Efficient Global Optimisation of Microwave Antennas Based on a Parallel Surrogate Model-assisted Evolutionary Algorithm

Computational efficiency is a major challenge for evolutionary algorithm (EA)-based antenna optimisation methods due to the computationally expensive electromagnetic simulations. Surrogate model-assisted EAs considerably improve the optimisation efficiency, but most of them are sequential methods, which cannot benefit from parallel simulation of multiple candidate designs for further speed improvement. To address this problem, a new method, called parallel surrogate model-assisted hybrid differential evolution for antenna optimisation (PSADEA), is proposed. The performance of PSADEA is demonstrated by a dielectric resonator antenna, a Yagi-Uda antenna, and three mathematical benchmark problems. Experimental results show high operational performance in a few hours using a normal desktop 4-core workstation. Comparisons show that PSADEA possesses significant advantages in efficiency compared to a state-of-the-art surrogate model-assisted EA for antenna optimisation, the standard parallel differential evolution algorithm, and parallel particle swarm optimisation. In addition, PSADEA also shows stronger optimisation ability compared to the above reference methods for challenging design cases

Self-Evaluation Applied Mathematics 2003-2008 University of Twente

This report contains the self-study for the research assessment of the Department of Applied Mathematics (AM) of the Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS) at the University of Twente (UT). The report provides the information for the Research Assessment Committee for Applied Mathematics, dealing with mathematical sciences at the three universities of technology in the Netherlands. It describes the state of affairs pertaining to the period 1 January 2003 to 31 December 2008

Cloud computing resource scheduling and a survey of its evolutionary approaches

A disruptive technology fundamentally transforming the way that computing services are delivered, cloud computing offers information and communication technology users a new dimension of convenience of resources, as services via the Internet. Because cloud provides a finite pool of virtualized on-demand resources, optimally scheduling them has become an essential and rewarding topic, where a trend of using Evolutionary Computation (EC) algorithms is emerging rapidly. Through analyzing the cloud computing architecture, this survey first presents taxonomy at two levels of scheduling cloud resources. It then paints a landscape of the scheduling problem and solutions. According to the taxonomy, a comprehensive survey of state-of-the-art approaches is presented systematically. Looking forward, challenges and potential future research directions are investigated and invited, including real-time scheduling, adaptive dynamic scheduling, large-scale scheduling, multiobjective scheduling, and distributed and parallel scheduling. At the dawn of Industry 4.0, cloud computing scheduling for cyber-physical integration with the presence of big data is also discussed. Research in this area is only in its infancy, but with the rapid fusion of information and data technology, more exciting and agenda-setting topics are likely to emerge on the horizon

A Partition-Based Random Search Method for Multimodal Optimization

Practical optimization problems are often too complex to be formulated exactly. Knowing multiple good alternatives can help decision-makers easily switch solutions when needed, such as when faced with unforeseen constraints. A multimodal optimization task aims to find multiple global optima as well as high-quality local optima of an optimization problem. Evolutionary algorithms with niching techniques are commonly used for such problems, where a rough estimate of the optima number is required to determine the population size. In this paper, a partition-based random search method is proposed, in which the entire feasible domain is partitioned into smaller and smaller subregions iteratively. Promising regions are partitioned faster than unpromising regions, thus, promising areas will be exploited earlier than unpromising areas. All promising areas are exploited in parallel, which allows multiple good solutions to be found in a single run. The proposed method does not require prior knowledge about the optima number and it is not sensitive to the distance parameter. By cooperating with local search to refine the obtained solutions, the proposed method demonstrates good performance in many benchmark functions with multiple global optima. In addition, in problems with numerous local optima, high-quality local optima are captured earlier than low-quality local optima

