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Producing Synthetic Natural Gas (SNG) via Power-to-Gas (PtG) is favourable for two reasons; it can be substituted for natural gas in the gas network, and it enables CO2 recycling as energy systems transition towards a low-carbon future. However, the expensive SNG production process is a barrier to being cost-competitive with other market gases. Several diverse factors influence SNG production cost, which results in several possible process configurations with varying performance which influence it. The hydrogen (H2) and carbon dioxide (CO2) required to produce SNG are available from multiple sources, while the SNG production cost is also influenced by the capital investment required for PtG process units, interim storage facilities, and operating expenses. Hence, an in-depth analysis of the factors affecting SNG production is required to understand their effect on the cost and to provide information on cost savings, economic implications, and optimal SNG production setup for decision-makers. In this paper, an optimisation algorithm is developed to model the PtG process units. The main objective of this work is to determine optimal process configurations for SNG production by analysing its influencing cost factors. A factorial design approach is integrated into the optimisation process to minimise the production cost by choosing the cost-effective process configurations. This work also determines the factors with a significant influence on the production cost using an ANOVA. The algorithm identifies the cost-effective H2 and CO2 source to obtain the least expensive SNG production setup. Based on the values of the cost factors, strategies for lowering the production cost in an existing setup are identified. The factors with the most influence on the SNG production cost are the capacity and capex of the methanator unit

In this work, recombination mechanisms are investigated in Cu(In, Ga)Se 2 solar cells based on numerical modelling and verified by regression analysis of the dark and light current density–voltage (J–V) curves. Loss mechanisms such as a back contact barrier, deep level defects in the absorber layer are determined by fitting the simulated cell performance with the measurements using global optimisation algorithm differential evolution. The cell performance in the fitting process includes J–V curves recorded at different temperatures and open-circuit voltage(V o c ) under different illumination intensities at 300K. The results show that for CIGSe solar cells with different preparation methods and absorber thickness, the main loss mechanisms are different. Based on the proposed numerical model and the observed loss mechanisms, suggestions are given for further improving the solar cell efficiency in each cell.

Smart devices and people existing on the internet are connected to smart objects or things in the Internet of Things (IoT) technology. To protect the user information, it is required to detect mali...

There are many issues and problems in cloud computing that researchers try to solve by using different techniques. Most of the cloud challenges are NP-hard problems; therefore, many meta-heuristic ...

The construction industry is mainly dependent on human resources, and labour costs are significant. Although many researchers have investigated construction labour productivity (CLP), the lack of a...

In this manuscript, an upgraded Fruit Fly Optimization Algorithm (FFOA) is proposed for optimising task scheduling and resource management processes. The proposed FOA algorithm is utilized to address the issues. In the proposed algorithm, every solution is represented by fruit fly. Every fruit fly upgrades their status through well‐organized smell search process. First, a basic approach is put forward to allocate every task for numerous resources and execution time is measured for every task. Second, the overloaded virtual machines (VMs) are identified and the load is balanced to obtain optimal system resource utilisation. The ability of Fly Task Scheduling Algorithm is to schedule the VMs execution time of tasks is minimal. The results of the Fruit Fly‐based algorithms such as Fruit Fly Task Scheduling Algorithm, Modified Fruit Fly Task Scheduling Algorithm, Improved Fruit Fly Task Scheduling Algorithm and Multi‐Swarm Fruit Fly Task Scheduling Algorithm (MSFFTSA) are proposed and analysed. The upgraded Fruit Fly Optimization algorithm of MSFFTSA is compared with different Fruit Fly algorithms. Finally, the proposed algorithm is compared with other algorithms and the experimental results shows that the proposed MSFFTSA technique is better than the other algorithms of the fruit fly algorithms.

Optimising the coding bit-rates and activity rates of sensor nodes is essential for the functionality and survival of wireless sensor networks as these rates not only affect the amount of information collected by the network, but also affect its power consumption. This study proposes a framework for joint coding bit-rates and activity rates optimisation (CARO) of sensor nodes in wireless visual sensor networks under limited energy constraints. The framework uses the concept of accumulative visual information (AVI) as a measure of the amount of visual information collected from a sensor node. The authors propose two optimisation algorithms for the following two cases: (I) networks with predetermined operation time, where algorithm CARO I maximises the AVI of the network over its predetermined operation time. (II) Best effort networks, where algorithm CARO II maximises the network operation time under constraints on the collected visual information. Simulations show that optimising the rates using CARO maximises the AVI of the network and extends its operation time compared to the straightforward solution when all nodes have equal activity rates. This is because the authors' framework takes into consideration that different nodes in different locations may operate under different conditions and collect information of different significance.

