TY - JOUR
T1 - AHPS2
T2 - An optimizer using adaptive heterogeneous particle swarms
AU - Chu, Xianghua
AU - Hu, Mengqi
AU - Wu, Teresa
AU - Weir, Jeffery D.
AU - Lu, Qiang
N1 - Funding Information:
This research was partially supported by funds from the National Science Foundation award under Grant No. CNS-1239257, from the United States Transportation Command (USTRANSCOM) in concert with the Air Force Institute of Technology (AFIT) under an ongoing Memorandum of Agreement and from the National Science Foundation of China (Grant No. 71171064). The U.S. Government is authorized to reproduce and distribute for governmental purposes notwithstanding any copyright annotation of the work by the author(s). The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of USTRANSCOM, AFIT, the Department of Defense, or the U.S. Government.
PY - 2014/10/1
Y1 - 2014/10/1
N2 - Particle swarm optimization (PSO) has suffered from premature convergence and lacked diversity for complex problems since its inception. An emerging advancement in PSO is multi-swarm PSO (MS-PSO) which is designed to increase the diversity of swarms. However, most MS-PSOs were developed for particular problems so their search capability on diverse landscapes is still less than satisfactory. Moreover, research on MS-PSO has so far treated the sub-swarms as cooperative groups with minimum competition (if not none). In addition, the size of each sub-swarm is set to be fixed which may encounter excessive computational cost. To address these issues, a novel optimizer using Adaptive Heterogeneous Particle SwarmS (AHPS2) is developed in this research. In AHPS2, multiple heterogeneous swarms, each consisting of a group of homogenous particles having similar learning strategy, are introduced. Two complementary search techniques, comprehensive learning and a subgradient method, are studied. To best take advantage of the heterogeneous learning strategies, an adaptive competition strategy is proposed so the size of each swarm can be dynamically adjusted based on its group performance. The analyses of the swarm heterogeneity and the competition models are presented to validate the effectiveness. Furthermore, comparisons between AHPS2 and state-of-the-art algorithms are grouped into three categories: 36 regular benchmark functions (30-dimensional), 20 large-scale benchmark functions (1000-dimensional) and 3 real-world problems. Experimental results show that AHPS2 displays a better or comparable performance compared to the other swarm-based or evolutionary algorithms in terms of solution accuracy and statistical tests.
AB - Particle swarm optimization (PSO) has suffered from premature convergence and lacked diversity for complex problems since its inception. An emerging advancement in PSO is multi-swarm PSO (MS-PSO) which is designed to increase the diversity of swarms. However, most MS-PSOs were developed for particular problems so their search capability on diverse landscapes is still less than satisfactory. Moreover, research on MS-PSO has so far treated the sub-swarms as cooperative groups with minimum competition (if not none). In addition, the size of each sub-swarm is set to be fixed which may encounter excessive computational cost. To address these issues, a novel optimizer using Adaptive Heterogeneous Particle SwarmS (AHPS2) is developed in this research. In AHPS2, multiple heterogeneous swarms, each consisting of a group of homogenous particles having similar learning strategy, are introduced. Two complementary search techniques, comprehensive learning and a subgradient method, are studied. To best take advantage of the heterogeneous learning strategies, an adaptive competition strategy is proposed so the size of each swarm can be dynamically adjusted based on its group performance. The analyses of the swarm heterogeneity and the competition models are presented to validate the effectiveness. Furthermore, comparisons between AHPS2 and state-of-the-art algorithms are grouped into three categories: 36 regular benchmark functions (30-dimensional), 20 large-scale benchmark functions (1000-dimensional) and 3 real-world problems. Experimental results show that AHPS2 displays a better or comparable performance compared to the other swarm-based or evolutionary algorithms in terms of solution accuracy and statistical tests.
KW - Adaptive competition strategy
KW - Heterogeneous learning
KW - Heterogeneous swarm
KW - Multiple swarm
KW - Particle swarm optimization
KW - Search behavior analysis
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U2 - 10.1016/j.ins.2014.04.043
DO - 10.1016/j.ins.2014.04.043
M3 - Article
AN - SCOPUS:84902474583
SN - 0020-0255
VL - 280
SP - 26
EP - 52
JO - Information Sciences
JF - Information Sciences
ER -