Population-Based Meta-Heuristic Optimization Algorithm Booster: An Evolutionary and Learning Competition Scheme

In a Population-Based Meta-Heuristic Optimization Algorithm (PMOA), individuals in the population
will constantly generate new promising individuals, to form new populations. Although the population
continuously changes, the variations in each individual are traceable in most algorithms. An individual
in the population comes from the individual in the previous population. The direction of the evolution
of populations can be identified on top of this historical inheritance relationship, which improves
the efficiency of PMOAs and solves optimization problems more effectively. Since Recurrent Neural
Networks (RNNs) are able to capture the temporal dependencies in sequences, we are motivated to
propose a novel but simple Evolutionary and Learning Competition Scheme (ELCS), also referred
to as the PMOA Booster, in which individuals keep changing for the better fitness based on the
heuristic rules of the PMOA while an RNN is used to learn the process that each individual changes
in order to guide the generation of promising individuals. The ELCS automatically selects the RNN
or PMOA which generates more individuals with the better fitness. We test the proposed scheme
using the benchmark of IEEE Congress on Evolutionary Computation 2022 competition (CEC 2022).
The results show that this scheme is able to boost the performance of both the classical and state-of-
the-art PMOAs and outperforms its counterparts. Also, the ELCS produces promising results in two
real-world industrial scenarios. We believe that the effectiveness of the proposed ELCS is due to the
adaptive competition between the RNN and the PMOA.