Rock bolts play an important supporting role in the construction of slopes, deep foundation pits and tunnels. As such, it is especially necessary to assess bolt anchorage quality. This paper proposes an identification model for bolt anchorage defects based on an Elman neural network (ElmanNN) optimised using an improved chicken swarm optimisation (CSO) algorithm and the frequency response function. First, the principal components of the frequency response functions of different anchorage bolts are used as the input within the model. Next, the weights and thresholds of the ElmanNN are optimised using an improved CSO algorithm based on chaotic disturbance and elite opposition-based learning. Finally, the model is used to identify bolt anchorage defects. The experimental results show that the model has a higher identification accuracy and faster convergence rate than other neural network models.Publisher Note: Following the original publication of this paper, a change was made to identify the corresponding authors on page 588. Full details can be found in the correction notice given after page 597 of the PDF for this article.

Virtual machine placement in cloud computing is to allocate the virtual machines (VMs) (user request) to suitable physical machines (PMs) so that the wastage of resources is reduced. Allocation of appropriate VMs to suitable and effective PMs will lead the service provider to be a better competitor with more available resources for affording a greater number of VMs simultaneously which in turn reflects with the growth in the economy. In this research work, an augmented intelligent water drop (IWD) algorithm is used for effectively placing VMs. The preliminary goal of this proposed work is to reduce the overall resource utilisation by packing the VMs to appropriate PMs effectively. The proposed IWD model is tested under the standard simulation process as it is given in the literature. Performance of IWD is compared with the existing techniques first fit decreasing, least loaded and ant colony optimisation algorithm. Performance analysis shows the significance of the proposed method over existing techniques.

Semantic image segmentation treats the issues involved in the object recognition and image segmentation as a combined task. The chief notion of semantic segmentation is to partition the image into visually uniform regions and to discriminate the class of the partitioned regions. Pixel classification is done over the segmented regions by assigning semantic labels. In general, inference frameworks are fed with the combination of low‐level features and high‐level contextual cues to segment an image. Since these combinations are rarely object consistent, result with minimum classification accuracy because of choosing non‐influencing features and cues to track specific objects. To overcome this problem, a nature‐inspired meta‐heuristic optimization algorithm called Seed Picking Crossover Optimization (SPCO) is proposed to optimize i.e. train the CRF (Conditional Random Field) for choosing relevant feature to segment the object with high accuracy. To meritoriously recognize the objects, a semi‐segmentation process is initially performed using Simple Linear Iterative Clustering (SLIC) algorithm. For pixel transformation and pixel association, Dirichlet process mixture model and CRF are employed. Optimized CRFs are used where the parametric optimization is done using the proposed SPCO algorithm. The proposed work results with 84% on classification accuracy and the performance evaluations are done using MSRC‐21 dataset.

Wireless sensor networks are an emerging technology deployed in many fields. Knowing the positions of sensor nodes is critical for many applications to perform their tasks. Hence, accurate node localisation in wireless sensor networks is an essential issue. The node localisation problem can be formulated as a multidimensional optimisation problem. In this study, the authors propose a node localisation scheme based on a sine–cosine algorithm. The proposed scheme addresses the flip ambiguity problem, which can badly affect the localisation accuracy of the entire network. The efficacy of the proposed scheme is evaluated through intensive experimentation under different scenarios. The obtained results show the superiority of the proposed scheme against other optimisation algorithms in terms of the localisation accuracy, the number of localised nodes, and the computation time.

