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.
Highlights
Many scientific and engineering application problems are complex multi-objective optimization problems involving more decision variables and optimization objectives, such as management and optimal distribution of energy resources [1], the short-term load forecast of power systems [2], the solution time of the joint energy-reserve market clearing problem [3], and wind signal prediction [4], etc
This paper will study the dynamic adaptive coevolution strategy for highdimensional complex optimization problems, where particles can be divided into model particles, which can guide the whole population to evolve toward the optimal value direction, and ordinary particles, which can guide the population to explore new search directions
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
Summary
Adaptive Coevolutionary Strategy for Keywords: large-scale complex optimization; dynamic adaptive evolutionary network; collaborative topology; search rules. Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations
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