Incorporating directional information within a differential evolution algorithm for multi-objective optimization

The field of Differential Evolution (DE) has demonstrated important advantages in single objective optimization. To date, no previous research has explored how the unique characteristics of DE can be applied to multi-objective optimization. This paper explains and demonstrates how DE can provide advantages in multi-objective optimization using directional information. We present three novel DE variants for multi-objective optimization, and a report of their performance on four multi-objective problems with different characteristics. The DE variants are compared with the NSGA-II (Non-dominated Sorting Genetic Algorithm). The results suggest that directional information yields improvements in convergence speed and spread of solutions. Categories and Subject Descriptors

• Type: Conference
• Year: 2006
• Where: GECCO
• Authors: Antony W. Iorio, Xiaodong Li
• Keywords: DE Variants, GECCO 2006, Multi-objective Optimization, Optimization, Single Objective Optimization