TY - CHAP
T1 - The harmony search in context with other nature inspired computational algorithms
AU - Wang, Xiaolei
AU - Gao, Xiao Zhi
AU - Zenger, Kai
PY - 2015
Y1 - 2015
N2 - 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.
AB - 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.
KW - Clonal selection algorithm
KW - Genetic algorithm
KW - Harmony search method
KW - Optimization
UR - http://www.scopus.com/inward/record.url?scp=85028872483&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-08356-8_3
DO - 10.1007/978-3-319-08356-8_3
M3 - Chapter
AN - SCOPUS:85028872483
T3 - SpringerBriefs in Applied Sciences and Technology
SP - 13
EP - 20
BT - SpringerBriefs in Applied Sciences and Technology
PB - Springer
ER -