DORA :: Browsing by Author "Neri, Ferrante"

Browsing by Author "Neri, Ferrante"

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Adaptive Differential Evolution Applied to Point Matching 2D GIS Data
(IEEE, 2015-12-07) Khan, N.; Neri, Ferrante; Ahmadi, Samad
The impetus behind data analytics and integration is the need for greater insight and data visibility, but since a growing share of our data is multimedia, there is a parallel need for methods that can align multimedia data. This paper explores georeferencing, which is used to combine spatial datasets and used here to align map images to 2D GIS models. This paper surveys various approaches for building the key components of a georeferencing solution, notes their strengths and weaknesses, and comments on their trajectory to help orient future work. The implementation presented here uses Hough transforms for feature detection, nearest neighbor correspondences with simplistic similarity measures, and a population based optimizer. The comparison among metaheuristics has shown that Differential Evolution (DE) frameworks appear especially suited for this problem. In particular, the controlled randomization of DE parameters appears to display the best performance in terms of execution time and competitive performance in terms of function evaluations even with respect to more complex memetic implementations.
Embargo
An Adaptive Local Search Algorithm for Real-Valued Dynamic Optimization
(IEEE Press, 2015-05) Mavrovouniotis, Michalis; Neri, Ferrante; Yang, Shengxiang
This paper proposes a novel adaptive local search algorithm for tackling real-valued (or continuous) dynamic optimization problems. The proposed algorithm is a simple single-solution based metaheuristic that perturbs the variables separately to select the search direction for the following step and adapts its step size to the gradient. The search directions that appear to be the most promising are rewarded by a step size increase while the unsuccessful moves attempt to reverse the search direction with a reduced step size. When the environment is subject to changes, a new solution is sampled and crosses over the best solution in the previous environment. Furthermore, the algorithm makes use of a small archive where the best solutions are saved. Experimental results show that the proposed algorithm, despite its simplicity, is competitive with complex population-based algorithms for tested dynamic optimization problems.
Open Access
Algorithm Design Issues in Adaptive Differential Evolution: Review and taxonomy
(Elsevier, 2018-05-09) Al-Dabbagh, R.D.; Neri, Ferrante; Idris, N.; Baba, M.S.
The performance of most metaheuristic algorithms depends on parameters whose settings essentially serve as a key function in determining the quality of the solution and the efficiency of the search. A trend that has emerged recently is to make the algorithm parameters automatically adapt to different problems during optimization, thereby liberating the user from the tedious and time-consuming task of manual setting. These fine-tuning techniques continue to be the object of ongoing research. Differential evolution (DE) is a simple yet powerful population-based metaheuristic. It has demonstrated good convergence, and its principles are easy to understand. DE is very sensitive to its parameter settings and mutation strategy; thus, this study aims to investigate these settings with the diverse versions of adaptive DE algorithms. This study has two main objectives: (1) to present an extension for the original taxonomy of evolutionary algorithms (EAs) parameter settings that has been overlooked by prior research and therefore minimize any confusion that might arise from the former taxonomy and (2) to investigate the various algorithmic design schemes that have been used in the different variants of adaptive DE and convey them in a new classification style. In other words, this study describes in depth the structural analysis and working principle that underlie the promising and recent work in this field, to analyze their advantages and disadvantages and to gain future insights that can further improve these algorithms. Finally, the interpretation of the literature and the comparative analysis of the results offer several guidelines for designing and implementing adaptive DE algorithms. The proposed design framework provides readers with the main steps required to integrate any proposed meta-algorithm into parameter and/or strategy adaptation schemes.
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An analysis on separability for Memetic Computing automatic design
(Elsevier, 2014-01-07) Caraffini, Fabio; Neri, Ferrante; Picinali, Lorenzo
Open Access
Artificial compound eye: a survey of the state-of-the-art
(Springer, 2016-09-01) Wu, Sidong; Jiang, Tao; Zhang, Gexiang; Schoenemann, Brigitte; Neri, Ferrante; Zhu, Ming; Bu, Chunguang; Han, Jianda; Kuhnert, Klaus-Dieter
An artificial compound eye system is the bionic system of natural compound eyes with much wider field-of-view, better capacity to detect moving objects and higher sensitivity to light intensity than ordinary single-aperture eyes. In recent years, renewed attention has been paid to the artificial compound eyes, due to their better characteristics inheriting from insect compound eyes than ordinary optical imaging systems. This paper provides a comprehensive survey of the state-of-the-art work on artificial compound eyes. This review starts from natural compound eyes to artificial compound eyes including their system design, theoretical development and applications. The survey of artificial compound eyes is developed in terms of two main types: planar and curved artificial compound eyes. Finally, the most promising future research developments are highlighted.
