An evolutionary swarm intelligence optimizer based on probabilistic distribution

In this study, we propose a novel approach to balance exploitation and exploration. The proposed approach is the Evolutionary Swarm Intelligence (ESI) optimizer, which combines an exploration-biased strategy with an exploitation-biased operator. The algorithm is built based on the collective behavior of biological groups, imitating their intelligence behavior. The biological evolutionary process, inspired by genetic algorithms, is applied to every individual in the algorithm. Both swarm intelligence and genetic algorithms have been widely used in practical problems, and their reliability has been proven. ESI is characterized by both spatial group intelligence behavior and temporal biological evolution. To test the performance of ESI, we used a classic test set from IEEE CEC2017 and 22 practical problems from IEEE CEC2011. The popular training tests of the dendritic neuron model were also included in the control trials. We compared ESI with some typical swarm intelligence algorithms and classic algorithms to evaluate its performance and ability to solve practical problems. The experimental results show that ESI outperforms other algorithms in terms of basic performance and the ability to solve practical problems.