelihood of obtaining optimal solutions via variety. It is inspired by the behavior of giant octopuses which&#xa0;discovered in the wild as well as popular MC methodologies. This hybridization serves as an illustration of the model’s performance, and it is suggested that, while the optimization process is underway, it be compared to previous algorithms such as&#xa0;LXMWOA, MBOICO, and XMACO. Based on all the model findings, the authors concluded that the model has superior development&#xa0;and a greater capacity for problem-solving flexibility, which is demonstrated in this study. </p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Time-cost-quality-environment trade-off problem: the crossover & mutation giant pacific octopus optimizer

  • 期刊论文

  • 发表日期：

    2025年5月21日

查看全文
  • Pham Vu Hong Son,
  • Luu Ngoc Quynh Khoi

摘要

The Giant Pacific Octopus Optimizer (GPOO) is combined with the mutation and crossover (MC) approach in this research to create the Crossover & Mutation Giant Pacific Octopus Optimizer (XMGPOO), a novel hybrid model that attempts to tackle the issue. In construction management, find solutions to challenges with multi-objective problems such as time, cost, quality, and environment trade-off (TCQET). The GPOO model seeks to enhance discovery and raise the likelihood of obtaining optimal solutions via variety. It is inspired by the behavior of giant octopuses which discovered in the wild as well as popular MC methodologies. This hybridization serves as an illustration of the model’s performance, and it is suggested that, while the optimization process is underway, it be compared to previous algorithms such as LXMWOA, MBOICO, and XMACO. Based on all the model findings, the authors concluded that the model has superior development and a greater capacity for problem-solving flexibility, which is demonstrated in this study.