A Lite Fireworks Algorithm with Fractal Dimension Constraint for Feature Selection

As the use of robotics becomes more widespread, the huge amount of vision data leads to a dramatic increase in data dimensionality. Although deep learning methods can effectively process these high-dimensional vision data. Due to the limitation of computational resources, some special scenarios still rely on traditional machine learning methods. However, these high-dimensional visual data lead to great challenges for traditional machine learning methods. Therefore, we propose a Lite Fireworks Algorithm with Fractal Dimension constraint for feature selection (LFWA+FD) and use it to solve the feature selection problem driven by robot vision. The "LFWA+FD" focuses on searching the ideal feature subset by simplifying the fireworks algorithm and constraining the dimensionality of selected features by fractal dimensionality, which in turn reduces the approximate features and reduces the noise in the original data to improve the accuracy of the model. The comparative experimental results of two publicly available datasets from UCI show that the proposed method can effectively select a subset of features useful for model inference and remove a large amount of noise noise present in the original data to improve the performance.Comment: International Conference on Pharmaceutical Sciences 202

A survey of swarm intelligence for dynamic optimization: algorithms and applications

Swarm intelligence (SI) algorithms, including ant colony optimization, particle swarm optimization, bee-inspired algorithms, bacterial foraging optimization, firefly algorithms, fish swarm optimization and many more, have been proven to be good methods to address difficult optimization problems under stationary environments. Most SI algorithms have been developed to address stationary optimization problems and hence, they can converge on the (near-) optimum solution efficiently. However, many real-world problems have a dynamic environment that changes over time. For such dynamic optimization problems (DOPs), it is difficult for a conventional SI algorithm to track the changing optimum once the algorithm has converged on a solution. In the last two decades, there has been a growing interest of addressing DOPs using SI algorithms due to their adaptation capabilities. This paper presents a broad review on SI dynamic optimization (SIDO) focused on several classes of problems, such as discrete, continuous, constrained, multi-objective and classification problems, and real-world applications. In addition, this paper focuses on the enhancement strategies integrated in SI algorithms to address dynamic changes, the performance measurements and benchmark generators used in SIDO. Finally, some considerations about future directions in the subject are given

Sound-to-imagination: an exploratory study on cross-modal translation using diverse audiovisual data

The motivation of our research is to explore the possibilities of automatic sound-to-image (S2I) translation for enabling a human receiver to visually infer occurrences of sound-related events. We expect the computer to ‘imagine’ scenes from captured sounds, generating original images that depict the sound-emitting sources. Previous studies on similar topics opted for simplified approaches using data with low content diversity and/or supervision/self-supervision for training. In contrast, our approach involves performing S2I translation using thousands of distinct and unknown scenes, using sound class annotations solely for data preparation, just enough to ensure aural–visual semantic coherence. To model the translator, we employ an audio encoder and a conditional generative adversarial network (GAN) with a deep densely connected generator. Furthermore, we present a solution using informativity classifiers for quantitatively evaluating the generated images. This allows us to analyze the influence of network-bottleneck variation on the translation process, highlighting a potential trade-off between informativity and pixel space convergence. Despite the complexity of the specified S2I translation task, we were able to generalize the model enough to obtain more than 14%, on average, of interpretable and semantically coherent images translated from unknown sounds.The present work was supported in part by the Brazilian National Council for Scientific and Technological Development (CNPq) under PhD grant 200884/2015-8. Also, the work was partly supported by the Spanish State Research Agency (AEI), project PID2019-107579RBI00/AEI/10.13039/501100011033.Peer ReviewedPostprint (published version

Development of Hybrid PS-FW GMPPT Algorithm for improving PV System Performance Under Partial Shading Conditions

A global maximum power point tracking (MPPT) algorithm hybrid based on Particle Swarm Fireworks (PS-FW) algorithm is proposed which is formed with Particle Swarm Optimization and Fireworks Algorithm. The algorithm tracks the global maximum power point (MPP) when conventional MPPT methods fail due to occurrence of partial shading conditions. With the applied strategies and operators, PS-FW algorithm obtains superior performances verified under simulation and experimental setup with multiple cases of shading patterns

