An IGD+ Indicator-Based Metric for Interactive Evolutionary Multi-Objective Optimization Algorithms

Interactive evolutionary multi-objective optimization algorithms have received widespread research. The goal of these algorithms is to iteratively involve decision makers(DM) in the solution process, by providing preference information, the solution is guided to regions of interest. However, most in...

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Veröffentlicht in:Chinese Automation Congress (Online) S. 7 - 12
Hauptverfasser: Tian, Hao, Li, Fei, Zhou, Baiyu, Yang, Yujie, Huang, Xun
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 01.11.2024
Schlagworte:
Approximation algorithms
Automation
decision making
IGD+ indicator
interactive evolutionary multi-objective optimization algorithm
Measurement
Optimization
Pareto optimization
Quality indicators
ISSN:2688-0938
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Zusammenfassung:Interactive evolutionary multi-objective optimization algorithms have received widespread research. The goal of these algorithms is to iteratively involve decision makers(DM) in the solution process, by providing preference information, the solution is guided to regions of interest. However, most indicators are designed to assess how well algorithms approximate the entire Pareto optimal front. In order to assess the quality of the preferred solution sets obtained by interactive algorithms, this paper introduces a novel performance indicator based on the modified inverted generational distance (IGD + ). The indicator is examined using interactive evolutionary methods and demonstrating its capability to evaluate the performance of interactive algorithms.
ISSN:2688-0938
DOI:10.1109/CAC63892.2024.10865357