Search

The human inner perception is the core technology for human‐robot interactions. Emotion recognition has been established as a representative study for recognising the internal state of human beings in addition to research regarding intention recognition. In the study of emotion recognition using EEG signals, emotion evaluation is categorised into two methods: discrete emotion and continuous emotion. This study proposes an ant colony optimisation‐bidirectional LSTM network model. Unlike other LSTM network models, this model improves performance by applying more weight values that are valid for emotion recognition in the current LSTM cell state using past and future biosignal information and combining ACO to find the optimal combination of emotion recognition features among many features. Furthermore, it extracts valid features using peripheral nervous system signals PPG, GSR, and EOG as well as the central nervous system signals EEG, simultaneously. The authors reinforce feature performance by adding brain lateralisation for emotion recognition. Emotion label data were recorded by performing annotation labelling, in which the values are between −100 and 100 in the arousal‐valence domain. The experimental result shows that the ACO‐bidirectional LSTM model using brain lateralisation in the MAHNOB‐HCI, DEAP, MERTI‐Apps database yields the best valence performance, RMSE of 0.0442, 0.0523, and 0.0568, respectively.

The usage of dedicated short‐range communication, vehicular ad hoc network (VANET) significantly increases the security of stakeholders and improves the efficacy of the intelligent transportation system. It supports multifarious applications to make transportation convenient through routing of the messages. Changing the topological scenario, high acceleration of vehicles and intermittent communication result in the reduction of successful packet transmission and overhead. To overcome such constraints, a new routing protocol for the city environment named ant colony optimisation and intersection‐based routing (ACO–IBR) is proposed. The key objective is to identify the necessity of intersection oriented routing and selection of the route as a task of optimisation. It uses a new metric called intersection rating and a greedy approach to forward a packet through the intersection to minimise the time required to discover the route. The efforts have been made to calculate intersection rating using three parameters such as the distance between vehicles, travel direction and the time taken to reach the intersection. The performance of ACO–IBR is determined by overhead, packet delivery ratio, delay and throughput. The results are compared with the standard protocols such as greedy perimeter stateless routing, intersection‐based routing, AntHocNet and zone routing protocol. On the basis of extensive simulation, experimental results show that the ACO–IBR minimises routing overhead and improves the packet delivery ratio.

The increasing demand for work-ready students has heightened the need for universities to provide work integrated learning programs to enhance and reinforce students’ learning experiences. Students benefit most when placements meet their academic requirements and graduate aspirations. Businesses and community partners are more engaged when they are allocated students that meet their industry requirements. In this paper, both an integer programming model and an ant colony optimisation heuristic are proposed, with the aim of automating the allocation of students to industry placements. The emphasis is on maximising student engagement and industry partner satisfaction. As part of the objectives, these methods incorporate diversity in industry sectors for students undertaking multiple placements, gender equity across placement providers, and the provision for partners to rank student selections. The experimental analysis is in two parts: (a) we investigate how the integer programming model performs against manual allocations and (b) the scalability of the IP model is examined. The results show that the IP model easily outperforms the previous manual allocations. Additionally, an artificial dataset is generated which has similar properties to the original data but also includes greater numbers of students and placements to test the scalability of the algorithms. The results show that integer programming is the best option for problem instances consisting of less than 3000 students. When the problem becomes larger, significantly increasing the time required for an IP solution, ant colony optimisation provides a useful alternative as it is always able to find good feasible solutions within short time-frames.

This paper presents the parameter optimisation of the flight control system of a singlerotor medium-scale rotorcraft. The six degrees-of-freedom (DOF) nonlinear mathematical model of the rotorcraft is developed. This model is then used to develop proportional–integral–derivative (PID)-based controllers. Since the majority of PID controllers installed in industry are poorly tuned, this paper presents a comparison of the optimised tuning of the flight controller parameters using particle swarm optimisation (PSO), genetic algorithm (GA), ant colony optimisation (ACO) and cuckoo search (CS) optimisation algorithms. The aim is to find the best PID parameters that minimise the specified objective function. Two trim conditions are investigated, i.e., hover and 10 m/s forward flight. The four algorithms performed better than manual tuning of the PID controllers. It was found, through numerical simulation, that the ACO algorithm converges the fastest and finds the best gains for the selected objective function in hover trim conditions. However, for 10 m/s forward flight trim, the GA algorithm was found to be the best. Both the tuned flight controllers managed to reject a gust wind of up to 5 m/s in the lateral axis in hover and in forward flight.

