In order to prolong the lifetime of the wireless sensor network (WSN) during locating attacks' position, an Attack Localization Task Allocation (ALTA) algorithm based on multi-objective binary particle swarm optimization is proposed to determine the nodes joining to locate attacks. ALTA models the attack localization tasks as a multi-objective optimization model, in which the main idea is to construct objective functions consisting of total task execution time, total energy consumption and load balance for achieving the minimum time cost, energy cost and maximum load balance to prolong the network lifetime, and construct the constraints consisting of the work load and the received signal strength (RSS) space constraints for ensuring enough beacon nodes and closer beacon nodes being selected to successfully locate the attacker node. During the processing of searching for the optional solution, a nonlinear decreasing inertia weight is adopted to overcome the drawback of easily trapping in local optimum exiting in binary particle swarm optimization (BPSO) algorithm, and the elite archive strategy is adopted to dynamically maintain the optimal solutions and improve the convergence rate. Simulation results indicate that ALTA is suitable for attack localization task allocation in wireless sensor networks with shorter period of task processing and lower energy consumption.

Based on multiobjective particle swarm optimization, a localization algorithm named multiobjective particle swarm optimization localization algorithm (MOPSOLA) is proposed to solve the multiobjective optimization localization issues in wireless sensor networks. The multiobjective functions consist of the space distance constraint and the geometric topology constraint. The optimal solution is found by multiobjective particle swarm optimization algorithm. Dynamic method is adopted to maintain the archive in order to limit the size of archive, and the global optimum is obtained according to the proportion of selection. The simulation results show considerable improvements in terms of localization accuracy and convergence rate while keeping a limited archive size by a method using the global optimal selection operator and dynamically maintaining the archive.