Park-and-Ride Facilities Design for Special Events Using Space-Time Network Models

abstract: Given that more and more planned special events are hosted in urban areas, during which travel demand is considerably higher than usual, it is one of the most effective strategies opening public rapid transit lines and building park-and-ride facilities to allow visitors to park their cars and take buses to the event sites. In the meantime, special event workforce often needs to make balances among the limitations of construction budget, land use and targeted travel time budgets for visitors. As such, optimizing the park-and-ride locations and capacities is critical in this process of transportation management during planned special event. It is also known as park-and-ride facility design problem. This thesis formulates and solves the park-and-ride facility design problem for special events based on space-time network models. The general network design process with park-and-ride facilities location design is first elaborated and then mathematical programming formulation is established for special events. Meanwhile with the purpose of relax some certain hard constraints in this problem, a transformed network model which the hard park-and-ride constraints are pre-built into the new network is constructed and solved with the similar solution algorithm. In doing so, the number of hard constraints and level of complexity of the studied problem can be considerable reduced in some cases. Through two case studies, it is proven that the proposed formulation and solution algorithms can provide effective decision supports in selecting the locations and capabilities of park-and-ride facilities for special events.Dissertation/ThesisMasters Thesis Civil and Environmental Engineering 201

Differential Evolution in Wireless Communications: A Review

Differential Evolution (DE) is an evolutionary computational method inspired by the biological processes of evolution and mutation. DE has been applied in numerous scientific fields. The paper presents a literature review of DE and its application in wireless communication. The detailed history, characteristics, strengths, variants and weaknesses of DE were presented. Seven broad areas were identified as different domains of application of DE in wireless communications. It was observed that coverage area maximisation and energy consumption minimisation are the two major areas where DE is applied. Others areas are quality of service, updating mechanism where candidate positions learn from a large diversified search region, security and related field applications. Problems in wireless communications are often modelled as multiobjective optimisation which can easily be tackled by the use of DE or hybrid of DE with other algorithms. Different research areas can be explored and DE will continue to be utilized in this contex

Comprehensive Taxonomies of Nature- and Bio-inspired Optimization: Inspiration versus Algorithmic Behavior, Critical Analysis and Recommendations

In recent years, a great variety of nature- and bio-inspired algorithms has been reported in the literature. This algorithmic family simulates different biological processes observed in Nature in order to efficiently address complex optimization problems. In the last years the number of bio-inspired optimization approaches in literature has grown considerably, reaching unprecedented levels that dark the future prospects of this field of research. This paper addresses this problem by proposing two comprehensive, principle-based taxonomies that allow researchers to organize existing and future algorithmic developments into well-defined categories, considering two different criteria: the source of inspiration and the behavior of each algorithm. Using these taxonomies we review more than three hundred publications dealing with nature-inspired and bio-inspired algorithms, and proposals falling within each of these categories are examined, leading to a critical summary of design trends and similarities between them, and the identification of the most similar classical algorithm for each reviewed paper. From our analysis we conclude that a poor relationship is often found between the natural inspiration of an algorithm and its behavior. Furthermore, similarities in terms of behavior between different algorithms are greater than what is claimed in their public disclosure: specifically, we show that more than one-third of the reviewed bio-inspired solvers are versions of classical algorithms. Grounded on the conclusions of our critical analysis, we give several recommendations and points of improvement for better methodological practices in this active and growing research field.Comment: 76 pages, 6 figure

Integrating Recognition and Decision Making to Close the Interaction Loop for Autonomous Systems

Intelligent systems are becoming increasingly ubiquitous in daily life. Mobile devices are providing machine-generated support to users, robots are coming out of their cages in manufacturing to interact with co-workers, and cars with various degrees of self-driving capabilities operate amongst pedestrians and the driver. However, these interactive intelligent systems\u27 effectiveness depends on their understanding and recognition of human activities and goals, as well as their responses to people in a timely manner. The average person does not follow instructions step-by-step or act in a formulaic manner, but instead varies the order of actions and timing when performing a given task. People explore their surroundings, make mistakes, and may interrupt an activity to handle more urgent matters. The decisions that an autonomous intelligent system makes should account for such noise and variance regardless of the form of interaction, which includes adapting action choices and possibly its own goals.While most people take these aspects of interaction for granted, they are complex and involve many specific tasks that have primarily been studied independently within artificial intelligence. This results in open-loop interactive experiences where the user must perform a fixed input command or the intelligent system performs a hard-coded output response---one of the components of the interaction cannot adapt with respect to the other for longer-term back-and-forth interactions. This dissertation explores how developments in plan recognition, activity recognition, intent recognition, and autonomous planning can work together to develop more adaptive interactive experiences between autonomous intelligent systems and the people around them. In particular, we consider a unifying perspective of recognition algorithms that provides sufficient information to dynamically produce short-term automated planning problems, and we present ways to run these algorithms faster for the real-time needs of interaction. This exploration leads to the introduction of the Planning and Recognition Together Close the Interaction Loop (PReTCIL) framework that serves as a first step towards identifying how we can address the problem of closing the interaction loop, in addition to new questions that need to be considered