Production and maintenance tasks apply for access to the same resources. Maintenance-related machine downtime reduces productivity, but the costs incurred due to unplanned machine failures often outweigh the costs associated with predictive maintenance. Costs incurred due to unplanned machine failure include corrective maintenance, reworks, delays in deliveries, breaks in the work of employees and machines. Therefore, scheduling of production and maintenance tasks should be considered jointly. The problem of generating a predictive schedule with given constrains is considered. The objective of the paper is to develop a scheduling method that reflects the operation of the production system and nature of disturbances. The original value of the paper is the development of the method of a basic schedule generation with the application of the Ant Colony Optimisation. A predictive schedule is built by planning the technical inspection of the machine at time of the predicted failure-free time. The numerical simulations are performed for job/flow shop systems.

Matheuristics have been gaining in popularity for solving combinatorial optimisation problems in recent years. This new class of hybrid method combines elements of both mathematical programming for intensification and metaheuristic searches for diversification. A recent approach in this direction has been to build a neighbourhood for integer programs by merging information from several heuristic solutions, namely construct, solve, merge and adapt (CMSA). In this study, we investigate this method alongside a closely related novel approach&mdash, merge search (MS). Both methods rely on a population of solutions, and for the purposes of this study, we examine two options: (a) a constructive heuristic and (b) ant colony optimisation (ACO), that is, a method based on learning. These methods are also implemented in a parallel framework using multi-core shared memory, which leads to improving the overall efficiency. Using a resource constrained job scheduling problem as a test case, different aspects of the algorithms are investigated. We find that both methods, using ACO, are competitive with current state-of-the-art methods, outperforming them for a range of problems. Regarding MS and CMSA, the former seems more effective on medium-sized problems, whereas the latter performs better on large problems.

Ant Colony Optimisation (ACO) algorithms emulate the foraging behaviour of ants to solve optimisation problems. They have proven effective in both academic and industrial settings. ACO algorithms share many features among them. Isula encapsulates these commonalities and exposes them for reuse in the form of a Java library. In this paper, we use the travelling salesman problem and image segmentation to showcase the framework capabilities using three top-performing ACO algorithms implemented in Isula. This framework is an open-source project available at GitHub, where is currently the most popular ACO java repository.

In social network analysis, community detection is one of the significant tasks to study the structure and characteristics of the networks. In recent years, several intelligent and meta-heuristic algorithms have been presented for community detection in complex social networks, among them label propagation algorithm (LPA) is one of the fastest algorithms for discovering community structures. However, due to the randomness of the LPA, its performance is not suitable for the general purpose of network analysis. In this study, the authors propose an improved version of the label propagation (called AntLP) algorithm using similarity indices and ant colony optimisation (ACO). The AntLP consists of two steps: in the first step, the algorithm assigns weights for edges of the input network using several similarity indices, and in the second step, the AntLP using ACO tries to propagate labels and optimise modularity measure by grouping similar vertices in each community based on the local similarities among the vertices of the network. In order to study the performance of the AntLP, several experiments are conducted on some well-known social network datasets. Experimental simulations demonstrated that the AntLP is better than some community detection algorithms for social networks in terms of modularity, normalised mutual information and running time.

To solve the problem of mutual superposition and interference of multi-target micro-Doppler in midcourse, a method based on modified ant colony optimisation (ACO) is proposed. On the basis of building 3D time–frequency map, pheromone on each spot in the workspace can be updated, according to the changing rule of micro-Doppler. The pheromone leads ant to seek for required optimal micro-Doppler path gradually; thus single micro-Doppler curve corresponding to each scattering point can be separated. Simulation results indicate that the proposed method can overcome the coupling between micro-Doppler of different scattering point and the influence of background noise better, and implement effective separation of multi-target.

