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Dijkstra P.,3 Innovation Center for Advanced Sensors and Sensor Systems | Dijkstra P.,University of Groningen | Angelone D.,University of Groningen | Talnishnikh E.,3 Innovation Center for Advanced Sensors and Sensor Systems | And 3 more authors.
Dalton Transactions | Year: 2014

The detection of nuclear radiation necessitates the availability of new generations of tunable blue emitting fluorophores with high emission quantum yields. Here we show that pyridyl-1,2,4-triazole based diphenyl boron complexes can provide highly tuneable emission through facile modification of the C5 position of the 1,2,4-triazolato ring. The series of complexes prepared show a wide range of emission from near-UV to green, enabling fine control over the spectral overlap with detectors used in scintillator technology. This journal is © The Royal Society of Chemistry.


Neri F.,De Montfort University | Neri F.,University of Jyväskylä | Mininno E.,University of Jyväskylä | Iacca G.,3 Innovation Center for Advanced Sensors and Sensor Systems
Information Sciences | Year: 2013

Some real-world optimization problems are plagued by a limited hardware availability. This situation can occur, for example, when the optimization must be performed on a device whose hardware is limited due to cost and space limitations. This paper addresses this class of optimization problems and proposes a novel algorithm, namely compact Particle Swarm Optimization (cPSO). The proposed algorithm employs the search logic typical of Particle Swarm Optimization (PSO) algorithms, but unlike classical PSO algorithms, does not use a swarm of particles and does not store neither the positions nor the velocities. On the contrary, cPSO employs a probabilistic representation of the swarm's behaviour. This representation allows a modest memory usage for the entire algorithmic functioning, the amount of memory used is the same as what is needed for storing five solutions. A novel interpretation of compact optimization is also given in this paper. Numerical results show that cPSO appears to outperform other modern algorithms of the same category (i.e. which attempt to solve the optimization despite a modest memory usage). In addition, cPSO displays a very good performance with respect to its population-based version and a respectable performance also with respect to some more complex population-based algorithms. A real world application in the field of power engineering and energy generation is given. The presented case study shows how, on a model of an actual power plant, an advanced control system can be online and real-time optimized. In this application example the calculations are embedded directly on the real-time control system. © 2013 Elsevier Inc. All rights reserved.


Caraffini F.,De Montfort University | Caraffini F.,University of Jyväskylä | Neri F.,De Montfort University | Neri F.,University of Jyväskylä | And 2 more authors.
Information Sciences | Year: 2013

Memetic Computing (MC) structures are algorithms composed of heterogeneous operators (memes) for solving optimization problems. In order to address these problems, this study investigates and proposes a simple yet extremely efficient structure, namely Parallel Memetic Structure (PMS). PMS is a single solution optimization algorithm composed of tree operators, the first one being a stochastic global search which explores the entire decision space searching for promising regions. In analogy with electrical networks, downstream of the global search component there is a parallel of two alternative elements, i.e. two local search algorithms with different features in terms of search logic, whose purpose is to refine the search in the regions detected by the upstream element. The first local search explores the space along the axes, while the second performs diagonal movements in the direction of the estimated gradient. The PMS algorithm, despite its simplicity, displays a respectable performance compared to that of popular meta-heuristics and modern optimization algorithms representing the state-of-the-art in the field. Thanks to its simple structure, PMS appears to be a very flexible algorithm for various problem features and dimensionality values. Unlike modern complex algorithm that are specialized for some benchmarks and some dimensionality values, PMS achieves solutions with a high quality in various and diverse contexts, for example both on low dimensional and large scale problems. An application example in the field of magnetic sensors further proves the potentials of the proposed approach. This study confirms the validity of the Ockham's Razor in MC: efficiently designed simple structures can perform as well as (if not better than) complex algorithms composed of many parts. © 2012 Elsevier Inc. All rights reserved.


Caraffini F.,De Montfort University | Caraffini F.,University of Jyväskylä | Neri F.,De Montfort University | Neri F.,University of Jyväskylä | And 2 more authors.
Soft Computing | Year: 2013

This paper proposes re-sampled inheritance search (RIS), a novel algorithm for solving continuous optimization problems. The proposed method, belonging to the class of Memetic Computing, is very simple and low demanding in terms of memory employment and computational overhead. The RIS algorithm is composed of a stochastic sample mechanism and a deterministic local search. The first operator randomly generates a solution and then recombines it with the best solution detected so far (inheritance) while the second operator searches in an exploitative way within the neighbourhood indicated by the stochastic operator. This extremely simple scheme is shown to display a very good performance on various problems, including hard to solve multi-modal, highly-conditioned, large scale problems. Experimental results show that the proposed RIS is a robust scheme that competitively performs with respect to recent complex algorithms representing the-state-of-the-art in modern continuous optimization. In order to further prove its applicability in real-world cases, RIS has been used to perform the control system tuning for yaw operations on a helicopter robot. Experimental results on this real-world problem confirm the value of the proposed approach. © 2013 Springer-Verlag Berlin Heidelberg.