A new methodology for modelling urban soundscapes: a psychometric revisitation of the current standard and a Bayesian approach for individual response prediction

Measuring how the urban sound environment is perceived by public space users, which is usually referred as urban soundscape, is a research field of particular in terest for a broad and multidisciplinary scientific community besides private and public agencies. The need for a tool to quantify soundscapes would provide much support to urban planning and design, so to public healthcare. Soundscape liter ature still does not show a unique strategy for addressing this topic. Soundscape definition, data collection, and analysis tools have been recently standardised and published in three respective ISO (International Organisation for Standardization) items. In particular, the third item of the ISO series defines the calculation of the soundscape experience of public space users by means of multiple Likert scales. In this thesis, with regards to the third item of the soundscape ISO series, the soundscape data analysis standard method is questioned and a correction paradigm is proposed. This thesis questiones the assumption of a point-wise superimposition match across the Likert scales used during the soundscape assessment task. In order to do that, the thesis presents a new method which introduces correction values, or metric, for adjusting the scales in accordance to the results of common scaling behaviours found across the investigated locations. In order to validate the results, the outcome of the new metric is used as tar get to predict the individual experience of soundscapes from the participants. In comparison to the current ISO output, the new correction values reveal to achievea better predictability in both linear and non-linear modelling by increasing the ac-curacy of prediction of individual responses up to 52.6% (8.3% higher than theaccuracy obtained with the standard method).Finally, the new metric is used to validate the collection of data samples acrossseveral locations on individual questionnaires responses. Models are trained, in aiterative way, on all the locations except the one used during the validation. Thisprocedure provides a strong validating framework for predicting individual subjectassessments belonging to locations totally unseen during the model training. The results show that the combination of the new metrics with the proposed modelling structure achieves good performance on individual responses across the dataset withan average accuracy above 54%. A new index for measuring the soundscape is fi-nally introduced based on the percentage of people agreeing on soundscape pleas-antness calculated from the new proposed metric and performing a r-squared valueequals to 0.87.The framework introduced is limited by cultural and linguistic factors. Indeed,different corrected metric space are expected to be found when data is collected from different countries or urban context. The current values found in this thesis areso expected to be valid in large British cities and eventually in international hub andcapital cities. In these scenarios the corrected metric would provide a more realisticand direction-invariant representation of how the urban soundscape is perceived compared to the current ISO tool, showing that some components in the circumplex model are perceived softer or stronger according to the dimension. Future research will need to understand better the limitations of this new ramework and to extendand compare it towards different urban, cultural, and linguistic contexts

Evolutionary Computation 2020

Intelligent optimization is based on the mechanism of computational intelligence to refine a suitable feature model, design an effective optimization algorithm, and then to obtain an optimal or satisfactory solution to a complex problem. Intelligent algorithms are key tools to ensure global optimization quality, fast optimization efficiency and robust optimization performance. Intelligent optimization algorithms have been studied by many researchers, leading to improvements in the performance of algorithms such as the evolutionary algorithm, whale optimization algorithm, differential evolution algorithm, and particle swarm optimization. Studies in this arena have also resulted in breakthroughs in solving complex problems including the green shop scheduling problem, the severe nonlinear problem in one-dimensional geodesic electromagnetic inversion, error and bug finding problem in software, the 0-1 backpack problem, traveler problem, and logistics distribution center siting problem. The editors are confident that this book can open a new avenue for further improvement and discoveries in the area of intelligent algorithms. The book is a valuable resource for researchers interested in understanding the principles and design of intelligent algorithms