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Non-Dominated Sorting Bidirectional Differential Coevolution

Cicero S. R. Mendes, Aluizio F. R. Araújo, Lucas R. C. Farias

TL;DR

A variant of the bidirectional coevolution algorithm (BiCo) with differential evolution (DE) with novelties in the model, including the DE differential mutation and crossover operators as the main search engine and a non-dominated sorting selection scheme.

Abstract

Constrained multiobjective optimization problems (CMOPs) are commonly found in real-world applications. CMOP is a complex problem that needs to satisfy a set of equality or inequality constraints. This paper proposes a variant of the bidirectional coevolution algorithm (BiCo) with differential evolution (DE). The novelties in the model include the DE differential mutation and crossover operators as the main search engine and a non-dominated sorting selection scheme. Experimental results on two benchmark test suites and eight real-world CMOPs suggested that the proposed model reached better overall performance than the original model.

Non-Dominated Sorting Bidirectional Differential Coevolution

TL;DR

A variant of the bidirectional coevolution algorithm (BiCo) with differential evolution (DE) with novelties in the model, including the DE differential mutation and crossover operators as the main search engine and a non-dominated sorting selection scheme.

Abstract

Constrained multiobjective optimization problems (CMOPs) are commonly found in real-world applications. CMOP is a complex problem that needs to satisfy a set of equality or inequality constraints. This paper proposes a variant of the bidirectional coevolution algorithm (BiCo) with differential evolution (DE). The novelties in the model include the DE differential mutation and crossover operators as the main search engine and a non-dominated sorting selection scheme. Experimental results on two benchmark test suites and eight real-world CMOPs suggested that the proposed model reached better overall performance than the original model.

Paper Structure

This paper contains 13 sections, 9 equations, 1 figure, 2 tables, 4 algorithms.

Table of Contents

  1. INTRODUCTION
  2. NSBiDiCo
  3. Determination of the non-dominated Infeasible Solutions
  4. Angle-based Selection
  5. EXPERIMENTAL SETUP
  6. Test Instances
  7. Parameter Settings
  8. Performance Metrics
  9. EXPERIMENTAL RESULTS
  10. Comparison for DOC Test Suite
  11. Comparison for LIRCMOP Test Suite
  12. Comparison on Real-World problems
  13. CONCLUSION

Figures (1)

  • Figure 1: The flowchart of the proposed NSBiDiCo algorithm.