As demand for high-speed telecommunication increases, automating the design of fibre networks is critical for minimising deployment costs and ensuring consistent service. However, existing search strategies within British Telecom’s (BT) design software struggle to consistently locate near-global optima as network scale increases, and the hard-coded definition of network domains limits flexibility for non-developers. To address these challenges, this paper proposes a novel framework with two key contributions: a Prolog Advisory System (PAS) for modular domain definition and a sequence-based selection hyper-heuristic for enhanced optimisation. The PAS utilises logic programming to decouple constraint checking and cost calculation from the core framework, enabling rapid domain modification. For optimisation, we introduce intelligent move sequencing using Luby-based and Hidden Markov Model approaches, augmented by a continuous pivot strategy that increases the exploration of constraint-breaking moves. Experimental results on real-world case studies illustrate the robustness and effectiveness of our method to obtain high-quality solutions, regardless of the size of the network. Specifically, on large-scale instances, the proposed method outperforms BT’s current approach, reducing average design cost by approximately 3.8% and decreasing the required evaluations by up to 65%. This work offers vital efficiency gains for BT’s multi-billion pound fibre roll-out program, one of the UK’s largest infrastructure investments, where even small design improvements yield substantial financial savings.

The multi-objective multigraph Shortest Path Problem (SPP) is intractable, necessitating efficient solution approaches. To address general multi-objective multigraph SPPs, this article introduces a Multi-Objective Multi-Graph A* (MOMGA*) algorithm and develops a learning-based heuristic function to expedite the search. MOMGA* generalises the Airport Multi-Objective A* (AMOA*), which was designed for a specific application on multigraphs, and further modifies its path selection and expansion procedures. Theoretical analysis demonstrates that the modifications in MOMGA* yield advantages over AMOA*, including higher search efficiency, more effective use of admissible heuristics for accelerating search, and seamless integration with likely-admissible heuristics without sacrificing solution quality. The admissibility proof of MOMGA* is also provided. The developed heuristic function is likely-admissible. It embraces node embedding techniques to extract node characteristics, based on which shortest path costs (heuristics) for every two nodes are estimated through neural networks. In particular, we present an extensive review of walk-based shallow embedding methods and experimentally validate their superior ability in capturing the characteristics of nodes for accurately predicting heuristics. Evaluation based on randomly generated multi-objective multigraphs confirms: (i) MOMGA* comprehensively outperforms AMOA*, consistent with the theoretical analysis; (ii) walk-based sampling for node embeddings is key to preserving distance-related information in graphs; (iii) the proposed likely-admissible heuristics, even learnt with a limited amount of training data, can empower MOMGA* to efficiently obtain a collection of optimal and near-optimal solutions; and (iv) a good balance between optimality and tractability in MOMGA* is controllable by tuning the predictive accuracy of learning heuristics.

This paper investigates heuristic initialisation for shortest path search on multi-objective multigraphs using genetic algorithms. An initialised solution comprises a node path and an edge path. The state-of-the-art initialisation method encodes node paths into random-key node priority sequences and assigns random values to represent the indices of traversed parallel edges, where the node priority is a combination of hop count and a random value. However, this method has three limitations. (i) Hop counts are computed using Dijkstra’s algorithm, incurring non-negligible computational cost. (ii) The upper bound of the random values added to hop counts is defined by a hyperparameter, requiring tedious parameter tuning. (iii) Heuristic information about edges is ignored, despite its potential to improve solution quality. To address these issues, this paper first eliminates the randomisation hyperparameter and constrains randomness to the range [0, 1). Second, a heuristic edge initialisation method is proposed. Finally, to reduce the computational burden of Dijkstra’s algorithm, a learning-based node priority generation method is devised using neural networks trained on node embeddings. Experiments on benchmark multi-objective multigraphs show that (i) combining hop counts with randomness in [0, 1) and heuristic edge initialisation outperforms the state-of-the-art method, and (ii) with limited training data, the learning-based node priorities surpass entirely random node priorities and are comparable to state-of-the-art performance, highlighting their potential in large-scale and dynamic scenarios.

Due to the advancements in electric vehicles, a new variant of the Vehicle Routing Problem (VRP) has emerged into what is known as the Capacitated Electric Vehicle Routing Problem (CEVRP). CEVRP is characterised by longer charging time, restrictive cruising range, and challenges posed by the scarcity of charging stations. In response to the new characteristics and challenges brought by CEVRP, we introduce a Confidence-based Bilevel Memetic Algorithm (CBMA). Furthermore, an adaptive selection scheme is introduced, which can dynamically adjust the algorithm's convergence, i.e., accelerating convergence in the early stage while maintaining exploration capability as convergence slows down. Experimental results demonstrate that the proposed algorithm outperforms existing algorithms and breaks the records of six previously best-known solutions in the IEEE WCCI-2020 benchmark.

