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In recent years, airport runways have become a more critical bottleneck in airports, and it is very unusual to use only one runway to solve the Aircraft Landing Problem (ALP). The ALP includes the aircraft's landing scheduling and assigning them to runways. In addition to certain limited time frames for aircraft while landing, to prevent accidents, the distance between aircraft should be restricted during the flight and in the landing phase. In this paper, to solve the problem in multi-runway mode, a solution is proposed that has considered all the limitations to create a trade-off between the runways to reduce the traffic in the runways. The present study considers the balance between bands and offers a new method of improving the Imperialist Competitive Algorithm (ICA) while reducing the cost due to the early and late landing of the aircraft. In other words, a novel approach for addressing the ALP with multiple criteria, employing a delay and early landing cost optimization technique and runway balance strategy, as well as using multi-runway, which will reflect the current realities of the aviation industry and provide a more accurate and relevant analysis, has been presented. Thirty-two benchmark instances were selected and compared with four famous algorithms: Particle Swarm Optimization (PSO), Immunoglobulin-Based Artificial Immune System (IAIS), Grey Wolf Optimizer (GWO), and flower pollination algorithm (FPA) as the results on a small-scale indicate, the ICA method performs superior outcomes, except for one case. Regarding the two objectives, the presented method, compared to other methods, managed to reduce the cost by 3.5% (on a small-scale). Furthermore, on a large scale (500 aircraft), improved ICA has been able to reduce the cost of the early or late arrival of the aircraft by 35%. [ABSTRACT FROM AUTHOR]

Considering the expansion of the Internet of Things (IoT) and the volume of data and user requests, Mobile Edge Computing (MEC) is considered a novel and efficient solution that puts decentralized servers at the network’s edge. This has the effect of lowering bandwidth demand and transmission latency. Optimal edge server placement and allocation, as the first stage of MEC, can improve end-user service quality, edge computing system utility, and cost and energy consumption. The majority of previous edge server placement studies have employed only one objective or developed a fitness function by the weighted sum method for optimization. Usually, using a single optimization objective without considering other objectives cannot yield the desired results for a problem with a multi-objective design. On the other hand, assigning weights to objectives can lead to losing optimal points in non-convex problems and selecting improper weights. Therefore, in this paper, we propose a multi-objective solution for the positioning and allocation of edge servers for MEC services based on the NSGA-II algorithm. In this regard, we identify two workload variance and latency reduction objectives with extensive evaluations. The experimental evaluation of the results using real-world data reveals that solutions based on the NSGA-II yield superior convergence and diversity of Pareto front points compared to Multi-Objective Particle Swarm Optimization (MOPSO), Multi-Objective Biogeography Based Optimization (MOBBO), and Adaptive Weighted Sum Method (AWSM). Additionally, it effectively mitigates workload variance on servers and exhibits an average latency reduction of 8.79% in comparison to the adaptive weighted-sum approach, 9.19% in comparison to MOPSO, and 0.28% in comparison to MOBBO. [ABSTRACT FROM AUTHOR]