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The K-connected Deployment and Power Assignment Problem (DPAP) in WSNs aims at deciding both the sensor locations and transmit power levels, for maximizing both the network coverage and lifetime under K-connectivity constraints, in a single run. Recently, it is shown that the Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D) is a strong enough tool for dealing with un-constraint real life problems (such as DPAP), emphasizing the importance of incorporating problem specific knowledge for increasing its efficiency. Since the K-connected DPAP requires constraint handling, several techniques are investigated and compared, including a DPAP-specific Repair Heuristic (RH) that transforms an infeasible network design into a feasible one and maintains the MOEA/D's efficiency simultaneously.
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