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Micro Multiobjective Evolutionary Algorithm With Piecewise Strategy for Embedded-Processor-Based Industrial Optimization
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AI
DOI：10.1109/TCYB.2023.3336369.png)
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Abstract
En 中文
In some industrial applications, it is required to do off-line multiobjective optimization in embedded systems. Due to their limited computing and memory capability, embedded processor may not be able to run conventional multiobjective optimization evolutionary algorithms (MOEAs). This article proposes a micro MOEA with piecewise strategy $(\mu$ MOEA) for industrial optimization in embedded processor. $\mu$ MOEA introduces an improved piecewise strategy based on the MOEA/D framework, which serially optimizes subclusters to be compatible with embedded processor under limited computing power. For the purpose of further enhancing $\mu$ MOEA, a dynamic and flexible weight vector update trigger mechanism is proposed, so that the algorithm can save and utilize the computing resources of the embedded processor as much as possible. Abundant artificial test problems are carrying out to test the performance of $\mu$ MOEA. Through various experiments, it can be found that $\mu$ MOEA has outstanding performance in ZDT, DTLZ, SMOP, and MaF problems. Last and most importantly, $\mu$ MOEA is successfully applied to two specific application scenarios of industrial optimization on embedded processor for simulation, such as two different types of semi-autogenous grinding optimization problems and micro-grid energy optimization problem, which prove the feasibility of applying MOEA to embedded processor.
Keywords:
Optimization
Statistics
Sociology
Evolutionary computation
Search problems
Memory management
Artificial intelligence
Decomposition framework
embedded-processor-based industrial optimization
micro multiobjective evolutionary algorithm (MOEA)
piecewise strategy
Journal
IF:
10.5
Papers:
1.1W
Citations:
5.0W