This paper introduces a dual-function radar-communication (DFRC) system with cognitive radio capability to tackle the spectral scarcity problem in wireless communications. Particularly, a cognitive DFRC system operates on a spectrum owned by a primary system to simultaneously perform data communication and target tracking while maintaining its interference to the primary users (PUs) below a certain threshold. To achieve this, an optimization problem is formulated to jointly design the beamforming vectors for both the radar and communication functions in minimizing the mean square error (MSE) of the beam patterns between the designed and desired waveforms under three constraints: i) the signal-to-interference-plus-noise ratio (SINR) at each data communication user; ii) the perantenna transmit power; and iii) the interference imposed on each PU. The semidefinite relaxation technique is utilized to search for the optimal solution to the optimization problem. The simulation results indicate that our proposed cognitive DFRC approach can effectively protect the PUs while simultaneously perform its communication and radar functions.

This paper proposes a generalized Firefly Algorithm (FA) to solve an optimization framework having objective function and constraints as multivariate functions of independent optimization variables. Four representative examples of how the proposed generalized FA can be adopted to solve downlink beamforming problems are shown for a classic transmit beamforming, cognitive beamforming, reconfigurable-intelligent-surfaces-aided (RIS-aided) transmit beamforming, and RIS-aided wireless power transfer (WPT). Complexity analyzes indicate that in large-antenna regimes the proposed FA approaches require less computational complexity than their corresponding interior point methods (IPMs) do, yet demand a higher complexity than the iterative and the successive convex approximation (SCA) approaches do. Simulation results reveal that the proposed FA attains the same global optimal solution as that of the IPM for an optimization problem in cognitive beamforming. On the other hand, the proposed FA approaches outperform the iterative, IPM and SCA in terms of obtaining better solution for optimization problems, respectively, for a classic transmit beamforming, RIS-aided transmit beamforming and RIS-aided WPT.

This paper studies a non-convex power minimization problem for reconfigurable-intelligent-surfaces-aided communication systems whose constraints are multivariate functions of two independent optimization variables, i.e., active and passive beamforming vectors. A widely adopted alternative optimization (AO) approach approximates the originally non-convex problem by two convex sub-optimization problems where each sub-optimization problem deals with one variable considering the other variable as a constant. The solution for the original problem is obtained by iteratively solving these sub-optimization problems. Although the AO approach converts the original NP-hard optimization problem to two convex sub-problems, the solutions attained by this method may not be the global optimal solution due to the approximation process as well as the inherent non-convexity of the original problem. To overcome the issue, this paper adopts a nature-inspired optimization approach and introduces a novel Firefly algorithm (FA) to simultaneously solve for two independent optimization variables of the originally non-convex optimization problem. Computational complexity analyses are provided for the proposed FA and the AO approaches. Simulation results reveal that the proposed FA approach prevails its AO counterpart in obtaining a better solution for the understudied optimization problem with the same order of computational complexity.

The flower pollination algorithm (FPA) is a highly efficient metaheuristic optimization algorithm that is inspired by the pollination process of flowering species. FPA is characterised by simplicity in its formulation and high computational performance. Previous studies on FPA assume fixed parameter values based on empirical observations or experimental comparisons of limited scale and scope. In this study, a comprehensive effort is made to identify appropriate values of the FPA parameters that maximize its computational performance. To serve this goal, a simple non-iterative, single-stage sampling tuning method is employed, oriented towards practical applications of FPA. The tuning method is applied to the set of 28 functions specified in IEEE-CEC'13 for real-parameter single-objective optimization problems. It is found that the optimal FPA parameters depend significantly on the objective functions, the problem dimensions and affordable computational cost. Furthermore, it is found that the FPA parameters that minimize mean prediction errors do not always offer the most robust predictions. At the end of this study, recommendations are made for setting the optimal FPA parameters as a function of problem dimensions and affordable computational cost.

