Scholarlink

<p>Optimization has become a necessary part of any activities in our life. Thus, bunch of optimization algorithms have been introduced by researchers during the past decades. However, large-scale optimization problems are still challenging. In this research, inspired by research and development procedure, a new metaheuristic algorithm called Research and Development (R&amp;D)-Based Algorithm (RDBA) has been proposed for optimization of large-scale problems. The mechanism of searching for the best result is based on four activities of “Learning,” doing a “Teamwork,” participating in the “Conference,” and “Self-study.” The conducted method is tested on 13 well-known benchmarks with dimensions ranging from 30 to 1000, and the results are compared with the previous studies working in this area. The simulations demonstrate that RDBA is much effective than 12 powerful algorithms in solving high-dimensional complicated functions regarding solution precision, stability, and convergence rate.</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

A new R&D-based algorithm for optimization of large-scale problems

  • 期刊论文

  • 发表日期：

    2025年2月15日

查看全文
  • Hossein Hajimiri,
  • Amir Bagheri

摘要

Optimization has become a necessary part of any activities in our life. Thus, bunch of optimization algorithms have been introduced by researchers during the past decades. However, large-scale optimization problems are still challenging. In this research, inspired by research and development procedure, a new metaheuristic algorithm called Research and Development (R&D)-Based Algorithm (RDBA) has been proposed for optimization of large-scale problems. The mechanism of searching for the best result is based on four activities of “Learning,” doing a “Teamwork,” participating in the “Conference,” and “Self-study.” The conducted method is tested on 13 well-known benchmarks with dimensions ranging from 30 to 1000, and the results are compared with the previous studies working in this area. The simulations demonstrate that RDBA is much effective than 12 powerful algorithms in solving high-dimensional complicated functions regarding solution precision, stability, and convergence rate.