TY - JOUR
T1 - Supply-Demand-Based Optimization
T2 - A Novel Economics-Inspired Algorithm for Global Optimization
AU - Zhao, Weiguo
AU - Wang, Liying
AU - Zhang, Zhenxing
N1 - Funding Information:
This work was supported in part by the Natural Science Foundation of Hebei Province of China under Grant E2018402092 and Grant F2017402142, and in part by the Scientific Research Key Project of University of Hebei Province of China under Grant ZD2017017.
Publisher Copyright:
© 2013 IEEE.
PY - 2019/5/23
Y1 - 2019/5/23
N2 - A novel metaheuristic optimization algorithm, named supply-demand-based optimization (SDO), is presented in this paper. SDO is a swarm-based optimizer motivated by the supply-demand mechanism in economics. This algorithm mimics both the demand relation of consumers and supply relation of producers. The proposed algorithm is compared with other state-of-the-art counterparts on 29 benchmark test functions and six engineering optimization problems. The results on the unconstrained test functions prove that SDO is able to provide very promising results in terms of exploration, exploitation, local optima avoidance, and convergence rate. The results on the constrained engineering problems suggest that SDO is considerately competitive in terms of computational expense, convergence rate, and solution accuracy. The codes are available at https://www.mathworks.com/matlabcentral/fileexchange/71764-supply-demand-based-optimization.
AB - A novel metaheuristic optimization algorithm, named supply-demand-based optimization (SDO), is presented in this paper. SDO is a swarm-based optimizer motivated by the supply-demand mechanism in economics. This algorithm mimics both the demand relation of consumers and supply relation of producers. The proposed algorithm is compared with other state-of-the-art counterparts on 29 benchmark test functions and six engineering optimization problems. The results on the unconstrained test functions prove that SDO is able to provide very promising results in terms of exploration, exploitation, local optima avoidance, and convergence rate. The results on the constrained engineering problems suggest that SDO is considerately competitive in terms of computational expense, convergence rate, and solution accuracy. The codes are available at https://www.mathworks.com/matlabcentral/fileexchange/71764-supply-demand-based-optimization.
KW - constrained problems
KW - engineering design
KW - global optimization
KW - optimization algorithm
KW - particle swarm optimization
KW - Supply-demand-based optimization
KW - swarm intelligence
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U2 - 10.1109/ACCESS.2019.2918753
DO - 10.1109/ACCESS.2019.2918753
M3 - Article
AN - SCOPUS:85067678324
SN - 2169-3536
VL - 7
SP - 73182
EP - 73206
JO - IEEE Access
JF - IEEE Access
M1 - 8721125
ER -