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A non-dominated sorting based multi-objective neural network algorithm.

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Summary

This study adapts the Neural Network Algorithm (NNA) for multi-objective optimization problems (MOPs). The modified NNA demonstrates strong performance in solving complex MOPs, outperforming existing algorithms.

Keywords:
Multi-objectiveMulti-objective Neural Network AlgorithmNeural network algorithmNon-dominated sortingPareto front

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Area of Science:

  • Artificial Intelligence
  • Computational Optimization

Background:

  • Neural Network Algorithm (NNA) shows promise for single-objective problems.
  • Multi-objective optimization problems (MOPs) present unique challenges.

Purpose of the Study:

  • To adapt the Neural Network Algorithm (NNA) for solving multi-objective optimization problems (MOPs).
  • To introduce novel concepts for solution initialization, position update, and target selection within NNA for MOPs.

Main Methods:

  • Restructuring the original Neural Network Algorithm (NNA) with fundamental changes.
  • Implementing novel strategies for candidate solution initialization, position update, and target solution selection.
  • Testing the proposed multi-objective NNA on benchmark MOPs and comparing with eight state-of-the-art algorithms.

Main Results:

  • The proposed multi-objective NNA shows very good overall optimization ability for MOPs.
  • Performance was evaluated using Inverse Generational Distance (IGD) and Hypervolume (HV) metrics.
  • Statistical validation using Wilcoxon signed rank test supports the findings.

Conclusions:

  • The adapted Neural Network Algorithm is effective for addressing challenging multi-objective optimization problems.
  • The proposed modifications enhance NNA's capability for MOPs.
  • This work presents a novel approach to multi-objective optimization using NNA.