A novel metaheuristic population algorithm for optimising the connection weights of neural networks

HIGHLIGHTS

• What: In this paper, the focus is on avoiding local optima and decreasing sensitivity to initialisation. In the following, the authors provide a detailed explanation of the key constituents of DDE-OP and describe the proposed technique, while Fig 2 depicts the general structure of the DDE-OP algorithm. By leveraging these advanced techniques, the authors aim to enhance the effectiveness and efficiency of the MPA in solving complex optimisation problems. 3.3 Division period DDE-OP uses a clustering mechanism at regular intervals to enhance the exploration of the search space, similar to the approach proposed . . .
  

   

  

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