Multi-Objective Mayfly Optimization Algorithm Based on Dimensional Swap Variation for RFID Network Planning

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.