The automatic detection of postures and posture changes in sows using a computer-vision system has substantial potential for learning their maternal abilities, enhancing their welfare and productivity, and reducing the crushing risk to piglets. The objectives of this study are to (1) detect frame-level sow postures, (2) temporally localise posture change actions, and (3) generate spatio-temporally action tubes parsed from a long-time untrimmed segment of depth video. Depth videos were recorded for five batches of lactating sows, using a Kinect from a top-view in a commercial farm. Three batches were used for training and validation, and the other two for testing. Four postures (standing, sitting, ventral lying, and lateral lying) were automatically detected, with a mean average-precision (mAP) of 0.927. The localisation performance of the clip-level mAP involved eight posture change actions, and achieved 0.774 in the temporal intersection over union (tIoU) ≥ 0.5. A tube optimisation algorithm was used to optimise and smooth the action tubes. When the mean IoU≥0.8 in the tube, the performance of the video-level mAP significantly improved, from 0.313 to 0.796. The error analysis could deepen the understanding of the causes of errors in action detection. The system was applied to test two day videos of various sows, by obtaining the regularity of posture change probability, comparing the action characteristics, and discerning the maternal differences of the sows. The methodology can be applied in large-scale deployments for learning livestock action preferences and behavioural traits, thereby enhancing welfare and productivity on a farm.

Cloud computing is a powerful technology of computing which renders flexible services anywhere to the user. Resource management and task scheduling are essential perspectives of cloud computing. One of the main problems of cloud computing was task scheduling. Usually task scheduling and resource management in cloud is a tough optimization issue at the time of considering quality of service needs. Huge works under task scheduling focuses only on deadline issues and cost optimization and it avoids the significance of availability, robustness and reliability. The main purpose of this study is to develop an Optimized Algorithm for Efficient Resource Allocation and Scheduling in Cloud Environment. This study uses PSO and R factor algorithm. The main aim of PSO algorithm is that tasks are scheduled to VM (virtual machines) to reduce the time of waiting and throughput of system. PSO is a technique inspired by social and collective behavior of animal swarms in nature and wherein particles search the problem space to predict near optimal or optimal solution. A hybrid algorithm combining PSO and R-factor has been developed with the purpose of reducing the processing time, make span and cost of task execution simultaneously. The test results and simulation reveals that the proposed method offers better efficiency than the previously prevalent approaches.

Moth-flame Optimisation Algorithm (MFO) is a new metaheuristics optimisation algorithm presented by Mirjalili in 2015 which inspired by the navigation method of moths in nature. It has gained a hug...

A chiller is a machine that removes heat from a liquid through one of the refrigeration cycles and heat exchangers and uses it to cool the air and the equipment. In this paper, a new method for sol...

Aiming at the low quality of the solution obtained with the traditional marginal analysis algorithm for solving multi-level inventory optimisation problem of repairable spare parts, a hybrid algorithm integrating particle swarm optimisation, marginal analysis algorithm-METRIC model and tabu search is proposed. In the early stage of the hybrid algorithm, particle swarm optimisation is used to explore the global solution space and in the later stage, tabu search is used to local search, and the marginal analysis algorithm is used to constructing neighbourhood. The experimental results show that the proposed algorithm can solve the problem of spare parts multi-level inventory optimisation effectively and efficiently.

Cloud computing became an inevitable information technology industry. Despite its several plus points such as economy of scale and rapid elasticity, it suffers from vendor lock-in, resource limitat...

Production of functional molecules from renewable bio-feedstocks and bio-waste has the potential to significantly reduce the greenhouse gas emissions. However, the development of such processes commonly requires invention and scale-up of highly selective and robust chemistry for complex reaction networks in bio-waste mixtures. We demonstrate an approach to optimising a chemical route for multiple objectives starting from a mixture derived from bio-waste. We optimise the recently developed route from a mixture of waste terpenes to p-cymene. In the first reaction step it was not feasible to build a detailed kinetic model. A Bayesian multiple objectives optimisation algorithm TS-EMO was used to optimise the first two steps of reaction for maximum conversion and selectivity. The model suggests a set of very different conditions that result in simultaneous high values of the two outputs.

