Multi-strategy differential evolution algorithm based on adaptive hash clustering and its application in wireless sensor networks - Bu, Xianglong; Zhang, Qingke; Gao, Hao; Zhang, Huaxiang (Expert systems with applications, 2024)
Population-based algorithms aim to explore the entire solution space in global numerical optimization problems. However, it is important to acknowledge that the solution spaces of different problems possess distinct characteristics, and even distinct regions within the same solution space can vary significantly. Efficient exploration of these diverse regions necessitates the utilization of distinct search models. To address this challenge, this study proposes a new variant of the Differential Evolution (DE) algorithm called MHDE. The MHDE algorithm introduces hash clustering technology combined with adaptive mutation strategies. The integration of hash clustering technology enables fast population clustering, significantly enhancing clustering efficiency. Additionally, a method for evaluating the population state is designed that allows for an adaptive clustering quantity to adapt the clustering quantity to the population state. Furthermore, a novel method is devised to calculate individual improvements considering the varying levels of difficulty that individuals face in achieving improvements within a population. This method is combined with a parameter adaptive mechanism, resulting in a weighted parameter adaptive mechanism. Experiments are conducted on the CEC2017 benchmark suite to evaluate the performance of the MHDE algorithm. The experimental results demonstrate that MHDE exhibits competitive performance compared with other efficient DE variants. Moreover, the MHDE algorithm is applied to the node deployment problem in wireless sensor networks (WSNs). The MHDE algorithm demonstrates efficient performance in node deployment problems through simulation experiments in different scenarios. •Introducing a BLSH technique for population clustering.•Designing a new method to evaluate population states.•Presenting a novel approach for calculating individual contributions.•Utilizing an external population to accelerate algorithm convergence.