Orthogonal learning harmonizing mutation-based fruit fly-inspired optimizers
DOI10.1016/J.APM.2020.05.019zbMath1481.90268OpenAlexW3026414371MaRDI QIDQ2049769
Shimin Li, Yutao Yang, Mingjing Wang, Ali Asghar Heidari, Hui ling Chen
Publication date: 27 August 2021
Published in: Applied Mathematical Modelling (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.apm.2020.05.019
global optimizationswarm intelligenceengineering designGaussian mutationfruit fly optimization alogrithm
Evolutionary algorithms, genetic algorithms (computational aspects) (68W50) Nonconvex programming, global optimization (90C26) Approximation methods and heuristics in mathematical programming (90C59)
Uses Software
Cites Work
- An improved harmony search algorithm for solving optimization problems
- Enhancing the performance of biogeography-based optimization using polyphyletic migration operator and orthogonal learning
- Differential evolution -- a simple and efficient heuristic for global optimization over continuous spaces
- Enhancing the performance of biogeography-based optimization using multitopology and quantitative orthogonal learning
- DSLC-FOA: improved fruit fly optimization algorithm for application to structural engineering design optimization problems
- An improved grasshopper optimization algorithm with application to financial stress prediction
- A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm
- A new meta-heuristic algorithm for continuous engineering optimization: harmony search theory and practice
- An empirical study about the usefulness of evolution strategies to solve constrained optimization problems
- A New Metaheuristic Bat-Inspired Algorithm
- An improved ant colony optimization for constrained engineering design problems
This page was built for publication: Orthogonal learning harmonizing mutation-based fruit fly-inspired optimizers