期刊
IEEE ACCESS
卷 7, 期 -, 页码 73182-73206出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2019.2918753
关键词
Supply-demand-based optimization; global optimization; engineering design; constrained problems; optimization algorithm; particle swarm optimization; swarm intelligence
资金
- Natural Science Foundation of Hebei Province of China [E2018402092, F2017402142]
- Scientific Research Key Project of University of Hebei Province of China [ZD2017017]
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.
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