TY - GEN
T1 - A modified whale optimization algorithm based on chaos initialization and regulation operation
AU - Ruiye, Jiang
AU - Tao, Chao
AU - Songyan, Wang
AU - Ming, Yang
N1 - Publisher Copyright:
© 2019 Technical Committee on Control Theory, Chinese Association of Automation.
PY - 2019/7
Y1 - 2019/7
N2 - A rising meta-heuristic swarm intelligent computation algorithm, the Whale optimization Algorithm (WOA), which has been proved competitive with other intelligent optimization algorithms in solving different optimization problems. To accelerate the convergence rate of WOA, this paper has proposed a new improved whale optimization algorithm, called Chaos Regulation Whale optimization Algorithm (CRWOA). The chaotic map generator composed with three kinds of chaotic maps has been used to initialize the population. Furthermore, Cauchy distribution and Gauss distribution are introduced to regulate the position in each iteration. To analyze the superiority of CRWOA further, ten single-objective benchmark test functions are chosen. Through the comparison and analysis of the optimal results, CRWOA is demonstrated superior in general. It has faster convergence speed and better results than the other algorithms when less iteration number, less population quantity and wider ranges of variables are set.
AB - A rising meta-heuristic swarm intelligent computation algorithm, the Whale optimization Algorithm (WOA), which has been proved competitive with other intelligent optimization algorithms in solving different optimization problems. To accelerate the convergence rate of WOA, this paper has proposed a new improved whale optimization algorithm, called Chaos Regulation Whale optimization Algorithm (CRWOA). The chaotic map generator composed with three kinds of chaotic maps has been used to initialize the population. Furthermore, Cauchy distribution and Gauss distribution are introduced to regulate the position in each iteration. To analyze the superiority of CRWOA further, ten single-objective benchmark test functions are chosen. Through the comparison and analysis of the optimal results, CRWOA is demonstrated superior in general. It has faster convergence speed and better results than the other algorithms when less iteration number, less population quantity and wider ranges of variables are set.
KW - Benchmark functions
KW - Chaotic map
KW - Computation intelligence
KW - Continuous probability distribution
KW - Whale optimization algorithm
UR - https://www.scopus.com/pages/publications/85074390557
U2 - 10.23919/ChiCC.2019.8866240
DO - 10.23919/ChiCC.2019.8866240
M3 - 会议稿件
AN - SCOPUS:85074390557
T3 - Chinese Control Conference, CCC
SP - 2702
EP - 2707
BT - Proceedings of the 38th Chinese Control Conference, CCC 2019
A2 - Fu, Minyue
A2 - Sun, Jian
PB - IEEE Computer Society
T2 - 38th Chinese Control Conference, CCC 2019
Y2 - 27 July 2019 through 30 July 2019
ER -