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MOEA3H: Multi-objective evolutionary algorithm based on hierarchical decision, heuristic learning and historical

Ziyu Hu1, Zihan Li1, Hao Sun1

  • 1School of Electrical and Engineering, Yanshan University, Qinhuangdao, 066004, China.

ISA Transactions
|January 23, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a new multi-objective evolutionary algorithm (MOEA3H) that improves solution generation and selection for complex problems by using hierarchical decisions and heuristic learning. MOEA3H demonstrates superior performance on benchmark test problems.

Keywords:
Dynamic multi-objective optimizationEvolutionary algorithmObjective decompositionPareto front

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Area of Science:

  • Computational intelligence
  • Optimization algorithms
  • Evolutionary computation

Background:

  • Complex multi-objective problems require efficient methods for generating and selecting feasible solutions.
  • Existing evolutionary algorithms face challenges in navigating complex solution landscapes and static optimization.
  • Understanding evolutionary history and environmental context is crucial for improving multi-objective optimization.

Purpose of the Study:

  • To develop an advanced multi-objective evolutionary algorithm (MOEA3H) that addresses key challenges in solution generation and selection.
  • To enhance the search capabilities by integrating hierarchical decision-making and heuristic learning.
  • To leverage historical data and environmental information for more comprehensive optimization.

Main Methods:

  • Analysis of multi-objective problem mechanisms using evolutionary history and environmental information.
  • Proposal of a hierarchical decision strategy based on rank fitness of distance correlation to guide evolutionary operators.
  • Introduction of heuristic learning through dynamic evolution for static optimization problems.
  • Utilization of historical solution landscape data for comprehensive feasible region searching.

Main Results:

  • The proposed Multi-Objective Evolutionary Algorithm based on Hierarchical decision, Heuristic learning, and Historical environment (MOEA3H) was developed.
  • MOEA3H achieved superior performance, outperforming existing methods on 10 out of 19 test problems using the IGD metric.
  • The algorithm demonstrated top performance on 14 out of 19 test problems when evaluated using the Hypervolume metric.

Conclusions:

  • MOEA3H offers a significant advancement in tackling complex multi-objective optimization problems.
  • The integration of hierarchical decision-making, heuristic learning, and historical environmental information enhances algorithm efficiency and effectiveness.
  • The algorithm's strong performance on benchmark problems validates its potential for real-world applications.