.png){kind=icon}
返回
Preference-inspired coevolutionary algorithm with sparse autoencoder for many-objective optimization
.png){kind=icon}
PRE
AI
DOI：10.1007/s00500-023-09050-7.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
The function of most multi-objective algorithms (MOEAs) is to provide an overall trade-off Pareto front to the decision makers (DMs). But DMs actually tend to have a preference for a specific subset of the Pareto front. So combing decision makers' preference information with many-objective optimization methods has recently been a hot survey topic in the research field of many-objective optimization. The preference-inspired coevolutionary algorithms using goal vectors (PICEA-g) coevolve a family of decision makers' preferences called preference set together with a population of candidate solutions. It is a classic and effective algorithm, but it has shortcomings in distribution and there is a waste of computing resources. So we use a sparse autoencoder as a controller to perform feature processing on the target solution set in the process of coordinated evolution and modify the fitness assignment formula to improve the diversity. The combination of PICEA-g and sparse autoencoder framework enhance the convergence and expand the ability of the PICEA-g algorithm. The preference-inspired coevolutionary algorithm with sparse autoencoder (PICEA-g/SAE) is evaluated by three widely used benchmark suites and compared with nine classic multi-objective evolutionary algorithms to prove the advantages of the sparse autoencoder framework. The experimental results show that PICEA-g/SAE could have a good performance on many DTLZ test problems. Moreover, PICEA-g/SAE on UF1-9 and WFG1-9 test problems in low dimensions could have good convergence, diversity, and spread compared with other algorithms.
Keyword:
Many-objective optimization
Preference-inspired coevolutionary algorithms
Sparse autoencoder
Decision makers
期刊
IF:
2.5
论文数:
1.0W
被引数:
2.1W
机构
引用论文
Preference-Inspired Co-Evolutionary Algorithms With Local PCA Oriented Goal Vectors for Many-Objective Optimization.png)
IEEE ACCESS
OAAI
IEEE ACCESSIF3.6
OAAI分享收藏Whitefly species efficiency in transmitting cassava mosaic and brown streak virus diseases白粉虱种类传播木薯花叶病和木薯褐 streak 病的效率OAAI
OAAI分享收藏分享收藏Optimization of an appointment scheduling problem for healthcare systems based on the quality of fairness service using whale optimization algorithm and NSGA-IISCIENTIFIC REPORTSOAAI
SCIENTIFIC REPORTSIF3.9
OAAI分享收藏Multi-objective mean-semi-entropy model for optimal standalone micro-grid planning with uncertain renewable energy resourcesENERGY
ENERGYIF9.4
PREAI
分享收藏
查看更多内容