TY - JOUR
T1 - Objective Space-Based Population Generation to Accelerate Evolutionary Algorithms for Large-Scale Many-Objective Optimization
AU - Deng, Qi
AU - Kang, Qi
AU - Zhang, Liang
AU - Zhou, Meng Chu
AU - An, Jing
N1 - Funding Information:
This work was supported in part by the National Natural Science Foundation of China under Grant 51775385 and Grant 61703279; in part by the Strategy Research Project of Artificial Intelligence Algorithms of Ministry of Education of China; in part by the Shanghai Industrial Collaborative Science and Technology Innovation Project under Grant 2021-cyxt2-kj10; in part by the Shanghai Municipal Science and Technology Major Project under Grant 2021SHZDZX0100; and in part by the Fundamental Research Funds for the Central Universities.
Publisher Copyright:
© 1997-2012 IEEE.
PY - 2023/4/1
Y1 - 2023/4/1
N2 - 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.
AB - 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.
KW - Decision variables decomposition
KW - large-scale evolution
KW - many-objective evolution
KW - objective space mapping
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U2 - 10.1109/TEVC.2022.3166815
DO - 10.1109/TEVC.2022.3166815
M3 - Article
AN - SCOPUS:85128613576
SN - 1089-778X
VL - 27
SP - 326
EP - 340
JO - IEEE Transactions on Evolutionary Computation
JF - IEEE Transactions on Evolutionary Computation
IS - 2
ER -