A method is presented for the design of cellular automata rules by means of ant algorithms. In particular, Elitist Ant System and a~modified MAX-MIN Ant System are applied to search for transition functions of 1D cellular automata that are able to calculate squares of given input values. It will be shown that the proposed MAX-MIN Ant System can perform significantly better than the standard variant of Elitist Ant System. In particular, in the most advanced case study, the ant algorithm showed an ability to design a~complete set of elementary cellular automata rules that fulfil the required square calculations. Some selected results will be presented and their features discussed.

Coverage path planning on a complex free-form surface is a representative problem that has been steadily investigated in path planning and automatic control. However, most methods do not consider many optimisation conditions and cannot deal with complex surfaces, closed surfaces, and the intersection of multiple surfaces. In this study, a novel and efficient coverage path-planning method is proposed that considers trajectory optimisation information and uses point cloud data for environmental modelling. First, the point cloud data are denoised and simplified. Then, the path points are converted into the rotation angle of each joint of the manipulator. A mathematical model dedicated to energy consumption, processing time, and path smoothness as optimisation objectives is developed, and an improved ant colony algorithm is used to solve this problem. Two measures are proposed to prevent the algorithm from being trapped in a local optimum, thereby improving the global search ability of the algorithm. The standard test results indicate that the improved algorithm performs better than the ant colony algorithm and the max–min ant system. The numerical simulation results reveal that compared with the point cloud slicing technique, the proposed method can obtain a more efficient path. The laser ablation de-rusting experiment results specify the utility of the proposed approach.

Data clustering is a data mining technique that discovers hidden patterns by creating groups (clusters) of objects. Each object in every cluster exhibits sufficient similarity to its neighbourhood, whereas objects with insufficient similarity are found in other clusters. Data clustering techniques minimise intra-cluster similarity in each cluster and maximise inter-cluster dissimilarity amongst different clusters. Ant colony optimisation for clustering (ACOC) is a swarm algorithm inspired by the foraging behaviour of ants. This algorithm minimises deterministic imperfections in which clustering is considered an optimisation problem. However, ACOC suffers from high diversification in which the algorithm cannot search for best solutions in the local neighbourhood. To improve the ACOC, this study proposes a modified ACOC, called M-ACOC, which has a modification rate parameter that controls the convergence of the algorithm. Comparison of the performance of several common clustering algorithms using real-world datasets shows that the accuracy results of the proposed algorithm surpasses other algorithms.

Wireless Sensor Networks (WSNs) are a particular type of distributed self-managed network with limited energy supply and communication ability. The most significant challenge of a routing protocol is the energy consumption and the extension of the network lifetime. Many energy-efficient routing algorithms were inspired by the development of Ant Colony Optimisation (ACO). However, due to the inborn defects, ACO-based routing algorithms have a slow convergence behaviour and are prone to premature, stagnation phenomenon, which hinders further route discovery, especially in a large-scale network. This paper proposes a hybrid routing algorithm by combining the Artificial Fish Swarm Algorithm (AFSA) and ACO to address these issues. We utilise AFSA to perform the initial route discovery in order to find feasible routes quickly. In the route discovery algorithm, we present a hybrid algorithm by combining the crowd factor in AFSA and the pseudo-random route select strategy in ACO. Furthermore, this paper presents an improved pheromone update method by considering energy levels and path length. Simulation results demonstrate that the proposed algorithm avoids the routing algorithm falling into local optimisation and stagnation, whilst speeding up the routing convergence, which is more prominent in a large-scale network. Furthermore, simulation evaluation reports that the proposed algorithm exhibits a significant improvement in terms of network lifetime.

The optimal design of sewer networks typically comprises two sub-problems. The first is to determine an optimal layout of the network elements, and the second to optimally design the network components. In this article the focus is on the optimisation of gravity sewer network layouts, which requires simultaneous optimisation of hydraulic design. The layout is optimised using ant colony algorithms with four proposed node and edge-based selection strategies, while a heuristic optimisation algorithm is used for the hydraulic optimisation. The resulting simultaneous optimisation algorithm is shown to perform very well. The selection strategies are shown to be effective, but no clear best strategy is identified, as the performance of the layout algorithms is shown to depend heavily on characteristics of the network under consideration. However, some strategies are shown to perform inconsistently and worse than others on average.