Results from a triple-blind mixed-method user study into the effectiveness of mixed-initiative tools for the procedural generation of game levels are presented. A tool which generates levels using interactive evolutionary optimisation was designed for this study which (a) is focused on supporting the designer to explore the design space and (b) only requires the designer to interact with it by designing levels. The tool identifies level design patterns in an initial hand-designed map and uses that information to drive an interactive optimisation algorithm. A rigorous user study was designed which compared the experiences of designers using the mixed-initiative tool to designers who were given a tool which provided completely random level suggestions. The designers using the mixed-initiative tool showed an increased engagement in the level design task, reporting that it was effective in inspiring new ideas and design directions. This provides significant evidence that procedural content generation can be used as a powerful tool to support the human design process., Accepted to be Published in: IEEE Transactions on Games

Graphical abstract, With omics data being generated at an unprecedented rate, genome-scale modelling has become pivotal in its organisation and analysis. However, machine learning methods have been gaining ground in cases where knowledge is insufficient to represent the mechanisms underlying such data or as a means for data curation prior to attempting mechanistic modelling. We discuss the latest advances in genome-scale modelling and the development of optimisation algorithms for network and error reduction, intracellular constraining and applications to strain design. We further review applications of supervised and unsupervised machine learning methods to omics datasets from microbial and mammalian cell systems and present efforts to harness the potential of both modelling approaches through hybrid modelling.

Frequency-based relays, commonly used for islanding detection of synchronous distributed generation (SDG), may either have large non-detection zones (NDZs) or wrongly operate for non-islanding disturbances unless they are efficiently adjusted. Trial and error simulation-based methods, employing application region (AR) and/or power imbalance AR (PIAR), are presented in the literature to adjust these relays for quick islanding detection without false trips. However, the associated settings are not optimal and finding them is time-consuming. In this study, an approach utilising particle swarm optimisation algorithm is proposed to adjust a rate of change of frequency (ROCOF) relay, one of the most effective relays in SDG islanding detection. The proposed approach needs a set of analytical formulas for determining the ROCOF relay performance curve and NDZ as well as AR and PIAR. These formulas are developed to be employed in the relay adjustment. By applying the developed analytical formulas to the optimisation-based approach, the ROCOF relay is adjusted fast and systematically. It is shown that while the performance of the relay adjusted by the proposed approach is close to the one with setting obtained by corresponding trial and error-based method, the latter is much more time-consuming than the former.

The production planning and scheduling problems of printed circuit board (PCB) assembly line robustly influence the production efficiency of PCB industries. The current study focuses on the optimis...

In this paper, exergy analysis of a steam power plant located in southern Iran named Zarand power plant has been studied. In order to optimize the performance of the Rankine cycle and achieve highe...

This paper presents the results of an experimental study of the detection of a defect in steel plates based on an optimisation algorithm using small amplitude homogenisation. In a first approximati...

Large time series are difficult to be mined and preprocessed, hence reducing their number of points with minimum information loss is an active field of study. This paper proposes new methods based on time series segmentation, including the adaptation of the particle swarm optimisation algorithm (PSO) to this problem, and more advanced PSO versions, such as barebones PSO (BBPSO) and its exploitation version (BBePSO). Moreover, a novel algorithm is derived, referred to as dynamic exploitation barebones PSO (DBBePSO), which updates the importance of the social and cognitive components throughout the generations. All these algorithms are further improved by considering a final local search step based on the combination of two well-known standard segmentation algorithms (Bottom-Up and Top-Down). The performance of the different methods is evaluated using 15 time series from various application fields, and the results show that the novel algorithm (DBBePSO) and its hybrid version (HDBBePSO) outperform the rest of segmentation techniques.

The economic viability of tidal energy projects will likely require large-scale deployment, with tens, or even hundreds, of turbines positioned in an array. There is limited published research available on the viability of large-scale deployments, which will depend on both the expected energy yield and potential adverse hydro-environmental impacts. This leads to the question of how best to position turbines in large-scale arrays for maximum energy capture with minimal associated hydro-environmental impacts. The primary aim of this research was the further development of an existing two-dimensional tidal flow model for optimising tidal turbine arrays relative to both power output and potential hydro-environmental impacts. Turbine impacts are simulated using a momentum sink approach and an optimisation algorithm was implemented, which determines an optimal array configuration for maximum energy capture, whilst employing spatial and environmental impact constraints. Application of the developed model to an idealised test case demonstrated that a staggered array is optimal, if one considers maximising power capture alone, while a fence layout was optimal, when considering both power output and associated impacts. In both the scenarios, the optimised configurations produced higher efficiencies than symmetrical inline arrays. To demonstrate the model’s applicability to a real tidal environment, it was applied to a case study site; the Shannon Estuary, along the West Coast of Ireland. To the authors’ knowledge, this is the first automated array optimisation model to incorporate hydro-environmental impact constraints with the aim of determining specific optimised array configurations with quantifiable efficiencies. The approach could be extremely useful for determining the economic viability of proposed arrays, enabling determination of ‘environmentally acceptable’ levels of tidal energy extraction, and thereby allowing the completion of realistic and accurate early stage cost–benefit analyses for tidal energy projects. The model is therefore, potentially, a very valuable tool for tidal energy researchers, developers and planners.

