IGD Indicator-Based Evolutionary Algorithm for Many-Objective Optimization Problems

Inverted generational distance (IGD) has been widely considered as a reliable performance indicator to concurrently quantify the convergence and diversity of multiobjective and many-objective evolutionary algorithms. In this paper, an IGD indicator-based evolutionary algorithm for solving many-objec...

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Bibliographic Details
Published in:IEEE transactions on evolutionary computation 2019-04, Vol.23 (2), p.173-187
Main Authors: Sun, Yanan, Yen, Gary G., Yi, Zhang
Format: Article
Language:English
Subjects:
Algorithm design and analysis
Approximation algorithms
Business metrics
Convergence
Estimation
Evolutionary algorithms
Evolutionary computation
Genetic algorithms
Inverted generational distance (IGD)
linear assignment problem (LAP)
many-objective evolutionary optimization algorithm
Multiple objective analysis
nadir point
Optimization
Performance evaluation
Performance measurement
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Summary:Inverted generational distance (IGD) has been widely considered as a reliable performance indicator to concurrently quantify the convergence and diversity of multiobjective and many-objective evolutionary algorithms. In this paper, an IGD indicator-based evolutionary algorithm for solving many-objective optimization problems (MaOPs) has been proposed. Specifically, the IGD indicator is employed in each generation to select the solutions with favorable convergence and diversity. In addition, a computationally efficient dominance comparison method is designed to assign the rank values of solutions along with three newly proposed proximity distance assignments. Based on these two designs, the solutions are selected from a global view by linear assignment mechanism to concern the convergence and diversity simultaneously. In order to facilitate the accuracy of the sampled reference points for the calculation of IGD indicator, we also propose an efficient decomposition-based nadir point estimation method for constructing the Utopian Pareto front (PF) which is regarded as the best approximate PF for real-world MaOPs at the early stage of the evolution. To evaluate the performance, a series of experiments is performed on the proposed algorithm against a group of selected state-of-the-art many-objective optimization algorithms over optimization problems with 8-, 15-, and 20-objective. Experimental results measured by the chosen performance metrics indicate that the proposed algorithm is very competitive in addressing MaOPs.
ISSN:1089-778X
1941-0026
DOI:10.1109/TEVC.2018.2791283