Internet of things (IoT) is a ubiquitous network that helps the system to monitor and organize the world through processing, collecting, and analyzing the data produced by IoT objects. The accurate localization of IoT objects is indispensable for most IoT applications, especially healthcare monitoring. Utilizing GPS as the positioning system is not cost-efficient and does not apply to some environments (e.g., deep forests, oceans, inside the buildings, etc.). Hereupon, copious position estimation approaches are developed in the literature. Among range-free approaches, distance vector-Hop (DV-Hop) is the widely used algorithm due to its straightforward applicability and can estimate the position of unknown objects that are far-off the anchors. Due to its low accuracy, various techniques were proposed to increase the accuracy of basic DV-Hop. In the most recent approach, meta-heuristic algorithms were used, the results of which were promising. In the present paper, Tunicate Swarm Algorithm and Harris hawk optimization were initially hybridized. Afterthought, the resulting hybrid algorithm was enhanced by appending a new phase. Then, the proposed hybrid algorithm was intermingled with the DV-Hop algorithm. In the first set of experiments, the proposed hybrid algorithm was evaluated on 50 test functions using average, SD, box plot, and p-value criteria. In the second part, the proposed localization algorithm's efficiency was investigated in twenty-eight different manners using node localization error, average localization error, and localization error variance metrics. The effectiveness of the contributions was evident from the experimental results.

Traveling Salesman Problem (TSP) is an intricate discrete hybrid optimization problem that is categorized as an NP-Hard problem. The objective of the TSP is to find the shortest Hamilton route between cities to visit all existing cities and returning to the original city, from which the route started. Various researches have been carried out on TSP, and manifold solutions have been proposed to find the shortest route between cities, but none has been able to solve this problem completely. In this paper, a novel discrete version of the Farmland Fertility Algorithm is proposed, which uses three neighborhood searching mechanisms, and one Crossover operator. The Metropolis Acceptance Criterion has also been utilized to evade the local optimal traps. Furthermore, the Roulette Wheel selection technique is used to select neighboring mechanisms during the optimization process. Moreover, a local search mechanism is used to maximize the performance of the proposed algorithm. To prove the effectiveness of the contributions, and illustrate the efficiency of the proposed algorithm, the TSP library is evaluated on 37 data sets and compared with some well-known similar methods. The simulation results showed the superiority of the proposed algorithm against other comparative methods.