A Multipopulation Dynamic Adaptive Coevolutionary Strategy for Large-Scale Complex Optimization Problems

Abstract

In this paper, a multipopulation dynamic adaptive coevolutionary strategy is proposed for large-scale optimization problems, which can dynamically and adaptively adjust the connection between population particles according to the optimization problem characteristics. Based on analysis of the network evolution characteristics of collaborative search between particles, a dynamic adaptive evolutionary network (DAEN) model with multiple interconnection couplings is established in this algorithm. In the model, the swarm type is divided according to the judgment threshold of particle types, and the dynamic evolution of collaborative topology in the evolutionary process is adaptively completed according to the coupling connection strength between different particle types, which enhances the algorithm’s global and local searching capability and optimization accuracy. Based on that, the evolution rules of the particle swarm dynamic cooperative search network were established, the search algorithm was designed, and the adaptive coevolution between particles in different optimization environments was achieved. Simulation results revealed that the proposed algorithm exhibited a high optimization accuracy and converging rate for high-dimensional and large-scale complex optimization problems.

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“…The existing variable decomposition strategies can be classified into three broader classes, i.e., fixed grouping, 25 , 26 random grouping, 27 , 28 and dynamic grouping. 29 , 30 , 31 …”

“…Yin et al. 31 designed a multi-population dynamic coevolutionary strategy able to adjust the connection dynamically and adaptively between individuals. Nevertheless, such a grouping strategy might incur computationally expensive costs and non-uniform information distribution as well.…”