Road traffic accidents, primarily caused by driver-related issues such as stress, result in numerous fatalities and injuries. Effective prediction of driver stress within the driving phase is paramount for real-time accident intervention, while it requires prediction accuracy, stability, and enough predictive lead time. Physiological data contains rich information related to driver stress levels, which can be captured by machine learning models. However, those models are mainly developed for and perform well in static stress detection tasks, not addressing practical requirements of predictive lead time and performance stability. This study introduces a novel approach, the GP-CNN-LSTM model, Which employs a Convolutional Neural Network (CNN) with a Long Short-Term Memory (LSTM) network and Genetic Programming (GP), leverages GP to explore the feature space upon physiological sensor signals, and relies on CNN-LSTM to predict driver stress. Experiments show that this model achieves high accuracy for a 60-second forward stress prediction, with more stability compared to Fractional Fourier Transform-based benchmark models. We found explainable effective signals and features described by math functions via genetic programming that helped in improving the accuracy and stability of driver stress prediction.

Routing and scheduling problems with increasingly realistic modeling approaches often entail the consideration of multiple objectives, time constraints, and modeling the system as a multigraph. This detailed modeling approach has increased computational complexity and may also lead to violation of the additivity property of the costs. In the worst scenario, increased complexity makes the problem intractable for exact algorithms. Even when the problem is solvable, exact algorithms may not provide solutions within the given time budget, and the found solutions are not guaranteed to be optimal due to the additivity property violation. Approximate solution methods become more suitable in this case. This article focuses on one particular real-world application, the Airport Ground Movement Problem, where both time constraints and parallel arcs are involved. We introduce a novel memetic algorithm for routing in multigraphs with time constraints (MARMT) and present a comprehensive study of its different variants based on diverse genetic representation methods. We propose a local search operator that enhances search efficiency and effectiveness. MARMT is tested on real data based on two airports of different sizes. Our results show that MARMT does not suffer from the nonadditivity property problem as it outperforms the state-of-the-art exact algorithm when allowed to converge. When a time budget of 10 s is imposed on MARMT, it is able to provide solutions with quality comparable (within 1%–5% degradation) to the ones given by the exact algorithm with respect to the aggregated objective values. MARMT can be adapted for other applications, such as train operations.

Airports and their related operations have become the major bottlenecks to the entire air traffic management system, raising predictability, safety, and environmental concerns. One of the underpinning techniques for digital and sustainable air transport is airport ground movement optimization. Currently, real ground movement data is made freely available for the majority of aircraft at many airports. However, the recorded data is not accurate enough due to measurement errors and general uncertainties. In this article, we aim to develop a new interval type-2 fuzzy logic-based map matching algorithm, which can match each raw data point to the correct airport segment. To this aim, we first specifically design a set of interval type-2 Sugeno fuzzy rules and their associated rule weights, as well as the model output, based on preliminary experiments and sensitivity tests. Then, the fuzzy membership functions are fine-tuned by a particle swarm optimization algorithm. Moreover, an extra checking step using the available data is further integrated to improve map matching accuracy. Using the real-world aircraft movement data at Hong Kong airport, we compared the developed algorithm with other well known map matching algorithms. Experimental results show that the designed interval type-2 fuzzy rules have the potential to handle map matching uncertainties, and the extra checking step can effectively improve map matching accuracy. The proposed algorithm is demonstrated to be robust and achieve the best map matching accuracy of over 96% without compromising the run time.

In light of increased demand for streaming services, the need for more cost-effective network services is pressing. The telecommunication industry is facing tight budgets and severe competition. Therefore, reducing the cost of designing a fibre network via automation and optimisation has become critical. In order to automate and optimise network designs, British Telecom (BT) have developed a network design software, BT NetDesign, which includes a number of heuristics to search the design space of networks using a simulated annealing (SA) search strategy. Although NetDesign's current SA-based method is able to provide exploration and exploitation via different move heuristics, it cannot consistently reach the near-global optimum as the search space grows exponentially with the size of the network. To deal with larger networks, this study implements sequence-based hyper-heuristics utilising a hidden Markov model (HMM) with different acceptance strategies. The proposed methods have been rigorously analysed and compared using real-world network instances of different sizes. Results showed that HMM with a longer learning period and threshold acceptance strategy has promising ability to reach high quality solutions for large real-world problem instances.

