Accelerating Vegetation Evolution with Mutation Strategy and Gbased Growth Strategy

We propose two strategies, mutation strategy and Gbased growth strategy, to enhance the performance of standard vegetation evolution (VEGE) that simulates the growth and reproduction of vegetation repeatedly to find the global optimum. We introduce two different mutation methods into the growth period and the maturity period individually to increase the diversity of population by simulating different types of mutations in real plants. Inspired by various growth patterns of real plants, the Gbased growth strategy is proposed to replace a completely random growth operation of standard VEGE and bias all non-optimal individuals to grow towards the current best area. We design a series of controlled experiments to evaluate the performance of our proposed strategies using 28 benchmark functions from CEC2013 suite with three different dimensions. The experimental results confirmed the mutation strategy can increase the diversity and the Gbased growth strategy plays an important role in accelerating convergence. Besides, the combination of both strategies can further improve the VEGE performance.