For a successful transition to a renewable energy economy, optimisation of renewable energy systems must evolve to take into account not only technical and/or economic objectives, but also environmental and socio-political objectives. A Normalised Weighted Constrained Multi-Objective (NWCMO) meta-heuristic optimisation algorithm has been proposed for achieving a compromise between mutually conflicting technical, economic, environmental and socio-political objectives, in order to simulate and optimise a renewable energy system of any configuration. In this paper, an optimisation methodology based on these four classes of objective is presented. The methodology was implemented using Particle Swarm Optimisation and tested against data from the literature. In the Case Study the original results could be reproduced, but the newly-implemented algorithm was further able to find a more optimal design solution under the same constraints. The influence of additional quantified socio-political inputs was explored.

This paper presents a new and efficient damage detection method for civil structures. To identify damage localisation and quantification, the proposed method defines the damage detection problem as...

Although several indoor positioning systems for greenhouse have been developed in automated agricultural machinery monitoring, the data measured by positioning systems still has large errors. In order to solve the problem, in this study, improved weighted-centroid algorithm and maximum likelihood estimation (MLE) algorithm were developed to optimise positioning data of agricultural machinery in the greenhouse, which is measured by ultra-wide band-based indoor positioning system according to different agricultural machinery working states. Compared with real coordinate data, when agricultural machinery stays still, the average static error of the data was 66.4 mm with the improved weighted-centroid algorithm; when the machinery is in dynamic state, the average dynamic error of the data was 61 mm and the probability that error

In this paper, we present a novel Type-2 fuzzy multi-objective multi-chromosomal optimisation algorithm for capacity planning within telecommunication networks. The proposed system is compared to one of the most successful multi-objective optimisation algorithms which is NSGA-II. This comparison shows that in the capacity planning problems the proposed algorithm can produce a better solution front than NSGA-II in 80% - 93 % of cases. Additionally the use of Type-2 fuzzy logic produces a better solution front in 72% of cases when compared to using Type-1 fuzzy logic.

Unlike more established renewable energy conversion technologies, such as wind turbines, wave power systems have reached neither commercial maturity,nor technological convergence. The significant variation in device geometries and operating principles has resulted in a diversification of effort, with little coordination or true comparative analysis. The situation is compounded by the relative lack of systematic optimisation applied to the sector, partly explained by the complexity and uncertainty associated with wave energy system models, as well as difficulties in the evaluation of appropriate target function metrics. This review provides a critical overview of the state-of-the-art in wave energy device geometry optimisation, comparing and contrasting various optimisation approaches, and attempting to detail the current limitations preventing further progress, and convergence, in the development of optimal wave energy technology.

Transparent and reproducible image processing is fundamental in science and engineering, whether to prove the efficiency of an algorithm or to prepare a new corpus of images. In this paper, we propose a Nature Inspired Optimisation algorithm based on Image Quality Assessment methods to obtain a reproducible sequence of transformations that improves the quality of a given image. Preliminary tests were realized on state-of-the-art benchmarks.

The key contribution of this article to the domain of engineering optimisation is establishing best practices with respect to the development of evolutionary algorithms in the context of design engineering. Despite their various uses, the uptake of evolutionary algorithms in industry remains limited. In order to understand why uptake is low a survey of engineers was undertaken, the results of which are presented here. A total of 23 participants (N = 23) took part in the 3-section mixed methods survey. Reflexive thematic analysis was conducted on the open-ended questions. A common thread throughout participants responses is that there is a question of trust towards evolutionary algorithms within industry. Perhaps surprising is that the key to gaining this trust is not producing good results, but creating algorithms which explain the process they take in reaching a result. Based on this, recommendations have been made to increase their use in industry.

