Indicator-based multi-objective adaptive bacterial foraging algorithm for RFID network planning
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This work develops a novel indicator-based multi-objective bacterial colony foraging algorithm (I-MOBCA) for complex multi-objective or many-objective optimization problems. The main idea of I-MOBCA is to develop an adaptive and cooperative model by combining bacterial foraging, adaptive searching, cell-to-cell communication and preference indicator-based measure strategies. In this algorithm, each bacterium can adopt its run-length unit to appropriately balance exploitation and exploration states, and the quality of position or solution is calculated on the basis of the binary quality indicator to determine the Pareto dominance relation. Our algorithm uses Pareto concept and preference indicator-based measure to determine the non-dominated solutions in each generation, which can essentially reduce the computation complexity. With several mathematical benchmark functions, I-MOBCA is proved to have significantly better performance over compared algorithms for solving some complex multi-objective optimization problems. Then the proposed I-MOBCA is used to solve three-objective RFID network planning problem. Simulation results show that I-MOBCA proves to be superior for planning RFID networks than compared algorithms in terms of optimization accuracy and computation robustness.
KeywordsPreference indicator Adaptive searching Bacterial forging algorithm Multi-objective optimization RFID network planning
This work is supported by National key Research and Development Plan of China under Grant No.(2016YFB1100501, 2017YFB1103603, 2017YFB1103603), National Natural Science Foundation of China under Grant No. (61772365, 41772123, 61602343, 51607122, 51575158, 51378350 and 51305167), Tianjin Province Science and Technology Projects under Grant No. (16ZLZDZF00150, 17JCQNJC04500, 17JCYBJC15100) and Basic Scientific Research Business Funded Projects of Tianjin (TJPUZK20170128, TJPUZK20170129).
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