Computational Intelligence - February 2017 - 41
VI. Conclusion
In this paper, a novel constrained multi-objective model for TA
planning is proposed. The geographic information-based multiobjective model has demonstrated its potential to significantly
reduce both the location update cost and paging cost in comparison with the results of a single-objective TA planning
model. Moreover, it can also provide a set of solutions for decision makers to select from, so as to make the TA planning more
flexible and adaptable to real-world circumstances. An EMO
algorithm based on the M2M decomposition strategy is
designed to solve the model. Fuzzy clustering based on the
geographic information is applied for initialization to enhance
the exploration. A specially designed coding method based on
the four-color theorem is utilized to encode and decode the
solutions. The experimental results have shown not only the
validity of the proposed multi-objective model, but also the
effectiveness of the M2M decomposition strategy to solve this
multi-objective constrained optimization model.
Acknowledgment
This work was supported in part by Natural Science Foundation of China (61673121, 61332002 and 61175073), in part by
the Natural Science Foundation of Guangdong Province
(2014A030313507), and in part by the Projects of Science and
Technology of Guangzhou (2014J4100209, 201508010008).
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