Achievement scalarizing function sorting for strength Pareto evolutionary algorithm in many-objective optimization

Multi-objective evolutionary algorithms (MOEAs) have proven their effectiveness in solving two or three objective problems. However, recent research shows that Pareto-based MOEAs encounter selection difficulties facing many similar non-dominated solutions in dealing with many-objective problems. In...

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Vydáno v:Neural computing & applications Ročník 33; číslo 11; s. 6369 - 6388
Hlavní autoři: Li, Xin, Li, Xiaoli, Wang, Kang, Yang, Shengxiang, Li, Yang
Médium: Journal Article
Jazyk:angličtina
Vydáno: London Springer London 01.06.2021
Springer Nature B.V
Témata:
Artificial Intelligence
Clustering
Computational Biology/Bioinformatics
Computational Science and Engineering
Computer Science
Convergence
Data Mining and Knowledge Discovery
Evolutionary algorithms
Genetic algorithms
Image Processing and Computer Vision
Multiple objective analysis
Optimization
Original Article
Probability and Statistics in Computer Science
Sorting algorithms
Strategy
Achievement scalarizing function
Many-objective optimization
Evolutionary algorithm
Diversity
Convergence
ISSN:0941-0643, 1433-3058
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Shrnutí:Multi-objective evolutionary algorithms (MOEAs) have proven their effectiveness in solving two or three objective problems. However, recent research shows that Pareto-based MOEAs encounter selection difficulties facing many similar non-dominated solutions in dealing with many-objective problems. In order to reduce the selection pressure and improve the diversity, we propose achievement scalarizing function sorting strategy to make strength Pareto evolutionary algorithm suitable for many-objective optimization. In the proposed algorithm, we adopt density estimation strategy to redefine a new fitness value of a solution, which can select solution with good convergence and distribution. In addition, a clustering method is used to classify the non-dominated solutions, and then, an achievement scalarizing function ranking method is designed to layer different frontiers and eliminate redundant solutions in the environment selection stage, thus ensuring the convergence and diversity of non-dominant solutions. The performance of the proposed algorithm is validated and compared with some state-of-the-art algorithms on a number of test problems with 3, 5, 8, 10 objectives. Experimental studies demonstrate that the proposed algorithm shows very competitive performance.
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ISSN:0941-0643
1433-3058
DOI:10.1007/s00521-020-05398-1