Heterogeneous Multi-Population Cultural Algorithm

In this article, a new architecture for Cultural Algorithms is proposed. The new architecture incorporates a number of sub-populations such that each sub-population is designed to optimize different parameters. According to the assigned parameters, each sub-population is a set of partial solutions which are managed by a local CA. Local CAs do not communicate with each other directly. In this architecture, a shared belief space is considered to record the best parameters. Local CAs send their best partial solutions to the belief space every generation. The belief space then updates its record of best parameters which will be used later by local CAs to evaluate their partial solutions. Due to incorporating a number of heterogeneous sub-populations, the proposed architecture is called Heterogeneous Multi-Population Cultural Algorithm (HMP-CA). Additionally, a local search heuristic is proposed to speed up the convergence of HMP-CA. The proposed HMP-CA is evaluated using a number of numerical optimization benchmark functions. The results show that the HMP-CA without the local search offers competitive results compared to the state-of-the-art methods and incorporating the proposed local search heuristic makes the proposed HMP-CA more efficient such that it outperforms all the state-of-the-art methods.