Open Access
Cloud-Assisted Secure eHealth Systems for Tamper-Proofing EHR via Blockchain
(Elsevier, 2019-02-14) Cao, S.; Zhang, G.; Liu, P.; Zhang, X.; Neri, Ferrante
The wide deployment of cloud-assisted electronic health (eHealth) systems has already shown great benefits in managing electronic health records (EHRs) for both medical institutions and patients. However, it also causes critical security concerns. Since once a medical institution generates and outsources the patients' EHRs to cloud servers, patients would not physically own their EHRs but the medical institution can access the EHRs as needed for diagnosing, it makes the EHRs integrity protection a formidable task, especially in the case that a medical malpractice occurs, where the medical institution may collude with the cloud server to tamper with the outsourced EHRs to hide the medical malpractice. Traditional cryptographic primitives for the purpose of data integrity protection cannot be directly adopted because they cannot ensure the security in the case of collusion between the cloud server and medical institution. In this paper, a secure cloud-assisted eHealth system is proposed to protect outsourced EHRs from illegal modification by using the blockchain technology (blockchain-based currencies, e.g., Ethereum). The key idea is that the EHRs only can be outsourced by authenticated participants and each operation on outsourcing EHRs is integrated into the public blockchain as a transaction. Since the blockchain-based currencies provide a tamper-proofing way to conduct transactions without a central authority, the EHRs cannot be modified after the corresponding transaction is recorded into the blockchain. Therefore, given outsourced EHRs, any participant can check their integrity by checking the corresponding transaction. Security analysis and performance evaluation demonstrate that the proposed system can provide a strong security guarantee with a high efficiency.
Embargo
Cluster-Based Population Initialization for differential evolution frameworks
(Elsevier, 2014-11-15) Poikolainen, Ilpo.; Neri, Ferrante; Caraffini, Fabio
Abstract This article proposes a procedure to perform an intelligent initialization for population-based algorithms. The proposed pre-processing procedure, namely Cluster-Based Population Initialization (CBPI) consists of three consecutive stages. At the first stage, the individuals belonging to a randomly sampled population undergo two subsequent local search algorithms, i.e. a simple local search that performs moves along the axes and Rosenbrock algorithm. At the second stage, the solutions processed by the two local searches undergo the K-means clustering algorithm and are grouped into sets on the basis of their euclidean distance. At the third stage the best individuals belonging to each cluster are saved into the initial population of a generic optimization algorithm. If the population has not been yet filled, the other individuals of the population are sampled within the clusters by using a fitness-based probabilistic criterion. This three stage procedure implicitly performs an initial screening of the problem features in order to roughly estimate the most interesting regions of the decision space. The proposed \{CBPI\} has been tested on multiple classical and modern Differential Evolution variants, on a wide array of test problems and dimensionality values as well as on a real-world problem. The proposed intelligent sampling appears to have a significant impact on the algorithmic functioning as it consistently enhances the performance of the algorithms with which it is integrated.
