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A Multiscenario Optimization Evolutionary Algorithm Based on Transfer Framework
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AI
DOI：10.1109/TEVC.2022.3211643.png)
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Abstract
En 中文
Multiscenario optimization problems involve multiple scenarios to be optimized simultaneously, where each scenario corresponds to a multiobjective optimization problem with specific operating conditions. The goal is to find a group of public compromised optimal solutions (PCOSs) achieving compromised optimal in every scenario. This type of optimization problems widely exists in real-world applications, but the research on it is few. In this article, a Multiscenario Optimization Evolutionary Algorithm based on a transfer framework is proposed. In one iteration, for each scenario, its knee solutions are recognized as its transfer candidates and are used to construct a constraint hyperplane, which determines whether transfer candidates from other scenarios can be the transferable solutions in this underlying scenario. Then, among all transfer candidates, those accepted by all scenarios are identified as the PCOSs and stored in archive. Afterwards, the archive updating process is applied to guarantee the optimality of solutions in archive and control the size of archive. Finally, each scenario's accepted transferable solutions are utilized in its offspring generation, thus achieving information transfer between different scenarios. Experimental results on a group of benchmark functions verify the superiority of the proposed design in terms of both optimality and computational efficiency over existing approaches.
Keywords:
Optimization
Task analysis
Evolutionary computation
Statistics
Sociology
Uncertainty
Tropical cyclones
Knowledge transfer
multiobjective evolutionary algorithm (MOEA)
multiscenario optimization problem (MSOP)
Journal
IF:
12
Papers:
1.8K
Citations:
2.4W