This chapter presents examples of studies in which scientific techniques have assisted the construction of playable reproductions of instruments from the ancient world, including the Etruscan cornu and the Celtic carnyx. The book concludes with a discussion of some recent developments which exploit digital technology including optimisation algorithms, active control and ‘live electronics’ to extend the capabilities of existing brass instruments.

This study addresses the pose estimation problem of an aircraft runway using visual observations in a landing approach scenario. The authors utilised the fact that the geodetic coordinates of most runways are known precisely with highly visible markers. Thus, the runway observations can increase the level of situational awareness during the landing approach, providing additional redundancy of navigation and less reliance on global positioning system. A novel pose optimisation algorithm is proposed utilising unit dual quaternion for the runway corner observations obtained from a monocular camera. The estimated runway pose is further fused with an inertial navigation system in an extended Kalman filter. An open-source flight simulator is used to collect and process the visual and flight dataset during the landing approach, demonstrating reliable runway pose estimates and the improved inertial navigation solution.

In the last decade, deep learning(DL) has witnessed excellent performances on a variety of problems, including speech recognition, object recognition, detection, and natural language processing (NLP) among many others. Of these applications, one common challenge is to obtain ideal parameters during the training of the deep neural networks (DNN). These typical parameters are obtained by some optimisation techniques which have been studied extensively. These research have produced state-of-art(SOTA) results on speed and memory improvements for deep neural networks(NN) architectures. However, the SOTA optimisers have continued to be an active research area with no compilations of the existing optimisers reported in the literature. This paper provides an overview of the recent advances in optimisation algorithms and techniques used in DNN, highlighting the current SOTA optimisers, improvements made on these optimisation algorithms and techniques, alongside the trends in the development of optimisers used in training DL based models. The results of the search of the Scopus database for the optimisers in DL provides the articles reported as the summary of the DL optimisers. From what we can tell, there is no comprehensive compilation of the optimisation algorithms and techniques so far developed and used in DL research and applications, and this paper summarises these facts.

Under low-carbon scheduling, to solve the hybrid lot-streaming flowshop scheduling (HLFS) problem, an improved migrating birds optimisation (IMBO) algorithm is proposed to minimise the total flow time. The neighbourhood solution of the population is generated by insertion and exchange, and the population is further optimised by a local search method. In addition, a reset mechanism is added to avoid falling into a local optimum. Extensive computational results demonstrate the feasibility and effectiveness of the proposed algorithm.

Wind energy has received increasing attention in recent years due to its sustainability and geographically wide availability. The efficiency of wind energy utilisation highly depends on the performance of wind turbines, which convert the kinetic energy in wind into electrical energy. In order to optimise wind turbine performance and reduce the cost of next-generation wind turbines, it is crucial to have a view of the state of the art in the key aspects on the performance optimisation of wind turbines using Computational Fluid Dynamics (CFD), which has attracted enormous interest in the development of next-generation wind turbines in recent years. This paper presents a comprehensive review of the state-of-the-art progress on optimisation of wind turbine performance using CFD, reviewing the objective functions to judge the performance of wind turbine, CFD approaches applied in the simulation of wind turbines and optimisation algorithms for wind turbine performance. This paper has been written for both researchers new to this research area by summarising underlying theory whilst presenting a comprehensive review on the up-to-date studies, and experts in the field of study by collecting a comprehensive list of related references where the details of computational methods that have been employed lately can be obtained.

This paper present a comparison between Fuzzy Cognitive Map (FCM) learning approaches and algorithms. FCMs are fuzzy digraphs with weights and feedback loops, consisting of nodes interconnected through directed arcs mostly used for knowledge representation and system modelling. One of the most important characteristics of FCMs is their learning capabilities. FCMs are normally constructed through experts’ opinions, thus they maybe subjective. Learning algorithms are introduced to overcome this inconvenient. One of the main problem of the new proposed algorithms is their validation. Using theoretical and experimental analysis, this research aims to (1) compare FCM learning algorithms proposed in the literature, (2) provide a validation tool for new FCM learning algorithms (3) present a new algorithm called Asexual Reproduction Optimisation (ARO) with one of its extensions – Modified ARO (MARO) – as a novel FCM learning algorithm to use the validation tool proposed. According to the findings from the literature, it seems that among FCM learning approaches, population-based algorithms perform better compared to other algorithms. Also, the testing was done in five benchmark datasets and a synthetic dataset with different node sizes using two criteria of in-sample and out-of-sample errors. The results show that MARO outperforms other algorithms in both error functions in terms accuracy and robustness.