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A CMA-ES Super-fit Scheme for the Re-sampled Inheritance Search
(IEEE, 2013) Caraffini, Fabio; Iacca, Giovanni; Neri, Ferrante; Picinali, Lorenzo; Mininno, Ernesto
Open Access
Coder Source Code
(2021-10) Yuan, Hui; Hamzaoui, Raouf; Neri, Ferrante; Yang, Shengxiang
Point clouds are representations of three-dimensional (3D) objects in the form of a sample of points on their surface. Point clouds are receiving increased attention from academia and industry due to their potential for many important applications, such as real-time 3D immersive telepresence, automotive and robotic navigation, as well as medical imaging. Compared to traditional video technology, point cloud systems allow free viewpoint rendering, as well as mixing of natural and synthetic objects. However, this improved user experience comes at the cost of increased storage and bandwidth requirements as point clouds are typically represented by the geometry and colour (texture) of millions up to billions of 3D points. For this reason, major efforts are being made to develop efficient point cloud compression schemes. However, the task is very challenging, especially for dynamic point clouds (sequences of point clouds), due to the irregular structure of point clouds (the number of 3D points may change from frame to frame, and the points within each frame are not uniformly distributed in 3D space). To standardize point cloud compression (PCC) technologies, the Moving Picture Experts Group (MPEG) launched a call for proposals in 2017. As a result, three point cloud compression technologies were developed: surface point cloud compression (S-PCC) for static point cloud data, video-based point cloud compression (V-PCC) for dynamic content, and LIDAR point cloud compression (L-PCC) for dynamically acquired point clouds. Later, L-PCC and S-PCC were merged under the name geometry-based point cloud compression (G-PCC). The aim of the OPT-PCC project is to develop algorithms that optimise the rate-distortion performance [i.e., minimize the reconstruction error (distortion) for a given bit budget] of V-PCC. The objectives of the project are to: 1. O1: build analytical models that accurately describe the effect of the geometry and colour quantization of a point cloud on the bit rate and distortion; 2. O2: use O1 to develop fast search algorithms that optimise the allocation of the available bit budget between the geometry information and colour information; 3. O3: implement a compression scheme for dynamic point clouds that exploits O2 to outperform the state-of-the-art in terms of rate-distortion performance. The target is to reduce the bit rate by at least 20% for the same reconstruction quality; 4. O4: provide multi-disciplinary training to the researcher in algorithm design, metaheuristic optimisation, computer graphics, media production, and leadership and management skills. As part of O3, this deliverable gives the source code of the algorithms used in the project to optimize the rate-distortion performance of V-PCC.
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Compact Differential Evolution
(2011) Mininno, Ernesto; Neri, Ferrante; Cupertino, Francesco; Naso, David
This paper proposes the compact differential evolution (cDE) algorithm. cDE, like other compact evolutionary algorithms, does not process a population of solutions but its statistic description which evolves similarly to all the evolutionary algorithms. In addition, cDE employs the mutation and crossover typical of differential evolution (DE) thus reproducing its search logic. Unlike other compact evolutionary algorithms, in cDE, the survivor selection scheme of DE can be straightforwardly encoded. One important feature of the proposed cDE algorithm is the capability of efficiently performing an optimization process despite a limited memory requirement. This fact makes the cDE algorithm suitable for hardware contexts characterized by small computational power such as micro-controllers and commercial robots. In addition, due to its nature cDE uses an implicit randomization of the offspring generation which corrects and improves the DE search logic. An extensive numerical setup has been implemented in order to prove the viability of cDE and test its performance with respect to other modern compact evolutionary algorithms and state-of-the-art population-based DE algorithms. Test results show that cDE outperforms on a regular basis its corresponding population-based DE variant. Experiments have been repeated for four different mutation schemes. In addition cDE outperforms other modern compact algorithms and displays a competitive performance with respect to state-of-the-art population-based algorithms employing a DE logic. Finally, the cDE is applied to a challenging experimental case study regarding the on-line training of a nonlinear neuralnetwork-based controller for a precise positioning system subject to changes of payload. The main peculiarity of this control application is that the control software is not implemented into a computer connected to the control system but directly on the micro-controller. Both numerical results on the test functions and experimental results on the real-world problem are very promising and allow us to think that cDE and future developments can be an efficient option for optimization in hardware environments characterized by limited memory.
Embargo
Compact differential evolution light: high performance despite limited memory requirement and modest computational overhead
(Springer US, 2012-09-01) Iacca, Giovanni; Caraffini, Fabio; Neri, Ferrante
Compact algorithms are Estimation of Distribution Algorithms which mimic the behavior of population-based algorithms by means of a probabilistic representation of the population of candidate solutions. These algorithms have a similar behaviour with respect to population-based algorithms but require a much smaller memory. This feature is crucially important in some engineering applications, especially in robotics. A high performance compact algorithm is the compact Differential Evolution (cDE) algorithm. This paper proposes a novel implementation of cDE, namely compact Differential Evolution light (cDElight), to address not only the memory saving necessities but also real-time requirements. cDElight employs two novel algorithmic modifications for employing a smaller computational overhead without a performance loss, with respect to cDE. Numerical results, carried out on a broad set of test problems, show that cDElight, despite its minimal hardware requirements, does not deteriorate the performance of cDE and thus is competitive with other memory saving and population-based algorithms. An application in the field of mobile robotics highlights the usability and advantages of the proposed approach.
