Push and Pull Search Embedded in an M2M Framework for Solving Constrained Multi-objective Optimization Problems

2019/06/02 by Fan, Zhun, Wang, Zhaojun, Li, Wenji +6
#FOS: Computer and information sciences #Neural and Evolutionary Computing (cs.NE)

paper · doi:10.48550/arxiv.1906.00402

Abstract

In dealing with constrained multi-objective optimization problems (CMOPs), a key issue of multi-objective evolutionary algorithms (MOEAs) is to balance the convergence and diversity of working populations.

Related

• Push and Pull Search for Solving Constrained Multi-objective Optimization Problems
• LLM4CMO: Large Language Model-aided Algorithm Design for Constrained Multiobjective Optimization
• An Improved Epsilon Constraint-handling Method in MOEA/D for CMOPs with Large Infeasible Regions
• An Efficient Approach for Solving Expensive Constrained Multiobjective Optimization Problems
• Evolutionary Multitasking for Multiobjective Continuous Optimization: Benchmark Problems, Performance Metrics and Baseline Results