On the convergence of biogeography-based optimization for binary problems
type: Item
instance of: scholarly article
title: On the convergence of biogeography-based optimization for binary problems
zbMATH Open document ID: 1407.90368
DOI: 10.1155/2014/147457
author: Haiping Ma
author: Dan Simon
author: Minrui Fei
published in: Mathematical Problems in Engineering
publication date: 2019-02-08T00:00:00Z
review text: Summary: Biogeography-based optimization (BBO) is an evolutionary algorithm inspired by biogeography, which is the study of the migration of species between habitats. A finite Markov chain model of BBO for binary problems was derived in earlier work, and some significant theoretical results were obtained. This paper analyzes the convergence properties of BBO on binary problems based on the previously derived BBO Markov chain model. Analysis reveals that BBO with only migration and mutation never converges to the global optimum. However, BBO with elitism, which maintains the best candidate in the population from one generation to the next, converges to the global optimum. In spite of previously published differences between genetic algorithms (GAs) and BBO, this paper shows that the convergence properties of BBO are similar to those of the canonical GA. In addition, the convergence rate estimate of BBO with elitism is obtained in this paper and is confirmed by simulations for some simple representative problems.
Mathematics Subject Classification ID: 90C59
Mathematics Subject Classification ID: 68T05
Mathematics Subject Classification ID: 90C40
zbMATH DE Number: 7015786
Wikidata QID: Q59063207
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2014/147457
OpenAlex ID: W2088987173
cites work: Convergence of an annealing algorithm
cites work: Analytical and numerical comparisons of biogeography-based optimization and genetic algorithms
cites work: Q4393471
cites work: The unified method analyzing convergence of genetic algorithms
cites work: Genetic algorithms: Principles and perpectives. A guide to GA theory
cites work: Q5306039
cites work: Analyzing evolutionary algorithms. The computer science perspective.
cites work: Bioinspired computation in combinatorial optimization. Algorithms and their computational complexity
cites work: On the convergence rates of genetic algorithms
cites work: Modeling genetic algorithms with Markov chains.
cites work: Q3876725
description: scientific article; zbMATH DE number 7015786