Abstract:
This paper introduces a Cluster-based Differential
Evolution Algorithm with Heterogeneous Influence for solving
complex optimization problems. The idea behind this
combination is to classify the Differential Evolution population
into a number of clusters using k-means clustering method and to
apply different mutation strategies for the clusters. The number
of clusters is changed dynamically in each generation. The
proposed algorithm uses three mutation strategies: DE/bestgroup/
1/exp, DE/rand1/exp and DE/rand/1/bin. The DE/bestgroup/
1/exp is an improved mutation strategy that randomly
selects a portion of the population and then chooses the best
individual in the group to guide the evolution. The k-means
clustering algorithm is used periodically to fine-tune solutions
that are generated from DE/best-group/1/exp by producing new
clusters. This helps in balancing the exploration and exploitation
capabilities by using different mutation strategies for these
clusters to enhance diversity. The performance of the proposed
approach is tested on 25 complex benchmark functions on single
objective real-parameter numerical optimization. Results show
that the proposed algorithm exhibits competitive performance
when compared to other stat-of the-art algorithms algorithms.