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Multi-Objective Mayfly Optimization Algorithm Based on Dimensional Swap Variation for RFID Network Planning
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AI
DOI：10.1109/JSEN.2022.3151932.png)
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Abstract
En 中文
In this study, a multi-objective mayfly optimization algorithm based on dimensional swap variation (DSV-MOMA) is proposed to solve Multi-objective RFID network planning problem (MORNP). The contributions of this work are as following: firstly, improving multi-objective mayfly optimization algorithm (MOMA)'s ability to solve high-dimensional nonlinear optimization problems; secondly, DSV-MOMA is used to solve the MORNP problem, and optimize two and three of the four objective functions simultaneously; lastly, the fuzzy decision mechanism is used to select an optimal solution objectively from pareto optimal solutions. The proposed DSV-MOMA algorithm contributes to having better diversity and convergence when solving high dimensional nonlinear and discontinuous test functions in comparison to other popular metaheuristic algorithms. DSV-MOMA also performs well when dealing with MORNP problems. In most experiments, DSV-MOMA can reduce interference effectively, and obtain satisfactory load balance and power while ensuring a higher coverage.
Keywords:
Optimization
Radiofrequency identification
Sensors
Planning
Linear programming
Interference
Pareto optimization
Multi-objective RFID network planning (MORNP)
multi-objective mayfly optimization algorithm (MOMA)
dimensional swap variation
pareto
Journal
IF:
4.5
Papers:
2.2W
Citations:
7.3W
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