Routing and scheduling of airport ground movements poses a critical issue for efficient surface operations. For real-world applications, multiple objectives should be considered, leading to a multigraph representation of the search space. Meanwhile, intermediate holding is often needed to a) release availability of scarce resources such as runways and gates for more cost-effective routing and scheduling, b) provide additional solutions in the speed profile database that can be used during routing and scheduling, and c) keep airport ground movements functional during disruptive events that may paralyse part of the taxiway network. This paper presents an extended multiobjective memetic algorithm upon the multigraph model to search for desirable solutions with intermediate holding. The performance of the proposed algorithm is examined with problem instances of different airport layouts. The results demonstrate prominent savings in both time and fuel costs compared with solutions without intermediate holding.

To solve general multi-objective multigraph shortest path problems, this paper proposes an algorithm (MOMGA*) that incorporates an online likely-admissible learning-based heuristic function to accelerate the solution-finding process. MOMGA* is an extended and generalised version of the airport multi-objective A* (AMOA*) algorithm that is tailored for a specific application problem. The online heuristic function is added and developed using artificial neural networks that estimate the costs between two nodes based on their metrics. To implement this metric-based prediction, a graph embedding technique is adopted to learn node feature representations. Results on a range of benchmark multi-objective multigraphs show that (i) in the absence of heuristic information, MOMGA* can deliver the same Pareto optimal solutions as AMOA* does, while requiring less computational time, and (ii) empowered by the likely-admissible learning- based heuristics, MOMGA* is able to provide a set of optimal and near-optimal solutions and strike a good balance between optimality and tractability.

Airspace complexity is a critical metric in current Air Traffic Management systems for indicating the security degree of airspace operations. Airspace complexity can be affected by many coupling factors in a complicated and nonlinear way, making it extremely difficult to be evaluated. In recent years, machine learning has been proved as a promising approach and achieved significant results in evaluating airspace complexity. However, existing machine learning based approaches require a large number of airspace operational data labeled by experts. Due to the high cost in labeling the operational data and the dynamical nature of the airspace operating environment, such data are often limited and may not be suitable for the changing airspace situation. In light of these, we propose a novel unsupervised learning approach for airspace complexity evaluation based on a deep neural network trained by unlabeled samples. We introduce a new loss function to better address the characteristics pertaining to airspace complexity data, including dimension coupling, category imbalance, and overlapped boundaries. Due to these characteristics, the generalization ability of existing unsupervised models is adversely impacted. The proposed approach is validated through extensive experiments based on the real-world data of six sectors in Southwestern China airspace. Experimental results show that our deep unsupervised model outperforms the state-of-the-art methods in terms of airspace complexity evaluation accuracy.

The threats and challenges of unmanned aerial vehicle (UAV) invasion defense due to rapid UAV development have attracted increased attention recently. One of the important UAV invasion defense methods is radar network detection. To form a tight and reliable radar surveillance network with limited resources, it is essential to investigate optimized radar network deployment. This optimization problem is difficult to solve due to its nonlinear features and strong coupling of multiple constraints. To address these issues, we propose an improved firefly algorithm that employs a neighborhood learning strategy with a feedback mechanism and chaotic local search by elite fireflies to obtain a trade-off between exploration and exploitation abilities. Moreover, a chaotic sequence is used to generate initial firefly positions to improve population diversity. Experiments have been conducted on 12 famous benchmark functions and in a classical radar deployment scenario. Results indicate that our approach achieves much better performance than the classical firefly algorithm (FA) and four recently proposed FA variants.

With increasing demand for air travel and overloaded airport facilities, inefficient airport taxiing operations are a significant contributor to unnecessary fuel burn and a substantial source of pollution. Although taxiing is only a small part of a flight, aircraft engines are not optimised for taxiing speed and so contribute disproportionately to the overall fuel burn. Delays in taxiing also waste scarce airport resources and frustrate passengers. Consequently, reducing the time spent taxiing is an important investment. An exact algorithm for finding shortest paths based on A* allocates routes to aircraft that maintains aircraft at a safe distance apart, has been shown to yield efficient taxi routes. However, this approach depends on the order in which aircraft are chosen for allocating routes. Finding the right order in which to allocate routes to the aircraft is a combinatorial optimization problem in itself.