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Compact particle swarm optimization
(Elsevier, 2013) Neri, Ferrante; Mininno, Ernesto; Iacca, Giovanni
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Composed compact differential evolution
(Springer, 2011-03) Iacca, Giovanni; Mininno, Ernesto; Neri, Ferrante
This paper proposes a novel algorithm for solving continuous complex optimization problems with a relatively low memory consumption. The proposed approach, namely Composed compact Differential Evolution, consists of a set of compact Differential Evolution units which simultaneously search the decision space from various perspectives. A randomization in the virtual population allows the algorithm to behave, on one hand, as a multiple local search with a multi-start logic integrated within it. On the other hand, the compact units communicate among each other by means of a ring topology and propagation of information. More specifically, the most promising elite solutions and scale factor values of each compact unit are migrated to the neighbour unit so that the search of the global optimum is performed. In other words, while each single compact unit performs a local search by exploiting the direction suggested by each elite solution, the entire structure combines the achievement of each local search operation towards the direction of the global search. The proposed algorithm is characterized by a limited memory consumption and is memory-wise equivalent to a population-based algorithm with a small population. Numerical results show that the proposed approach outperforms other compact algorithms and various modern population-based structures.
Embargo
Continuous Parameter Pools in Ensemble Differential Evolution
(IEEE, 2015-12) Iacca, Giovanni; Caraffini, Fabio; Neri, Ferrante
Ensemble of parameters and mutation strategies differential evolution (EPSDE) is an elegant promising optimization framework based on the idea that a pool of mutation and crossover strategies along, with associated pools of parameter settings, can flexibly adapt to a large variety of problems when a simple success based rule is introduced. Modern versions of this scheme successfully attempts to improve upon the original performance at the cost of a high complexity. One of most successful implementations of this algorithmic scheme is the Self-adaptive Ensemble of Parameters and Strategies Differential Evolution (SaEPSDE). This paper operates on the SaEPSDE, reducing its complexity by identifying some algorithmic components that we experimentally show as possibly unnecessary. The result of this de-constructing operation is a novel algorithm implementation, here referred to as "j" Ensemble of Strategies Differential Evolution (jESDE). The proposed implementation is drastically simpler than SaEPSDE as several parts of it have been removed or simplified. Nonetheless, jESDE appears to display a competitive performance, on diverse problems throughout various dimensionality values, with respect to the original EPSDE algorithm, as well as to SaEPSDE and three modern algorithms based on Differential Evolution.
Open Access
Covariance Matrix Adaptation Pareto Archived Evolution Strategy with Hypervolume-sorted Adaptive Grid Algorithm
(IOS Press, 2016-09-20) Rostami, Shahin; Neri, Ferrante
Real-world problems often involve the optimisation of multiple conflicting objectives. These problems, referred to as multi-objective optimisation problems, are especially challenging when more than three objectives are considered simultaneously. This paper proposes an algorithm to address this class of problems. The proposed algorithm is an evolutionary algorithm based on an evolution strategy framework, and more specifically, on the Covariance Matrix Adaptation Pareto Archived Evolution Strategy (CMA-PAES). A novel selection mechanism is introduced and integrated within the framework. This selection mechanism makes use of an adaptive grid to perform a local approximation of the hypervolume indicator which is then used as a selection criterion. The proposed implementation, named Covariance Matrix Adaptation Pareto Archived Evolution Strategy with Hypervolume-sorted Adaptive Grid Algorithm (CMA-PAES-HAGA), overcomes the limitation of CMA-PAES in handling more than two objectives and displays a remarkably good performance on a scalable test suite in five, seven, and ten-objective problems. The performance of CMA-PAES-HAGA has been compared with that of a competition winning meta-heuristic, representing the state-of-the-art in this sub-field of multi-objective optimisation. The proposed algorithm has been tested in a seven-objective real-world application, i.e. the design of an aircraft lateral control system. In this optimisation problem, CMA-PAES-HAGA greatly outperformed its competitors.
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Design and implementation of membrane controllers for trajectory tracking of nonholonomic wheeled mobile robots
(IOS Press, 2016) Wang, X.; Zhang, G.; Neri, Ferrante; Jiang, T.; Zhao, J.; Gheorghe, M.; Ipate, F.; Lefticaru, R.
This paper proposes a novel trajectory tracking control approach for nonholonomic wheeled mobile robots. In this approach, the integration of feed-forward and feedback controls is presented to design the kinematic controller of wheeled mobile robots, where the control law is constructed on the basis of Lyapunov stability theory, for generating the precisely desired velocity as the input of the dynamic model of wheeled mobile robots; a proportional-integral-derivative based membrane controller is introduced to design the dynamic controller of wheeled mobile robots to make the actual velocity follow the desired velocity command. The proposed approach is defined by using an enzymatic numerical membrane system to integrate two proportional-integral-derivative controllers, where neural networks and experts' knowledge are applied to tune parameters. Extensive experiments conducted on the simulated wheeled mobile robots show the effectiveness of this approach.