Performance Enhancement in Copper Twisted Pair Cable Communications

The thesis focuses on the area of copper twisted pair based wireline communications. As one of the most widely deployed communication media, the copper twisted pair cable plays an important role in the communication network cabling infrastructure. This thesis looks to exploit diversity to improve twisted pair channels for data communications in two common application areas, namely Ethernet over Twisted Paris and digital subscriber line over twisted pair based telephone network. The first part of the thesis addresses new approaches to next generation Ethernet over twisted pair cable. The coming challenge for Ethernet over twisted pair cable is to realise a higher data rate beyond the 25/40GBASE-T standard, in relatively short reach scenarios. The straight-forward approaches, such as improving cable quality and extending frequency bandwidth, are unlikely to provide significant improvement in terms of data rate. However, other system diversities, such as spectrum utilization are yet to be fully exploited, so as to meet the desired data rate performance. The current balanced transmission over the structured twisted pair cable and its parallel single-in-single-out channel model is revisited and formulated as a full-duplex multiple-in-multiple-out (MIMO) channel model. With a common ground (provided by the cable shield), the balanced transmission is converted into unbalanced transmission, by replacing the differential-mode excitation with single-ended excitation. In this way, MIMO adoption may offer spectrum utilization advantages due to the doubled number of the channels. The S-parameters of the proposed MIMO channel model is obtained through the full wave electromagnetic simulation of a short CAT7A cable. The channel models are constructed from the resulting S-parameters, also the corresponding theoretical capacity is evaluated by exploiting different diversity scenarios. With higher spectrum efficiency, the orthogonal-frequency-division-multiplexing (OFDM) modulation can significantly improve the theoretical capacity compared with single-carrier modulation, where the channel frequency selectivity is aided. The MIMO can further enhance the capacity by minimising the impact of the crosstalk. When the crosstalk is properly handled under the unbalanced transmission, this thesis shows that the theoretical capacity of the EoTP cable can reach nearly 200GBit/s. In order to further extend the bandwidth capability of twisted pair cables, Phantom Mode transmission is studied, aiming at creating more channels under balanced transmission operation. The second part of the thesis focuses on the research of advanced scheduling algorithms for VDSL2 QoS enhancement. For VDSL2 broadband access networks, multi-user optimisation techniques have been developed, so as to improve the basic data rate performance. Spectrum balancing improves the network performance by optimising users transmit power spectra as the resource allocation, to mitigate the impact from the crosstalk. Aiming at enhancing the performance for the upstream VDSL2 service, where the users QoS demand is not known by all other users, a set of autonomous spectrum balancing algorithms is proposed. These optimise users transmit power spectra locally with only direct channel state information. To prevent selfish behaviour, the concept of a virtual user is introduced to represent the impact on both crosstalk interference and queueing status of other users. Moreover, novel algorithms are developed to determine the parameters and the weight of the virtual user. Another type of resource allocation in the VDSL2 network is crosstalk cancellation by centralised signal coordination. The history of the data queue is considered as a time series, on which different smooth filter characteristics are investigated in order to investigate further performance improvement. The use of filter techniques accounts for both the instantaneous queue length and also the previous data to determine the most efficient dynamic resource allocation. With the help of this smoothed dynamic resource allocation, the network will benefit from both reduced signalling communication and improved delay performance.The proposed algorithms are verified by numerical experiments

Evolutionary computation for expensive optimization: a survey

Expensive optimization problem (EOP) widely exists in various significant real-world applications. However, EOP requires expensive or even unaffordable costs for evaluating candidate solutions, which is expensive for the algorithm to find a satisfactory solution. Moreover, due to the fast-growing application demands in the economy and society, such as the emergence of the smart cities, the internet of things, and the big data era, solving EOP more efficiently has become increasingly essential in various fields, which poses great challenges on the problem-solving ability of optimization approach for EOP. Among various optimization approaches, evolutionary computation (EC) is a promising global optimization tool widely used for solving EOP efficiently in the past decades. Given the fruitful advancements of EC for EOP, it is essential to review these advancements in order to synthesize and give previous research experiences and references to aid the development of relevant research fields and real-world applications. Motivated by this, this paper aims to provide a comprehensive survey to show why and how EC can solve EOP efficiently. For this aim, this paper firstly analyzes the total optimization cost of EC in solving EOP. Then, based on the analysis, three promising research directions are pointed out for solving EOP, which are problem approximation and substitution, algorithm design and enhancement, and parallel and distributed computation. Note that, to the best of our knowledge, this paper is the first that outlines the possible directions for efficiently solving EOP by analyzing the total expensive cost. Based on this, existing works are reviewed comprehensively via a taxonomy with four parts, including the above three research directions and the real-world application part. Moreover, some future research directions are also discussed in this paper. It is believed that such a survey can attract attention, encourage discussions, and stimulate new EC research ideas for solving EOP and related real-world applications more efficiently