Key update and residence management have been investigated as an effective solution to cope with desynchronization attacks in mobility management entity (MME) handovers. In this paper, we first analyze the impacts of the key update interval (KUI) and MME residence interval (MRI) on handover processes and their secrecy performance in terms of the number of exposed packets (NEP), signaling overhead rate (SOR), and outage probability of vulnerability (OPV). Specifically, the bounds of the derived NEP and SOR not only capture their behaviors at the boundary of the KUI and MRI, but also show the tradeoff between the NEP and SOR. Additionally, through the analysis of the OPV, it is shown that the handover security can be enhanced by shortening the KUI and the desynchronization attacks can be avoided with high-mobility users. The above facts accordingly motivate us to propose a multi-objective optimization (MO) problem to find the optimal KUI and MRI that minimize both the NEP and SOR subject to the constraint on the OPV. To this end, two scalarization techniques are adapted to transform the proposed MO problem into single-objective optimization problems, i.e., an achievement-function method via fractional programming (FP) and a weighted-sum method. Based on the derived bounds on NEP and SOR, the FP approach can be optimally solved via a simple numerical method. For the weighted-sum method, the firefly algorithm (FA) is utilized to find the optimal solution. The results show that both techniques can solve the proposed MO problem with a significantly reduced searching complexity compared to the conventional heuristic iterative search technique.

We study a situation where a swarm of robots is deployed to handle multiple different targets in a confined unknown area. The targets are found in real time and each target requires a certain amount of resources. An individual robot may not have sufficient capabilities for its execution, therefore an announcement process can start. We address the issue of how each robot responds itself to one of the discovered targets in an efficient way, considering a dynamic scenario where failure of the robots and unreliable communications unpredictably can occur. We propose a network architecture that incorporates a self-regulating mechanism allowing the distribution among the targets, with the minimal exchange of information. We have conducted experiments for evaluating our proposed approach in a simulated environment, considering different network parameters and studying the scalability and the robustness of the proposed model.

In LTE networks, key update and residence management have been investigated as an effective solution to cope with desynchronization attacks in mobility management entity (MME) handovers. In this paper, we first analyse the impacts of the key update interval (KUI) and MME residence interval (MRI) on the handover performance in terms of the number of exposed packets (NEP) and signaling overhead rate (SOR). By deriving the bounds of the NEP and SOR over the KUI and MRI, it is shown that there exists a tradeoff between the NEP and the SOR, while our aim is to minimise both of them simultaneously. This accordingly motivates us to propose a multiobjective optimisation problem to find the optimal KUI and MRI that minimise both the NEP and SOR. By introducing a relative importance factor between the SOR and NEP along with their derived bounds, we further transform the proposed optimisation problem into a single-objective optimisation problem which can be solved via a simple numerical method. In particular, the results show that a higher accuracy of up to 1 second is achieved with the proposed approach while requiring a lower complexity compared to the conventional approach employing iterative searches.

Among the multiple advantages and applications of remote sensing, one of the most important uses is to solve the problem of crop classification, i.e., differentiating between various crop types. Satellite images are a reliable source for investigating the temporal changes in crop cultivated areas. In this letter, we propose a novel bat algorithm (BA)-based clustering approach for solving crop type classification problems using a multispectral satellite image. The proposed partitional clustering algorithm is used to extract information in the form of optimal cluster centers from training samples. The extracted cluster centers are then validated on test samples. A real-time multispectral satellite image and one benchmark data set from the University of California, Irvine (UCI) repository are used to demonstrate the robustness of the proposed algorithm. The performance of the BA is compared with two other nature-inspired metaheuristic techniques, namely, genetic algorithm and particle swarm optimization. The performance is also compared with the existing hybrid approach such as the BA with K-means. From the results obtained, it can be concluded that the BA can be successfully applied to solve crop type classification problems.

Abstract The strong need and global interests for green communications necessitate the maximum reduction of energy consumption. Hence, one of the major challenges is to optimize the resource allocation and to improve the energy efficiency in an orthogonal frequency division multiple access system. In this paper, we analyze the energy‐efficient resource allocation problem mathematically. Our analytical results show that the optimal resource allocation can be achieved by primarily assigning the user with either the best channel gain or the least optimal power. Furthermore, our detailed case studies show that such analytical results are consistent with the best result obtained from the exhaustive search method. This means that the proposed approach can provide quick and better resource allocation. It can be expected that real‐timein situoptimal resource allocation can be achieved by extending the current methodology. Copyright © 2014 John Wiley & Sons, Ltd.

