752 publications from this institution
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 with the condition that its interference to the primary users (PUs) is 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 such a way that the mean square error (MSE) of the beam pattern between the designed and desired waveforms is minimized. The optimization problem has the following three constraints: i) the signal-to-interference-plus-noise ratio (SINR) at each data communication user is above a predetermined level; ii) the per-antenna transmit power is maintained at a given level; iii) the interference imposed on each PU is below a certain threshold. Both the semidefinite relaxation and nature-inspired firefly algorithms are proposed in order to search for the optimal solutions to the optimization problem. The simulation results indicate that our proposed algorithms can enable the DFRC system to protect the PUs while simultaneously performing its communication and radar functions.
Design optimization in engineering tends to be very challenging, partly due to the complexity and high nonlinearity of the problem of interest and partly due to stringent design codes in engineering. Conventional algorithms are not the best tools for highly nonlinear global optimization, as they are local search algorithms and thus often miss the global optimality. In addition, design solutions have to be robust and subject to uncertainty in the parameters and tolerance of available components and materials. Metaheuristic algorithms have become increasingly popular in the last two decades. This chapter reviews some of the latest metaheuristics.KeywordsAlgorithm, ant algorithm, bee algorithm, bat algorithm, cuckoo search, firefly algorithm, harmony search, particle swarm optimization, metaheuristics
There is a growing interest in discovering Internet path characteristics using end-to-end measurements. However, the current mechanisms for performing this task either send probe traffic, or require the sender to cooperate by time stamping the packets or sending them back-to-back. Furthermore, most of these techniques require the packets to carry sequence numbers to detect losses, and a few of them assume the existence of multicast. This paper introduces a completely passive approach for learning Internet path characteristics. In particular, we show that by noting the time difference between consecutive packets, a passive observer can cluster the flows into groups, such that all the flows in one group share the same bottleneck. Our approach relies on the observation that the correct clustering minimizes the entropy of the inter-packet spacing seen by the observer. It does not inject any probe traffic into the network, does not require any cooperation from the senders, and works with any type of traffic whether it is TCP, UDP, or even multicast.
The application of optimization and data mining in databases in geosciences is becoming promising, though still at an early stage. We present a case study of the application of data mining and optimization in the prediction of fractures using well-logging data. We compare various approaches, including multiple regression analysis (MRA), back-propagation neural network (BPNN), and support vector machine (SVM). The modelling problem in data mining is formulated as a minimization problem, showing that we can reduce an 8-D problem to a 4-D problem by dimension reduction. The MRA, BPNN and SVM methods are used as optimization techniques for knowledge discovery in data. The calculations for both the learning samples and prediction samples show that both BPNN and SVM can have zero residuals, which suggests that these combined data-mining techniques are practical and efficient.
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Many inverse problems in engineering can be considered as constrained optimisation, as the aim of inversion is to find the best parameter estimates so as to minimise the differences between the predicted results and the observations while satisfying all known constraints. Such optimisation problems can thus be solved by efficient optimisation techniques. As the number of degrees of freedom is usually very large, metaheuristic algorithms such as Cuckoo Search are particularly suitable for inverse problems, because metaheuristics are very efficient for solving non-linear global optimisation problems. In this paper, we will take a unified approach to inversion and optimisation and introduce a few nature-inspired metaheuristics, including genetic algorithms, differential evolution, firefly algorithm, Cuckoo Search, particle swarm optimisation and their applications in solving inverse problems.
No abstract is provided for this article.
No abstract is provided for this article.
Many optimization problems in engineering and industrial design applications can be formulated as optimization problems with highly nonlinear objectives, subject to multiple complex constraints.Solving such optimization problems requires sophisticated algorithms and optimization techniques.A major trend in recent years is the use of nature-inspired metaheustic algorithms (NIMA).Despite the popularity of nature-inspired metaheuristic algorithms, there are still some challenging issues and open problems to be resolved.Two main issues related to current NIMAs are: there are over 540 algorithms in the literature, and there is no unified framework to understand the search mechanisms of different algorithms.Therefore, this paper attempts to analyse some similarities and differences among different algorithms and then presents a generalized evolutionary metaheuristic (GEM) in an attempt to unify some of the existing algorithms.After a brief discussion of some insights into nature-inspired algorithms and some open problems, we propose a generalized evolutionary metaheuristic algorithm to unify more than 20 different algorithms so as to understand their main steps and search mechanisms.We then test the unified GEM using 15 test benchmarks to validate its performance.Finally, further research topics are briefly discussed.
SUMMARY A new metaheuristic optimization algorithm is developed to solve truss optimization problems. The new algorithm, called cuckoo search (CS), is examined by solving five truss design optimization problems with increasing numbers of design variables and complexity in constraints. The performance of the CS algorithm is further compared with various classical and advanced algorithms, selected from a wide range of the state‐of‐the‐art algorithms in the area. The results identify that the final solutions obtained by the CS are superior compared with the best solutions obtained by the other algorithms. Finally, the unique search features used in the CS and the implications for future researches are discussed in detail. Copyright © 2012 John Wiley & Sons, Ltd.
Networks have increased rapidly both in scale and speed. Problems related to the control and management are of increasing interest. The average throughput and end-to-end delay of a network flow are important design factors. However, there is no satisfactory tool to obtain such parameters. The traditional packet-by-packet event driven simulation is slow when the network speed is high. The time driven simulation faces the difficulty of choosing the right time interval when simulating packet-switched networks. As the Transmission Control Protocol (TCP) is the most widely used transport layer protocol, and it uses a window based flow control mechanism, classic queuing theories involving Markov chain assumptions are not applicable. This paper describes a model for window based flow control packet-switched networks. The model attempts to provide a way to obtain the steady state results for large and high speed networks using TCP. We discuss in detail the construction, implementation and application of the model. This paper also compares the results obtained from the model with those from the packet-by-packet event driven simulation. The comparison shows the model is correctly modeling the networks.
It is now five years since the launch of the International Journal of Bio-Inspired Computation (IJBIC). At the same time, significant new progress has been made in the area of bio-inspired computation. This review paper summarises the success and achievements of IJBIC in the past five years, and also highlights the challenges and key issues for further research.
