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A Stable Large-Scale Multiobjective Optimization Algorithm with Two Alternative Optimization Methods.

Tianyu Liu1, Junjie Zhu1, Lei Cao1

  • 1College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China.

Entropy (Basel, Switzerland)
|May 16, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a new large-scale multiobjective optimization algorithm (LSMOEA-TM) that balances convergence and diversity using novel grouping strategies. It effectively solves complex optimization problems with reduced computational costs.

Keywords:
Bayesian-based parameter adjustingevolutionary algorithmslarge-scale multiobjective optimizationtwo alternative optimization methods

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Area of Science:

  • Computational intelligence
  • Optimization algorithms
  • Evolutionary computation

Background:

  • Large-scale multiobjective optimization presents challenges in balancing convergence and diversity.
  • Existing algorithms struggle with stable decision variable grouping strategies.

Purpose of the Study:

  • To propose a novel large-scale multiobjective optimization algorithm (LSMOEA-TM).
  • To enhance the efficiency and stability of solving large-scale multiobjective optimization problems.
  • To reduce computational costs through adaptive parameter tuning.

Main Methods:

  • Introduced two alternative optimization methods with distinct variable grouping strategies.
  • Incorporated a Bayesian-based parameter-adjusting strategy.
  • Tested LSMOEA-TM against four established large-scale multiobjective evolutionary algorithms on benchmark problems.

Main Results:

  • The proposed LSMOEA-TM demonstrated superior performance compared to existing algorithms.
  • The novel grouping strategies effectively balanced convergence and population diversity.
  • The Bayesian parameter adjustment reduced computational overhead.

Conclusions:

  • LSMOEA-TM is an effective algorithm for large-scale multiobjective optimization.
  • The proposed methods offer a promising direction for future research in evolutionary computation.
  • The algorithm provides a stable and efficient approach to complex optimization tasks.