In this paper, we have observed that different types of plants in nature can use their own survival mechanisms to adapt to various living environments. A new population-based vegetation evolution (VEGE) algorithm is proposed to solve optimization problems by interactively simulating the growth and maturity periods of plants. In the growth period, individuals explore their local areas and grow in potential directions, while individuals generate many seed individuals and spread them as widely as possible in the maturity period. The main contribution of our proposed VEGE is to balance exploitation and exploration from a novel perspective, which is to perform these two periods in alternation to switch between two different search capabilities. To evaluate the performance of the proposed VEGE, we compare it with three well-known algorithms in the evolutionary computation community: differential evolution, particle swarm optimization, and enhanced fireworks algorithm - and run them on 28 benchmark functions with 2-dimensions (2D), 10D, and 30D with 30 trial runs. The experimental results show that VEGE is efficient and promising in terms of faster convergence speed and higher accuracy. In addition, we further analyze the effects of the composition of VEGE on performance, and some open topics are also given.
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