The optimisation problems related to the assignment of tasks to workstations in assembly and disassembly lines have been largely discussed in the literature. They are known, respectively, as Assembly Line Balancing and Disassembly Line Balancing Problems. In this study, both types of task performed on the identical product are integrated in a common hybrid production system. Therefore, the logistic process is simplified and disassembly tasks can supply easier the assembly tasks with the required components. The considered production system has the layout of two parallel lines with common workstations. The product flow is conventional in the assembly line and reverse in the disassembly line. The paper provides a new mathematical model for designing such a hybrid system and an approximate approach based on ant colony optimisation for solving large-scale instances. The solution method is tested in a case study. The obtained results are compared with the solution provided by the design of two independent lines. The analysis of the results highlights the potential benefits of the hybrid production system.

Ant Colony Optimisation for classification has mostly been limited to rule based approaches where artificial ants walk on datasets in order to extract rules from the trends in the data, and hybrid approaches which attempt to boost the performance of existing classifiers through guided feature reductions or parameter optimisations. A recent notable example that is distinct from the mainstream approaches is PolyACO, which is a proof of concept polygon-based classifier that resorts to ant colony optimisation as a technique to create multi-edged polygons as class separators. Despite possessing some promise, PolyACO has some significant limitations, most notably, the fact of supporting classification of only two classes, including two features per class.This paper introduces PolyACO+, which is an extension of PolyACO in three significant ways: (1) PolyACO+ supports classifying multiple classes, (2) PolyACO+ supports polygons in multiple dimensions enabling classification with more than two features, and (3) PolyACO+ substantially reduces the training time compared to PolyACO by using the concept of multi-leveling. This paper empirically demonstrates that these updates improve the algorithm to such a degree that it becomes comparable to state-of-the-art techniques such as SVM, Neural Networks, and AntMiner+. nivå1

The real-life construction of examination timetabling problem is considered as a common problem that always encountered and experienced in educational institution whether in school, college, and university. This problem is usually experienced by the academic management department where they have trouble to handle complexity for assign examination into a suitable timeslot manually. In this paper, an algorithm approach of ant colony optimisation (ACO) is presented to find an effective solution for dealing with Universiti Sultan Zainal Abidin (UniSZA) examination timetabling problems. A combination of heuristic with ACO algorithm contributes the development solution in order to simplify and optimize the pheromone occurrence of matrix updates which include the constraints problem. The implementation of real dataset instances from academic management is applied to the approach for generating the result of examination timetable. The result and performance that obtained will be used for further use to evaluate the quality and observe the solution whether our examination timetabling system is reliable and efficient than the manual management that can deal the constraints problem.

In this article we propose a formalisation of the concept of exploration performed by metaheuristics. In particular, we define and test a method for studying this aspect regardless of the specific approach implemented. Understanding the behaviour of metaheuristics is important for being able to boost their results. Measuring the exploration performed may help increase this understanding. We propose an experimental analysis to show how the measure of exploration defined may be used to this aim. We quantify the different level of exploration implied by different parameter settings in an ant colony optimisation and in a genetic algorithm for the travelling salesman problem. The results suggest that it may be possible to establish a relation between exploration and performance of the algorithm.

In Location-Based Services (LBSs), the service is provided based on the users' locations through location deter- mination and mobility anticipation. Most of the current location prediction research focuses on generalised location models, where the geographic extent is divided into regular shape cells. One such technique is the Mobility Prediction based on an Ant System (MPAS), which depends on the earlier Ant Colony Optimisation (ACO) that suffers from problems such as search stagnation and pheromone update. In this paper, a New Routing Area Displacement Prediction (NRADP) is introduced, which works on the routing-area level instead of the cell level. Experimental results show that the NRADP offers improved effectiveness, higher prediction rate, and a reduced search stagnation ratio in comparison with the MPAS. Index Terms—Ant Colony Optimisation, LBSs, Mobility Pre- diction, Cellular Network, UMTS, Routing Area.

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link. Efficient rescheduling after a perturbation is an important concern of the railway industry. Extreme delays can result in large fines for the train company as well as dissatisfied customers. The problem is exacerbated by the fact that it is a dynamic one; more timetabled trains may be arriving as the perturbed trains are waiting to be rescheduled. The new trains may have different priorities to the existing trains and thus the rescheduling problem is a dynamic one that changes over time. The aim of this research is to apply a population-based ant colony optimisation algorithm to address this dynamic railway junction rescheduling problem using a simulator modelled on a real-world junction in the UK railway network. The results are promising: the algorithm performs well, particularly when the dynamic changes are of a high magnitude and frequency.

