TY - GEN
T1 - The bat algorithm with dynamic niche radius for multimodal optimization
AU - Iwase, Takuya
AU - Takano, Ryo
AU - Uwano, Fumito
AU - Sato, Hiroyuki
AU - Takadama, Keiki
PY - 2019/3/23
Y1 - 2019/3/23
N2 - In this paper, we proposed Bat Algorithm extending with Dynamic Niche Radius (DNRBA) which enables solutions to locate multiple local and global optima for solving multimodal optimization problems. This proposed algorithm is designed Bat Algorithm (BA) dealing with the exploration and the exploitation search with Niche Radius which is calculated by the fitness landscape and the number of local and global optima to avoid converging solutions at the same optimum. Although the Niche Radius is an effective niching method for locating solutions at the peaks in the fitness landscape, it is not applicable for uneven multimodal functions and easily fails to keep multiple optima. To overcome this problem, we introduce a dynamic niche sharing scheme which is able to adjust the distance of the niche radius in the search process dynamically for the BA. In the experiment, we employ several multimodal functions and compare DNRBA with the conventional BA to evaluate the performance of DNRBA.
AB - In this paper, we proposed Bat Algorithm extending with Dynamic Niche Radius (DNRBA) which enables solutions to locate multiple local and global optima for solving multimodal optimization problems. This proposed algorithm is designed Bat Algorithm (BA) dealing with the exploration and the exploitation search with Niche Radius which is calculated by the fitness landscape and the number of local and global optima to avoid converging solutions at the same optimum. Although the Niche Radius is an effective niching method for locating solutions at the peaks in the fitness landscape, it is not applicable for uneven multimodal functions and easily fails to keep multiple optima. To overcome this problem, we introduce a dynamic niche sharing scheme which is able to adjust the distance of the niche radius in the search process dynamically for the BA. In the experiment, we employ several multimodal functions and compare DNRBA with the conventional BA to evaluate the performance of DNRBA.
KW - Bat Algorithm
KW - Multimodal optimization
KW - Niching method
KW - Swarm Intelligence
UR - http://www.scopus.com/inward/record.url?scp=85068800331&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85068800331&partnerID=8YFLogxK
U2 - 10.1145/3325773.3325776
DO - 10.1145/3325773.3325776
M3 - Conference contribution
AN - SCOPUS:85068800331
T3 - ACM International Conference Proceeding Series
SP - 8
EP - 13
BT - Proceedings of the 2019 3rd International Conference on Intelligent Systems, Metaheuristics and Swarm Intelligence, ISMSI 2019
PB - Association for Computing Machinery
T2 - 3rd International Conference on Intelligent Systems, Metaheuristics and Swarm Intelligence, ISMSI 2019
Y2 - 23 March 2019 through 24 March 2019
ER -