The harmony search in context with other nature inspired computational algorithms

Xiaolei Wang*, Xiao Zhi Gao, Kai Zenger

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapterScientificpeer-review

1 Citation (Scopus)

Abstract

Inspiration drawn from nature and modeling of natural processes are the two common characteristics existing in most NIC algorithms. These methodologies, therefore, share many similarities, e.g., adaptation, learning, and evolution, and have a general flowchart including candidate initialization, operation, and renewal. On the other hand, mimicking various natural phenomena leads to their different generation, evaluation, selection, and update mechanisms, which may result in individual inherent distinctive properties, advantages, as well as drawbacks in the performances of dealing with different optimization problems. For example, the CSA on the basis of modeling the clonal selection principle of the artificial immune system performs well in the local search but suffers from a long convergence time. This chapter compares three typical evolutionary optimization methods, GA, CSA, and HS, with regard to their structures and performances using illustrative examples.

Original languageEnglish
Title of host publicationSpringerBriefs in Applied Sciences and Technology
PublisherSpringer Verlag
Pages13-20
Number of pages8
Edition9783319083551
DOIs
Publication statusPublished - 2015
MoE publication typeA3 Part of a book or another research book

Publication series

NameSpringerBriefs in Applied Sciences and Technology
Number9783319083551
ISSN (Print)2191-530X
ISSN (Electronic)2191-5318

Keywords

  • Clonal selection algorithm
  • Genetic algorithm
  • Harmony search method
  • Optimization

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