Designing a robust classification mechanism with a higher accuracy for Electroencephalogram (EEG) signals is a challenging task. In this paper, a metaheuristic based multi-objective fuzzy modelling mechanism based on the One-Against-One (OAO) strategy has been developed for classification of multiclass steady state visual evoked potential (SSVEP) data. In this work, three different flickering frequencies in 10Hz, 14Hz and 21Hz were used to elicit the SSVEPs. The recorded EEG signals were segmented into 2.5-second long epochs and features were extracted using the Discrete Wavelet Transform (DWT) method. The proposed classification mechanism provides higher classification accuracy compared to baseline classification algorithms based on adaptive neuro fuzzy inference system (ANFIS) and artificial neural networks (ANNs), this is achieved by simultaneously improving two objectives: the prediction accuracy and interpretability of the fuzzy rule-based systems. The results highlight that by searching for both optimal parameters and structure of the classifier, the generalisation capability and interpretability are improved.

Piecewise Aggregate Approximation (PAA) provides a powerful yet computationally efficient tool for dimensionality reduction and Feature Extraction (FE) on large datasets compared to previously reported and well-used FE techniques, such as Principal Component Analysis (PCA). Nevertheless, performance can degrade as a result of either regional information insufficiency or over-segmentation, and because of this, additional relatively complex modifications have subsequently been reported, for instance, Adaptive Piecewise Constant Approximation (APCA). To recover some of the simplicity of the original PAA, whilst addressing the known problems, a distance-based Hierarchical Clustering (HC) technique is now proposed to adjust PAA segment frame sizes to focus segment density on information rich data regions. The efficacy of the resulting hybrid HC-PAA methodology is demonstrated using two application case studies viz. fault detection on industrial gas turbines and ultrasonic biometric face identification. Pattern recognition results show that the extracted features from the hybrid HC-PAA provide additional benefits with regard to both cluster separation and classification performance, compared to traditional PAA and APCA alternatives. The method is therefore demonstrated to provide a robust and readily implemented algorithm for rapid FE and identification for datasets.

This paper draws upon earlier work, which developed a multiobjective speed profile generation framework for unimpeded taxiing aircraft. Here, we deal with how to seamlessly integrate such efficient speed profiles into a holistic decision-making framework. The availability of a set of nondominated unimpeded speed profiles for each taxiway segment, with respect to conflicting objectives, has the potential to significantly impact upon airport ground movement research. More specifically, the routing and scheduling function that was previously based on distance, emphasizing time efficiency, could now be based on richer information embedded within speed profiles, such as the taxiing times along segments, the corresponding fuel consumption, and the associated economic implications. The economic implications are exploited over a day of operation, to take into account cost differences between busier and quieter times of the airport. Therefore, a more cost-effective and tailored decision can be made, respecting the environmental impact. Preliminary results based on the proposed approach show a 9%-50% reduction in time and fuel respectively for two international airports: Zurich and Manchester. The study also suggests that, if the average power setting during the acceleration phase could be lifted from the level suggested by the International Civil Aviation Organization, ground operations may simultaneously improve both time and fuel efficiency. The work described in this paper aims to open up the possibility to move away from the conventional distance-based routing and scheduling to a more comprehensive framework, capturing the multifaceted needs of all stakeholders involved in airport ground operations.

Among all airport operations, aircraft ground movement plays a key role in improving overall airport capacity as it links other airport operations. Moreover, ever-increasing air traffic, rising costs, and tighter environmental targets create pressure to minimize fuel burn on the ground. However, current routing functions envisioned in Advanced Surface Movement, Guidance and Control Systems almost exclusively consider the most time-efficient solution and apply a conservative separation to ensure conflict-free surface movement, sometimes with additional buffer times to absorb small deviations from the taxi times. Such an overly constrained routing approach may result in either a too tight planning for some aircraft so that fuel efficiency is compromised due to multiple acceleration phases, or performance could be further improved by reducing the separation and buffer times. In light of this, Parts I and II of this paper present a new Active Routing (AR) framework with the aim of providing a more realistic, cost-effective, and environmental friendly surface movement, targeting some of the busiest international hub airports. Part I of this paper focuses on optimal speed profile generation using a physics-based aircraft movement model. Two approaches based, respectively, on the Base of Aircraft Data and the International Civil Aviation Organization engine emissions database have been employed to model fuel consumption. These models are then embedded within a multiobjective optimization framework to capture the essence of different speed profiles in a Pareto optimal sense. The proposed approach represents the first attempt to systematically address speed profiles with competing objectives. Results reveal an apparent tradeoff between fuel burn and taxi times irrespective of fuel consumption modeling approaches. This will have a profound impact on the routing and scheduling and open the door for the new concept of AR discussed in Part II of this paper.

