Abstract
In the field of Differential Evolution (DE), a number of measures have been used to enhance algorithm. However, most of the measures need revision for fitting ensemble of different combinations of DE operators—ensemble DE algorithm. Meanwhile, although ensemble DE algorithm may show better performance than each of its constituent algorithms, there still exists the possibility of further improvement on performance with the help of revised measures. In this paper, we manage to implement measures into Ensemble of Differential Evolution Variants (EDEV). Firstly, we extend the collecting range of optional external archive of JADE—one of the constituent algorithm in EDEV. Then, we revise and implement the Event-Triggered Impulsive (ETI) control. Finally, Linear Population Size Reduction (LPSR) is used by us. Then, we obtain Improved Ensemble of Differential Evolution Variants (IEDEV). In our experiments, good performers in the CEC competitions on real parameter single objective optimization among population-based metaheuristics, state-of-the-art DE algorithms, or up-to-date DE algorithms are involved. Experiments show that our IEDEV is very competitive.
Highlights
Differential evolution (DE), a type of population-based metaheuristic, is reliable and powerful for global numerical optimization
According to the results of our experiments, our Improved Ensemble of Differential Evolution Variants (IEDEV) is competitive among DE algorithms
Most existing measures for improving DE algorithm need be revised for fitting ensemble DE algorithm
Summary
Differential evolution (DE), a type of population-based metaheuristic, is reliable and powerful for global numerical optimization. DE incorporates mutation, crossover and selection operators to move population gradually toward a global optimum [1]. Crossover is executed based on ~xi;g and ~vi;g to generate trial vectors ~ui;g 1⁄4 ðu1;i;g; u2;i;g; . All the types of ensemble listed above are be further introduced in our section for related work. Most of the measures need revision for fitting ensemble DE algorithm. Ensemble DE algorithm may show better performance than each of its constituent algorithms, there still exists the possibility of further improvement on performance with the help of revised measures. We enhance Ensemble of Differential Evolution Variants (EDEV) [21], an ensemble DE algorithm, by measures listed below. The ETI control is implemented to the two inefficient constituent DE algorithms in ensemble confirmed by comparison at intervals.
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