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Large-scale wireless sensor network (LSWSN) is composed of a huge number of sensor nodes that are distributed in some region of interest (ROI), to sense and measure the environmental conditions like pressure, temperature, pollution levels, humidity, wind, and so on. The objective is to collect data for real-time monitoring so that appropriate actions can be taken promptly. One of the sensor nodes used in an LSWSN is called the sink node, which is responsible for processing and analyzing the collected information. It works as a station between the network sensor nodes and the administrator. Also, it is responsible for controlling the whole network. Determining the sink node location in an LSWSN is a challenging task, as it is crucial to the network lifetime, for keeping the network activity to the most possible extent. In this paper, the Harris' hawks optimization (HHO) algorithm is employed to solve this problem and subsequently the Prim's shortest path algorithm is used to reconstruct the network by making minimum transmission paths from the sink node to the rest of the sensor nodes. The performance of HHO is compared with other well-known algorithms such as particle swarm optimization (PSO), flower pollination algorithm (FPA), grey wolf optimizer (GWO), sine cosine algorithm (SCA), multi-verse optimizer (MVO), and whale optimization algorithm (WOA). The simulation results of different network sizes, with single and multiple sink nodes, show the superiority of the employed approach in terms of energy consumption and localization error, and ultimately prolonging the lifetime of the network in an efficacious way.INDEX TERMS Large-scale wireless sensor network, Harris' hawks optimization, topology control, sink node placement.

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This paper proposes an enhanced orca predation algorithm (OPA) called the Lévy flight orca predation algorithm (LFOPA). LFOPA improves OPA by integrating the Lévy flight (LF) strategy into the chasing phase of OPA and employing the greedy selection (GS) strategy at the end of each optimization iteration. This enhancement is made to avoid the entrapment of local optima and to improve the quality of acquired solutions. OPA is a novel, efficient population-based optimizer that surpasses other reliable optimizers. However, owing to the low diversity of orcas, OPA is prone to stalling at local optima in some scenarios. In this paper, LFOPA is proposed for addressing global and real-world optimization challenges. To investigate the validity of the proposed LFOPA, it is compared with seven robust optimizers, including the improved multi-operator differential evolution algorithm (IMODE), covariance matrix adaptation evolution strategy (CMA-ES), gravitational search algorithm (GSA), grey wolf optimizer (GWO), moth-flame optimization algorithm (MFO), Harris hawks optimization (HHO), and the original OPA on 10 unconstrained test functions linked to 2020 IEEE Congress on Evolutionary Computation (CEC’20). Furthermore, four different design engineering issues, including the welded beam, the tension/compression spring, the pressure vessel, and the speed reducer, are solved using the proposed LFOPA, to test its applicability. It was also employed to address node localization challenges in wireless sensor networks (WSNs) as an example of real-world applications. Results and tests of significance show that the proposed LFOPA performs much better than OPA and other competitors. LFOPA simulation results on node localization challenges are much superior to other competitors in terms of minimizing squared errors and localization errors.