Some evolutionary algorithms have been proposed to address biparty multiobjective optimization problems (BPMOPs). However, all these algorithms are centralized algorithms which directly obtain the privacy information ...
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ISBN:
(纸本)9783031366215;9783031366222
Some evolutionary algorithms have been proposed to address biparty multiobjective optimization problems (BPMOPs). However, all these algorithms are centralized algorithms which directly obtain the privacy information including objective functions from decision makers (DMs). This paper transforms the centralized algorithm OptMPNDS2 into a distributed framework for BPMOPs and focuses on the privacy issue in the framework. The framework has a server and two clients, and each client belongs to a DM. The clients keep their objective functions locally, evaluate individuals, and upload Pareto levels and crowding distances of all individuals to the server. The server performs the other operations including reproduction and selection of offspring. Experimental results show that the performance of the framework is very close to OptMPNDS2. Besides, two privacy attacks are proposed when one client is malicious. Experimental results show that the client could steal approximate Pareto optimal solutions of the other honest DM.
Unmanned aerial vehicles (UAVs) have been widely used in urban missions, and proper planning of UAV paths can improve mission efficiency while reducing the risk of potential third-party impact. Existing work has consi...
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Unmanned aerial vehicles (UAVs) have been widely used in urban missions, and proper planning of UAV paths can improve mission efficiency while reducing the risk of potential third-party impact. Existing work has considered all efficiency and safety objectives for a single decision-maker (DM) and regarded this as a multiobjectiveoptimization problem (MOP). However, there is usually not a single DM but two DMs, i.e., an efficiency DM and a safety DM, and the DMs are only concerned with their respective objectives. The final decision is made based on the solutions of both DMs. In this paper, for the first time, bipartymultiobjective UAV path planning (BPMO-UAVPP) problems involving both efficiency and safety departments are modeled. The existing multiobjective immune algorithm with nondominated neighbor-based selection (NNIA), the hybrid evolutionary framework for the multiobjective immune algorithm (HEIA), and the adaptive immune-inspired multiobjective algorithm (AIMA) are modified for solving the BPMO-UAVPP problem, and then biparty multiobjective optimization algorithms, including the BPNNIA, BPHEIA, and BPAIMA, are proposed and comprehensively compared with traditional multiobjective evolutionary algorithms and typical multiparty multiobjective evolutionary algorithms (i.e., OptMPNDS and OptMPNDS2). The experimental results show that BPAIMA performs better than ordinary multiobjective evolutionary algorithms such as NSGA-II and multiparty multiobjective evolutionary algorithms such as OptMPNDS, OptMPNDS2, BPNNIA and BPHEIA.
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