This paper introduces a modified bat algorithm using quaternion representation of individuals. Quaternions are a number system, which extends complex numbers. They are successfully applied to problems of theoretical physics and to those areas needing fast rotation calculations. We propose the application of quaternions in optimization, more precisely, we have been using quaternions for representation of individuals in bat algorithm belonging to a swarm intelligence family. It is expected that the problems with stagnation in swarm intelligence should be reduced or even eliminated. We believe that this representation could successfully be applied also to the other swarm intelligence and evolutionary algorithms.

Cuckoo search is a swarm-intelligence-based algorithm that is very effective for solving highly nonlinear optimization problems. In this paper, the multiobjective cuckoo search is extended so as to obtain high-quality Pareto fronts more accurately for multiobjective optimization problems with complex constraints. The proposed approach uses a combination of the cuckoo search with non-dominated sorting and archiving techniques. The performance of the proposed approach is validated by seven test problems. The convergence property and diversity as well as uniformity are compared with those of the NSGA-II. The results show that the proposed approach can find Pareto fronts with better uniformity and quicker convergence.

The increasing demand of high‐speed and secure wireless broadband networks has generated significant interests in the optimisation and the analysis of energy‐efficiency in orthogonal frequency division multiple access (OFDMA) system. In this study, the authors present an in‐depth mathematical analysis of the maximisation of energy‐efficiency by taking into consideration the quality of service (QoS). By using optimality conditions, they have shown that the optimal solutions can be obtained analytically. Furthermore, it has been proved in this study that this optimisation problem is strictly concave with the existence of a global maximum. Case studies with multiple subchannels validate the consistency of numerical results with the results obtained from derivative‐free method like genetic algorithm. Graphical illustrations also validate and confirm the numerical values obtained from the mathematical analysis. Therefore the solution is optimal with respect to the OFDMA model adopted in this study. The authors proposed approach can be used for practical applications because of its simplicity and efficacy with QoS guaranteed for efficient energy consumption.

Hybrid flowshop scheduling problems include the generalization of flowshops with parallel machines in some stages. Hybrid flowshop scheduling problems are known to be NP-hard. Hence, researchers have proposed many heuristics and metaheuristic algorithms to tackle such challenging tasks. In this letter, a recently developed discrete firefly algorithm is extended to solve hybrid flowshop scheduling problems with two objectives. Makespan and mean flow time are the objective functions considered. Computational experiments are carried out to evaluate the performance of the proposed algorithm. The results show that the proposed algorithm outperforms many other metaheuristics in the literature.

Although hyperspectral images acquired by on-board satellites provide information from a wide range of wavelengths in the spectrum, the obtained information is usually highly correlated. This paper proposes a novel framework to reduce the computation cost for large amounts of data based on the efficiency of the optimum-path forest (OPF) classifier and the power of metaheuristic algorithms to solve combinatorial optimizations. Simulations on two public data sets have shown that the proposed framework can indeed improve the effectiveness of the OPF and considerably reduce data storage costs.

Four schemes for binary solutions are proposed in this work to solve the resource allocation problem in an OFDMA system. The binary solution is sought at the early stage before applying the Genetic Algorithm to solve the real-valued problem. Each scheme performed differently in terms of Energy Efficient (EE) values obtained and the required computational cost. Out of four schemes (maxJ, maxP, minP and CP), the best scheme found in this study is CP as this scheme obtained the highest average EE with moderate computational cost.

Feature selection has been actively pursued in the last years, since to find the most discriminative set of features can enhance the recognition rates and also to make feature extraction faster. In this paper, the propose a new feature selection called Binary Cuckoo Search, which is based on the behavior of cuckoo birds. The experiments were carried out in the context of theft detection in power distribution systems in two datasets obtained from a Brazilian electrical power company, and have demonstrated the robustness of the proposed technique against with several others nature-inspired optimization techniques.