This paper addresses the joint optimization of trajectories and bandwidth allocation for multiple Unmanned Aerial Vehicles (UAVs) to enhance energy efficiency in the cooperative data collection problem. We focus on an important yet underestimated aspect of the system, where action synchronization across all UAVs is impossible. Since most existing learning-based solutions are not designed to learn in this asynchronous environment, we formulate the trajectory planning problem as a Decentralized Partially Observable Semi-Markov Decision Process and introduce an asynchronous multi-agent learning algorithm to learn UAVs’ cooperative policies. Once the UAVs’ trajectory policies are learned, the bandwidth allocation can be optimally solved based on local observations at each collection point. Comprehensive empirical results demonstrate the superiority of the proposed method over other learning-based and heuristic baselines in terms of both energy efficiency and mission completion time. Additionally, the learned policies exhibit robustness under varying environmental conditions.

In this study, we investigate the trade-off between reliability and security in unmanned aerial vehicle (UAV) communications systems, considering a UAV-terrestrial network aided by a relay powered by a dedicated power beacon. For this system, we derive the outage probability (OP) under both exact and approximate frameworks and compute the approximations in the closed-form expressions. For the security aspect, we also derive the intercept probability (IP) under exact and approximated frameworks. To minimize the IP, a friendly jamming technique is employed whereby the power beacon constantly broadcasts artificial noise (AN) toward an eavesdropper. Based on the derived mathematical framework, we then formulate a bi-objective optimization problem by jointly minimizing the OP and IP with respect to the UAV’s position and the time-switching (TS) ratio. A suitable algorithm, named non-dominated sorting genetic algorithm version II (NSGA-II), is deployed to obtain the sub-optimal solution. Finally, numerical results are presented to verify the accuracy of the proposed mathematical framework and the superiority of jointly minimizing both the OP and IP using only the channel statistics.

Current years have witnessed an increment in the number of research activities on improving the efficacy of multitasking algorithms for tackling challenging optimization problems. However, current approaches often present two potential problems. First, although tasks may have different characteristics, existing literature usually utilizes only one search operator for all of them. Second, while multitasking environments comprise tasks of varying difficulty, previous proposals treat them equally. This article proposes an algorithm named ensemble multifactorial evolution with biased skill-factor inheritance (EME-BI) for optimizing a large number of tasks simultaneously. In EME-BI, an effective parameter adaptation based on the knowledge transfer quality with biased skill-factor inheritance mechanism is designed to minimize negative transfer and allocate generated offspring to tasks that need resources. Besides, instead of using only one fixed search operator, EME-BI can automatically select the most appropriate one for each task at each evolutionary stage. Finally, the proposed algorithm is armed with a dynamically adjusted population size to promote exploitation. Empirical studies on various many-task benchmark problems and a real-world problem are conducted to verify the efficiency of EME-BI. The results portrayed that EME-BI achieves highly competitive performance compared to several state-of-the-art algorithms regarding the solution quality, convergence trend, and computation time. This proposal also won first prize at the CEC2021 Competition on Evolutionary Multitask Optimization, multitask single-objective optimization.

With the breakthrough of electromagnetic power transfer technology, wireless charging has emerged as a hopeful solution for the energy provisioning problem in wireless sensor networks. One of the prominent issues that affect the potential exploitation of this technology is the charging scheduling problem. However, existing works on this topic either focus mainly on using a single mobile charger for the whole network or suffer from several common limitations such as enforcing the chargers to visit all sensors or applying the rigid full-charging scheme. Moreover, they rarely delve into maximizing the survival nodes ratio, which impacts directly on the multi-hop communication of the network. This paper addresses the charging scheduling for multiple mobile chargers without the above limitations. We first formulate a maximum survival ratio problem and prove its NP-hardness. A charging scheme that exploits the advantages of the multifactorial evolutionary algorithm is then proposed to optimize the charging paths of all chargers simultaneously. We finally evaluate the efficacy of the proposed algorithm through extensive simulations. The experimental results demonstrate that our scheduling scheme provides promising outcomes in terms of survival ratio and the traveling energy of chargers.

Wireless Rechargeable Sensor Network has emerged as a potential solution for the constrained energy problem in sensor networks in recent years. The charging process has been employed to prolong the sensor's lifetime. An effective charging algorithm requires simultaneously optimize the charging path and charging time at each charging location under the Mobile Charger's limited energy. The existing methods, however, are generally lacking in the literature. Moreover, most works are based on the assumption that Mobile Charger has sufficient or infinite energy to visit and charge all sensors within each charging cycle. This constraint leads to prolonging the waiting charging time of energy-hurry sensors and unnecessary visiting of energy-sufficient sensors. In this paper, we aim at minimizing energy depletion and the charging cost of Mobile Charger in Wireless Rechargeable Sensor Networks without the mentioned limitations above. We first mathematically formulate the investigated problem as mixed integer and linear programming. We propose a novel partial charging scheme based on fuzzy logic and genetic algorithms to determine which sensors should be charged in each cycle and optimize both charging paths and charging time simultaneously. A range of experimental simulations is conducted to demonstrate the effectiveness of our charging scheme. The simulation results show our proposed algorithm's effectiveness compared to the existing works concerning various performance metrics.

Inspired by the ability of human multitasking, there is a growing body of literature in the computational intelligence community dedicated to solving multiple problems concurrently. One of the research areas that have been receiving much attention in this topic is evolutionary multitasking, which is able to solve multiple complicated optimization problems together, yielding a better result than solving them in isolation. However, researches on evolutionary multitasking mostly focus on solving a small number of problems together. In an attempt to improve evolutionary multitasking, we propose Many-Task Multi-Armed Bandit Evolutionary Algorithm (Ma2BEA), including a new structure for the evolutionary multitasking algorithm. It also adopts a well-proven result of Multi-Armed Bandit (MAB) as the method to control the adaptive knowledge exchange between different tasks. In particular, the action of selecting which task to perform inter-task crossover is learned and decided by the designed MAB agent. We verify that Ma2BEA correctly learned the underlying relationship between tasks using the simple 10-task benchmarks. Besides, Ma2BEA is compared with other evolutionary many-tasking algorithms that have recently been proposed using the Single-Objective Many-task benchmark from the WCCI 2020 Competition on Evolutionary Multi-task Optimization. Empirical results show that Ma2BEA is competitive in terms of high solution quality and reasonable execution time.