TY - GEN
T1 - Particle swarm optimization with ensemble of inertia weight strategies
AU - Shirazi, Muhammad Zeeshan
AU - Pamulapati, Trinadh
AU - Mallipeddi, Rammohan
AU - Veluvolu, Kalyana Chakravarthy
N1 - Publisher Copyright:
© Springer International Publishing AG 2017.
PY - 2017
Y1 - 2017
N2 - Particle swarm optimization (PSO) has gained significant attention for solving numerical optimization problems in different applications. However, the performance of PSO depends on the appropriate setting of inertia weight and the optimal setting changes with generations during the evolution. Therefore, different adaptive inertia weight strategies have been proposed. However, the best inertia weight adaptive strategy depends on the nature of the optimization problem. In this paper, different inertia weight strategies such as linear, Gompertz, logarithmic and exponential decreasing inertia weights as well as chaotic and oscillating inertia weight strategies are explored. Finally, PSO with an adaptive ensemble of linear & Gompertz decreasing inertia weights is proposed and compared with other strategies on a diverse set of benchmark optimization problems with different dimensions. Additionally, the proposed method is incorporated into heterogeneous comprehensive learning PSO (HCLPSO) to demonstrate its effectiveness.
AB - Particle swarm optimization (PSO) has gained significant attention for solving numerical optimization problems in different applications. However, the performance of PSO depends on the appropriate setting of inertia weight and the optimal setting changes with generations during the evolution. Therefore, different adaptive inertia weight strategies have been proposed. However, the best inertia weight adaptive strategy depends on the nature of the optimization problem. In this paper, different inertia weight strategies such as linear, Gompertz, logarithmic and exponential decreasing inertia weights as well as chaotic and oscillating inertia weight strategies are explored. Finally, PSO with an adaptive ensemble of linear & Gompertz decreasing inertia weights is proposed and compared with other strategies on a diverse set of benchmark optimization problems with different dimensions. Additionally, the proposed method is incorporated into heterogeneous comprehensive learning PSO (HCLPSO) to demonstrate its effectiveness.
KW - Ensemble of inertia weight strategies
KW - Gompertz decreasing inertia weight
KW - Linear decreasing inertia weight
KW - Particle swarm optimization
UR - https://www.scopus.com/pages/publications/85026765076
U2 - 10.1007/978-3-319-61824-1_15
DO - 10.1007/978-3-319-61824-1_15
M3 - Conference contribution
AN - SCOPUS:85026765076
SN - 9783319618234
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 140
EP - 147
BT - Advances in Swarm Intelligence - 8th International Conference, ICSI 2017, Proceedings
A2 - Tan, Ying
A2 - Takagi, Hideyuki
A2 - Shi, Yuhui
PB - Springer Verlag
T2 - 8th International Conference on Swarm Intelligence, ICSI 2017
Y2 - 27 July 2017 through 1 August 2017
ER -