Compressed-air energy storage (CAES) is an energy-storage option with a history of almost half a century. The concept of CAES is formed around the integration of air storage with a gas-fired power ...

Optimal operation of reservoirs is one of the most important issues in water resources management. In this study, a novel metaheuristic optimisation algorithm, called the water cycle algorithm (WCA), was used to derive operating policy for a multi-reservoir system. In the first step, the performance of the model was successfully assessed through several benchmark functions. The WCA was then used to derive the optimal operation of four- and ten-reservoir systems. It was then applied to the monthly operation of Golestan and Voshmgir consecutive dams located in Gorganrood basin in the north of Iran. In this way, the objective function was defined as minimising the total deficit for the study period. The WCA results were compared with the results of other developed evolutionary algorithms, including a genetic algorithm, harmony search algorithm, particle swarm optimisation and imperialist competitive algorithm. The results showed that, for cases of four- and ten-reservoir systems, the best solutions achieved by the WCA were 306·3918 and 1172·4197, which had differences of 0·5% and 1% compared with the global optimum solutions, respectively. In addition, it was found that the WCA was superior to other algorithms in calculating the annual deficit of the Golestan–Voshmgir multi-reservoir system.

In view of the observation that saliency maps generated by saliency detection algorithms usually show similarity imperfection against the ground truth, the authors propose an optimisation algorithm based on clustering and fitting (CF) for saliency detection. The algorithm uses a fitting model to represent the quantitative relationship between ground truth and algorithm-generated saliency maps. The authors use the K -means method to cluster the images into k clusters according to the similarities among images. Image similarity is measured in terms of scene and colour by using the GIST and colour histogram features, after which the fitting model for each cluster is calculated. The saliency map of a new image is optimised by using one of the fitting models which correspond to the cluster to which the image belongs. Experimental results show that their CF-based optimisation algorithm improves the performance of various single image saliency detection algorithms. Moreover, the improvement achieved by their algorithm when using both CF strategies is greater than the improvement achieved by the same algorithm when not using the clustering strategy. In addition, their proposed optimisation algorithm can also effectively optimise co-saliency detection algorithms which already consider multiple similar images simultaneously to improve saliency of single images.

The approach of Learning from Demonstrations (LfD) can support human operators especially those without much programming experience to control a collaborative robot (cobot) in an intuitive and convenient means. Gaussian Mixture Model and Gaussian Mixture Regression (GMM and GMR) are useful tools for implementing such a LfD approach. However, well-performed GMM/GMR require a series of demonstrations without trembling and jerky features, which are challenging to achieve in actual environments. To address this issue, this paper presents a novel optimised approach to improve Gaussian clusters then further GMM/GMR so that LfD enabled cobots can carry out a variety of complex manufacturing tasks effectively. This research has three distinguishing innovative characteristics: 1) a Gaussian noise strategy is designed to scatter demonstrations with trembling and jerky features to better support the optimisation of GMM/GMR; 2) a Simulated Annealing-Reinforcement Learning (SA-RL) based optimisation algorithm is developed to refine the number of Gaussian clusters in eliminating potential under-/over-fitting issues on GMM/GMR; 3) a B-spline based cut-in algorithm is integrated with GMR to improve the adaptability of reproduced solutions for dynamic manufacturing tasks. To verify the approach, cases studies of pick-and-place tasks with different complexities were conducted. Experimental results and comparative analyses showed that this developed approach exhibited good performances in terms of computational efficiency, solution quality and adaptability.

