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AMOBH: Adaptive Multiobjective Black Hole Algorithm.

Chong Wu1,2, Tao Wu1,2, Kaiyuan Fu1,2

  • 1School of Automation, China University of Geosciences, Wuhan 430074, China.

Computational Intelligence and Neuroscience
|January 20, 2018
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Summary
This summary is machine-generated.

A new adaptive multiobjective black hole algorithm (AMOBH) uses cell density for efficient Pareto front optimization. This method balances convergence and diversity, outperforming existing algorithms in key performance metrics.

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Area of Science:

  • Computational Intelligence
  • Optimization Algorithms
  • Evolutionary Computation

Background:

  • Multiobjective optimization problems (MOPs) are common in science and engineering.
  • Existing evolutionary algorithms face challenges in balancing convergence and diversity.
  • The black hole algorithm provides a novel basis for optimization.

Purpose of the Study:

  • To introduce the Adaptive Multiobjective Black Hole Algorithm (AMOBH).
  • To enhance Pareto front convergence and diversity using a novel cell density assessment.
  • To adapt evolutionary strategies using Shannon entropy.

Main Methods:

  • Development of the Adaptive Multiobjective Black Hole Algorithm (AMOBH).
  • Introduction of a cell density metric for solution evaluation.
  • Mapping the Pareto front to a parallel cell coordinate system.
  • Adaptive strategy adjustment using Shannon entropy.
  • Fitness evaluation combining cell density and cell dominance.

Main Results:

  • AMOBH demonstrates superior performance compared to SPEA-II, PESA-II, NSGA-II, and MOEA/D.
  • The algorithm achieves a good balance between convergence and diversity.
  • AMOBH shows improved convergence rate, population diversity, and convergence.
  • Effective obtention of subpopulations across different Pareto regions.
  • Competitive time complexity against state-of-the-art methods.

Conclusions:

  • AMOBH offers a promising new approach for multiobjective evolutionary optimization.
  • The cell density metric is effective in maintaining Pareto front quality.
  • The adaptive framework enhances the algorithm's robustness and efficiency.