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A Differential Evolution for Optimisation in Noisy Environment
(Inderscience, 2010-05) Neri, Ferrante; Caponio, A.
This paper proposes a novel variant of differential evolution (DE) tailored to the optimisation of noisy fitness functions. The proposed algorithm, namely noise analysis differential evolution (NADE), combines the stochastic properties of a randomised scale factor and a statistically rigorous test which supports one-to-one spawning survivor selection that automatically selects a proper sample size and then selects, among parent and offspring, the most promising solution. The actions of these components are separately analysed and their combined effect on the algorithmic performance is studied by means of a set of numerous and various test functions perturbed by Gaussian noise. Various noise amplitudes are considered in the result section. The performance of the NADE has been extensively compared with a classical algorithm and two modern metaheuristics designed for optimisation in the presence of noise. Numerical results show that the proposed NADE has very good performance with most of the problems considered in the benchmark set. The NADE seems to be able to detect high quality solutions despite the noise and display high performance in terms of robustness.
Open Access
A Differential Evolution Framework with Ensemble of Parameters and Strategies and Pool of Local Search Algorithms
(Springer Berlin Heidelberg, 2014-11) Iacca, Giovanni; Neri, Ferrante; Caraffini, Fabio; Suganthan, Ponnuthurai Nagaratnam
The ensemble structure is a computational intelligence supervised strategy consisting of a pool of multiple operators that compete among each other for being selected, and an adaptation mechanism that tends to reward the most successful operators. In this paper we extend the idea of the ensemble to multiple local search logics. In a memetic fashion, the search structure of an ensemble framework cooperatively/competitively optimizes the problem jointly with a pool of diverse local search algorithms. In this way, the algorithm progressively adapts to a given problem and selects those search logics that appear to be the most appropriate to quickly detect high quality solutions. The resulting algorithm, namely Ensemble of Parameters and Strategies Differential Evolution empowered by Local Search (EPSDE-LS), is evaluated on multiple testbeds and dimensionality values. Numerical results show that the proposed EPSDE-LS robustly displays a very good performance in comparison with some of the state-of-the-art algorithms.
Embargo
Differential Evolution Schemes for Speech Segmentation: A Comparative Study
(IEEE Press, 2014) Iliya, Sunday; Neri, Ferrante; Menzies, Dylan; Cornelius, Pip; Picinali, Lorenzo
This paper presents a signal processing technique for segmenting short speech utterances into unvoiced and voiced sections and identifying points where the spectrum becomes steady. The segmentation process is part of a system for deriving musculoskeletal articulation data from disordered utterances, in order to provide training feedback. The functioning of the signal processing technique has been optimized by selecting the parameters of the model. The optimization has been carried out by testing and comparing multiple Differential Evolution implementations, including a standard one, a memetic one, and a controlled randomized one. Numerical results have also been compared with a famous and efficient swarm intelligence algorithm. For the given problem, Differential Evolution schemes appear to display a very good performance as they can quickly reach a high quality solution. The binomial crossover appears, for the given problem, beneficial with respect to the exponential one. The controlled randomization appears to be the best choice in this case. The overall optimized system proved to segment well the speech utterances and efficiently detect its uninteresting parts.
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Differential Evolution with Scale Factor Local Search for Large Scale Problems
(SpringerLink, 2010) Caponio, A.; Kononova, A.V.; Neri, Ferrante
This chapter proposes the integration of fitness diversity adaptation techniques within the parameter setting of Differential Evolution (DE). The scale factor and crossover rate are encoded within each genotype and self-adaptively updated during the evolution by means of a probabilistic criterion which takes into account the diversity properties of the entire population. The population size is also adaptively controlled by means of a novel technique based on a measurement of the fitness diversity. An extensive experimental setup has been implemented by including multivariate problems and hard to solve fitness landscapes. A comparison of the performance has been conducted by considering a standard DE as well as modern DE based algorithms, recently proposed in literature. Numerical results available show that the proposed approach seems to be very promising for some fitness landscapes and still competitive with modern algorithms in other cases. In most cases analyzed the proposed self-adaptation is beneficial in terms of algorithmic performance and can be considered a useful tool for enhancing the performance of a DE scheme.

Browsing by Author "Neri, Ferrante"

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