Ever increasing air traffic, rising costs and tighter environmental targets create a pressure for efficient airport ground movement. Ground movement links other airport operations such as departure sequencing, arrival sequencing and gate/stand allocation and its operation can affect each of these. Previously, reducing taxi time was considered the main objective of the ground movement problem. However, this may conflict with efforts of airlines to minimise their fuel consumption as shorter taxi time may require higher speed and acceleration during taxiing. Therefore, in this paper a multi-objective multi-component optimisation problem is formulated which combines two components: scheduling and routing of aircraft and speed profile optimisation. To solve this problem an integrated solution method is adopted to more accurately investigate the trade-off between the total taxi time and fuel consumption. The new heuristic which is proposed here uses observations about the characteristics of the optimised speed profiles in order to greatly improve the speed of the graph-based routing and scheduling algorithm. Current results, using real airport data, confirm that this approach can find better solutions faster, making it very promising for application within on-line applications.

The paper presents a new system identification methodology for industrial systems. Using the original Mamdani fuzzy rule based system (FRBS), an adaptive Mamdani fuzzy modeling (AMFM) is introduced in this paper. It differs from the original Mamdani FRBS in that it applies different membership functions and a denazification mechanism that is `differentiable' with respect to the membership function parameters. The proposed system also includes a back error propagation (BEP) algorithm that is used to refine the fuzzy model. The efficacy of the proposed AMFM approach is demonstrated through the experimental trails from a compressor in an industrial gas turbine system.

The primary objective of this paper is to introduce Fuzzy Rule-Based Systems (FRBSs) as a relatively new technology into airport transportation research, with a special emphasis on ground movement operations. Hence, a Mamdani FRBS with the capability to learn from data has been adopted for taxi time estimations at Zurich Airport (ZRH). Linear regression is currently the dominating technique for such an estimation task due to its established nature, proven mathematical characteristics and straightforward explanatory ability. In this study, we demonstrate that FRBSs, although having a more complex structure, can offer more accurate estimations due to their proven properties as nonlinear universal approximators. Furthermore, such improvements in accuracy do not come at the cost of the model's interpretability. FRBSs can offer more explanations of the underlying behavior in different regions. Preliminary results on data for ZRH suggest that FRBSs are a valuable alternative to already established linear regression methods. FRBSs have great potential to be further seamlessly integrated into the taxiway routing and scheduling process due to the fact that more information is now available in the explanatory variable space.

In this paper, an immune inspired multi-objective fuzzy modeling (IMOFM) mechanism is proposed specifically for high-dimensional regression problems. For such problems, high predictive accuracy is often the paramount requirement. With such a requirement in mind, however, one should also put considerable efforts in making the elicited model as interpretable as possible, which leads to a difficult optimization problem. The proposed modeling approach adopts a multistage modeling procedure and a variable length coding scheme to account for the enlarged search space due to the simultaneous optimization of the rule-base structure and its associated parameters. IMOFM can account for both Singleton and Mamdani Fuzzy Rule-Based Systems (FRBS) due to the carefully chosen output membership functions, the inference and the defuzzification methods. The proposed algorithm has been compared with other representatives using a simple benchmark problem, and has also been applied to a high-dimensional problem which models mechanical properties of hot rolled steels. Results confirm that IMOFM can elicit accurate and yet transparent FRBSs from quantitative data.

In this paper, a systematic multi-objective Mamdani fuzzy modeling approach is proposed, which can be viewed as an extended version of the previously proposed Singleton fuzzy modeling paradigm. A set of new back-error propagation (BEP) updating formulas are derived so that they can replace the old set developed in the singleton version. With the substitution, the extension to the multi-objective Mamdani fuzzy rule-based systems (FRBS) is almost endemic. Due to the carefully chosen output membership functions, the inference and the defuzzification methods, a closed form integral can be deducted for the defuzzification method, which ensures the efficiency of the developed Mamdani FRBS. Some important factors, such as the variable length coding scheme and the rule alignment, are also discussed. Experimental results for a real data set from the steel industry suggest that the proposed approach is capable of eliciting not only accurate but also transparent FRBS with good generalization ability.