International audience; We present a model for the travelling salesman problem (TSP) solved using the ant colony optimisation (ACO), a bio-inspired mechanism that helps speed up the search for a solution and that can be applied to many other problems. The natural complexity of the TSP combined with the self-organisation and emergent behaviours that result from the application of the ACO make model-checking this system a hard task. We discuss our approach for modelling the ACO in a well-known probabilistic model checker and describe results of verifications carried out using our model and a couple of probabilistic temporal properties. These results demonstrate not only the effectiveness of the ACO applied to the TSP, but also that our modelling approach for the ACO produces the expected behaviour. It also indicates that the same modelling could be used in other scenarios.

The task assignment problem commonly appears in distributed computing environments. It asks an assignment of tasks to processors is found such that it satisfies the imposed constraints and that the total execution and communication cost of the tasks is minimal. This paper presents an algorithm based on ant colony optimisation that incorporates ideas from the clonal selection algorithm. Namely, the ant colony optimisation algorithm includes the cloning of the iteration-best ant and mutation of its clones' solutions ; the goal being a better exploitation of promising parts of the search space. Besides that, the solution construction procedure is modified to take the memory constraints into account and the pheromone update mechanism is modified to enable the best clone to deposit pheromone. The experimental analysis, conducted on a large number of problem instances, showed that the proposed algorithm performs better compared to the MAX-MIN ant system, a differential evolution and a particle swarm optimisation algorithm.

An Ant Colony Optimisation technique has been implemented in order to help students visit pedagogical items proposed by Paraschool (a French leading e-learning company). The large number of students (more than 250 000) suggested to students as artificial ants, to leave stigmergic information on the web-site graph. This difference brought many changes in the original ACO process, but also a large improvement in the students' guiding system. The concept of Man-Hill has therefore been introduced.At this stage, it becomes necessary to rate the pedagogical items to refine the model and propose the students with items corresponding to their level. The Elo rating (used in Chess competitions) is used for his purpose. As a side effect, it revealed to be also a powerful audit system that can track semantic problem in exercises.; Ce travail de recherche se place dans le cadre de l'adaptation du paradigme de l'Optimisation par Colonie de Fourmis (OCF) à l'environnement du logiciel d'e-learning de la société Paraschool (leader français du soutien scolaire sur internet) pour le rendre intelligent et réactif à l'utilisateur. Le grand nombre d'utilisateurs a permis d'associer chaque élève à une fourmi, ce qui signifie que le mécanisme d'optimisation n'est plus basé sur une colonie de fourmis artificielles, mais sur une « colonie d'élèves » que nous appellerons Hommilière.Dans le but d'adapter la force des exercices aux élèves, la notation Elo des échecs a été appliquée afin d'évaluer le niveau des élèves et des items. En plus de cela, le système Elo s'avère être un très bon système d'audit, capable, entre autres, de mettre en évidence les erreurs sémantiques des exercices.

In this, an attempt has been made to analyse blood vessels in human retinal digital images using Ant Colony Optimisation (ACO) based edge detection algorithm and was then correlated with Otsu and Matched filter methods. Results show that the ACO method provides high visual quality output with better detection of blood vessels. It provides better delineation, distinctly differentiates central veins, extracts small blood vessels and detects abnormalities in the image. The ratio of vessel-to-vessel free area using ACO method is distinctly different for normal and abnormal images (p < 0.005). It appears that this study is useful for mass screening and avoids complications at later stages of diseases. Copyright � 2011 Inderscience Enterprises Ltd.

In this paper we propose a Stochastic User Equilibrium (SUE) algorithm that can be adopted as a model, known as a simulation model, that imitates the behaviour of transportation systems. Indeed, analyses of real dimension networks need simulation algorithms that allow network conditions and performances to be rapidly determined. Hence, we developed an MSA (Method of Successive Averages) algorithm based on the Ant Colony Optimisation paradigm that allows transportation systems to be simulated in less time but with the same accuracy as traditional MSA algorithms. Finally, by means of Blum’s theorem, we stated theoretically the convergence of the proposed ACO-based algorithm.