Improved Crowding Distance for NSGA-II
By Xiang-Hua Chu, Xinjie Yu. 2018
11 citations recorded by the source corpus.
Non-dominated sorting genetic algorithm II (NSGA-II) does well in dealing with multi-objective problems. When evaluating validity of an algorithm for multi-objective problems, two kinds of indices are often considered simultaneously, i.e. the convergence to Pareto Front and the distribution characteristic. The crowding distance in the standard NSGA-II has the property that solutions within a cubic have the same crowding distance, which has no contribution to the convergence of the algorithm. Actually the closer to the Pareto Front a solution is, the higher priority it should have. In the paper, the crowding distance is redefined while keeping almost all the advantages of the original one. Moreover, the speed of converging to the Pareto Front is faster. Finally, the improvement is proved to be effective by applying it to solve nine Benchmark problems.
Subfields: Artificial Intelligence, Computational Theory and Mathematics
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
In Computational Theory and Mathematics
Citations in the corpus
- Fast, Accurate and Lightweight Super-Resolution with Neural Architecture Search (2019, methodology): “Tournament Selection We calculate the crowding distance as noted in [10] to render a uniform distribution of our models, and we apply tournament selection (k = 2) to control the evolution pressure.”
- Multi-Objective Reinforced Evolution in Mobile Neural Architecture Search (2019, methodology): “We use the same strategy as NSGA-II with an improved crowding distance to address the existing problem of original definition that doesn’t differentiate individuals within the same cuboid (Chu & Yu, 2018).”