Objective Space-Based Population Generation to Accelerate Evolutionary Algorithms for Large-Scale Many-Objective Optimization

The generation and updating of solutions, e.g., crossover and mutation, of many existing evolutionary algorithms directly operate on decision variables. The operators are very time consuming for large-scale and many-objective optimization problems. Different from them, this work proposes an objectiv...

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Bibliographic Details
Published in:IEEE transactions on evolutionary computation Vol. 27; no. 2; pp. 326 - 340
Main Authors: Deng, Qi, Kang, Qi, Zhang, Liang, Zhou, MengChu, An, Jing
Format: Journal Article
Language:English
Published: New York IEEE 01.04.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Approximation algorithms
Convergence
Decision variables decomposition
Evolutionary algorithms
Evolutionary computation
Genetic algorithms
Inverse problems
large-scale evolution
many-objective evolution
Multiple objective analysis
objective space mapping
Optimization
Sociology
Statistics
ISSN:1089-778X, 1941-0026
Online Access:Get full text
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Summary:The generation and updating of solutions, e.g., crossover and mutation, of many existing evolutionary algorithms directly operate on decision variables. The operators are very time consuming for large-scale and many-objective optimization problems. Different from them, this work proposes an objective space-based population generation method to obtain new individuals in the objective space and then map them to decision variable space and synthesize new solutions. It introduces three new objective vector generation methods and uses a linear mapping method to tightly connect objective space and decision one to jointly determine new-generation solutions. A loop can be formed directly between two spaces, which can generate new solutions faster and use more feedback information in the objective space. In order to demonstrate the performance of the proposed algorithm, this work performs a series of empirical experiments involving both large-scale decision variables and many objectives. Compared with the state-of-the-art traditional and large-scale algorithms, the proposed method exceeds or at least reaches its peers' best level in overall performance while achieving great saving in execution time.
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ISSN:1089-778X
1941-0026
DOI:10.1109/TEVC.2022.3166815