Process modelling and simulation for composites manufacturing have advanced remarkably lately, evolving from just an assistive design tool to playing a pivotal role in the optimisation of Liquid Composite Moulding (LCM) processes. In particular, to improve the mould filling stage, the placement optimisation of resin injection gates and air vents on the mould design has received great research attention due to their substantial influence on the manufacturing efficiency and product quality. When optimisation algorithms are introduced into predictive numerical simulations to assist mould design, the notion of simulation-based optimisation is formed. This review provides a comprehensive literature compilation on the state-of-the-art of simulation-based optimisation of gates and vents configuration for LCM processes, focusing on the optimisation algorithms adopted and their respective search strategies. The search performance, solution optimality and application suitability of each algorithm with respect to different moulding scenarios are discussed to provide helpful guidelines for future optimisation studies.

Analysing the stability of soil slopes and investigating critical slip surfaces present an important and very complicated optimisation problem. In this paper, we solve this problem by introducing and modifying a new global optimisation (black hole algorithm) technique to form a new global optimisation algorithm for continuous optimisation (the improved black hole algorithm). By combination with typical mature limit equilibrium analysis (the Spencer method and the Morgenstern-Price method), the improved black hole algorithm was used to find the critical slip surface of soil slope. Thus, this paper proposed a new method for finding the critical slip surface of soil slope. Finally, the proposed method was verified through two typical numerical soil slope cases and was compared with the black hole algorithm and other extant studies. The results indicated that the applicability, validity and effectiveness of the improved black hole algorithm showed an improvement over previous studies and over the black hole algorithm. Moreover, the proposed method was applied for one highway landslide, and the determined critical slip surface was well agreement with the real one.

This paper presents a stochastic optimisation model for the long-term expansion planning of the electric vehicle charging infrastructure based on the minimisation of the charging station overall costs subjected to the charging reliability and the requested Quality of Service. In fact, an earlier deterministic optimisation model is upgraded to a stochastic model due to the stochastic nature of the mobility behaviour of electric vehicle drivers, driving range, disposable charging time and the overall costs for different charging technology types. A probabilistic approach is used to generate numerous stochastic trajectories for electric vehicles followed by the newly proposed scenario reduction procedure that employs the new Trajectory Similarity Index to obtain representative trajectories of the stochastic mobility behaviour of electric vehicle drivers. The K-MEANS reduction procedure is also used to derive stochastic scenarios of the electric vehicle driving range, Quality of Service and overall (installation, maintenance, operation) costs, which are subsequently executed by applying an optimisation algorithm together with representative trajectories. The proposed model is verified on a test road network. Results show the optimal charging locations and their placement probability, which exposes their importance to charging infrastructure planners in terms of prioritisation and robust decision-making.

A backtracking search optimisation algorithm that uses historic population information for learning was proposed recently for solving optimisation problems. However, the learning ability and the robustness of this algorithm remain relatively poor. To improve the performance of the backtracking search algorithm (BSA), a modified backtracking search optimisation algorithm (MBSA), based on learning and niching strategies, is presented in this paper. Three main strategies, a learning strategy, a niching strategy, and a mutation strategy, are incorporated into the proposed MBSA algorithm. Learning the best individual in current generation and the best position achieved so far is used to improve the convergence speed. Niching and mutation strategies are used to improve the diversity of the MBSA. Finally, some benchmark functions and three chaotic time series prediction problems based on neural networks are simulated to test the effectiveness of MBSA, and the results are compared with those obtained using some other evolutionary algorithms (EAs). The simulation results indicate that the MBSA outperforms other EAs for most functions and chaotic time series.

CAMELOT (Computational-Analytical Multi-fidElity Low-thrust Optimisation Toolbox) is a toolbox for the fast preliminary design and optimisation of low-thrust trajectories. It solves highly complex combinatorial problems to plan multi-target missions characterised by long spirals including different perturbations. In order to do so, CAMELOT implements a novel multi-fidelity approach combining analytical surrogate modelling and accurate computational estimations of the mission cost. Decisions are then made by using two optimisation engines included in the toolbox, a single objective global optimiser and a combinatorial optimisation algorithm. CAMELOT has been applied to a variety of case studies: from the design of interplanetary trajectories to the optimal deorbiting of space debris, from the deployment of constellations to on-orbit servicing. In this paper the main elements of CAMELOT are described and two examples, solved using the toolbox, are presented.