Iacca G.,3 Innovation Center for Advanced Sensors and Sensor Systems | Caraffini F.,De Montfort University | Caraffini F.,University of Jyväskylä | Neri F.,De Montfort University | And 2 more authors.
2013 IEEE Congress on Evolutionary Computation, CEC 2013 | Year: 2013

This paper introduces two lightweight variants of ISPO, a Single Particle Optimization algorithm recently proposed in the literature. The goal of this work is to improve upon the performance of the original ISPO, still bearing in mind its admirable algorithmic simplicity. The first variant, namely ISPO-restart, combines in a memetic fashion the logics of ISPO with a partial restart mechanism similar to the binomial crossover typically used in Differential Evolution. The second variant, named VISPO, builds on top of the restart process a very simple learning stage which tries to adapt the algorithm behaviour to the (non)-separability of the problem. Numerical results obtained on three complete optimization benchmarks show that not only the two algorithms are able to improve, incrementally, upon the performance of ISPO, but also they show respectable performance in comparison with modern complex state-of-the-art methods, especially when the problem dimensionality increases. © 2013 IEEE.


Iacca G.,3 Innovation Center for Advanced Sensors and Sensor Systems | Caraffini F.,De Montfort University | Caraffini F.,University of Jyväskylä | Neri F.,De Montfort University | Neri F.,University of Jyväskylä
Applied Soft Computing Journal | Year: 2013

In this paper, a recently proposed single-solution memetic computing optimization method, namely three stage optimization memetic exploration (3SOME), is used to implement a self-tuning PID controller on board of a mobile robot. More specifically, the optimal PID parameters minimizing a measure of the following error on a path-following operation are found, in real-time, during the execution of the control loop. The proposed approach separates the control and the optimization tasks, and uses simple operating system primitives to share data. The system is able to react to modifications of the trajectory, thus endowing the robot with intelligent learning and self-configuration capabilities. A popular commercial robotic tool, i.e. the Lego Mindstorms robot, has been used for testing and implementing this system. Tests have been performed both in simulations and in a real Lego robot. Experimental results show that, compared to other online optimization techniques and to empiric PID tuning procedures, 3SOME guarantees a robust and efficient control behaviour, thus representing a valid alternative for self-tuning control systems. © 2012 Elsevier B.V. All rights reserved.


Iacca G.,3 Innovation Center for Advanced Sensors and Sensor Systems | Caraffini F.,De Montfort University | Neri F.,De Montfort University
International Journal of Neural Systems | Year: 2014

We propose Multi-Strategy Coevolving Aging Particles (MS-CAP), a novel population-based algorithm for black-box optimization. In a memetic fashion, MS-CAP combines two components with complementary algorithm logics. In the first stage, each particle is perturbed independently along each dimension with a progressively shrinking (decaying) radius, and attracted towards the current best solution with an increasing force. In the second phase, the particles are mutated and recombined according to a multi-strategy approach in the fashion of the ensemble of mutation strategies in Differential Evolution. The proposed algorithm is tested, at different dimensionalities, on two complete black-box optimization benchmarks proposed at the Congress on Evolutionary Computation 2010 and 2013. To demonstrate the applicability of the approach, we also test MS-CAP to train a Feedforward Neural Network modeling the kinematics of an 8-link robot manipulator. The numerical results show that MS-CAP, for the setting considered in this study, tends to outperform the state-of-the-art optimization algorithms on a large set of problems, thus resulting in a robust and versatile optimizer. © 2014 World Scientific Publishing Company.


Iacca G.,3 Innovation Center for Advanced Sensors and Sensor Systems
2012 12th UK Workshop on Computational Intelligence, UKCI 2012 | Year: 2012

Driven by a broad range of applications, the Computational Intelligence research community has recently put a growing interest on the emergent technology of Wireless Sensor Networks (WSNs). Due to their distributed structure, WSNs pose several technical challenges caused by local failures, network issues and severely constrained hardware resources. Nevertheless, the possibility to perform an online optimization within WSNs is appealing since it might lead the path to advanced network features like intelligent sensing, distributed modelling, self-optimizing protocols, anomaly detection, etc. just to name a few. In this paper we present DOWSN, a novel Distributed Optimization framework for WSNs. Based on an island model, DOWSN is characterized by a peer-to-peer infrastructure in which each node executes an optimization process and shares pieces of information, i.e. local achievements, with its neighbors. Preliminary experiments show that DOWSN is able to efficiently exploit the communication capabilities and the inherently parallel nature of WSNs, thus finding optimal solutions fast and reliably. © 2012 IEEE.


PubMed | 3 Innovation Center for Advanced Sensors and Sensor Systems
Type: Journal Article | Journal: Dalton transactions (Cambridge, England : 2003) | Year: 2014

The detection of nuclear radiation necessitates the availability of new generations of tunable blue emitting fluorophores with high emission quantum yields. Here we show that pyridyl-1,2,4-triazole based diphenyl boron complexes can provide highly tuneable emission through facile modification of the C5 position of the 1,2,4-triazolato ring. The series of complexes prepared show a wide range of emission from near-UV to green, enabling fine control over the spectral overlap with detectors used in scintillator technology.


PubMed | 3 Innovation Center for Advanced Sensors and Sensor Systems
Type: Journal Article | Journal: International journal of neural systems | Year: 2013

We propose Multi-Strategy Coevolving Aging Particles (MS-CAP), a novel population-based algorithm for black-box optimization. In a memetic fashion, MS-CAP combines two components with complementary algorithm logics. In the first stage, each particle is perturbed independently along each dimension with a progressively shrinking (decaying) radius, and attracted towards the current best solution with an increasing force. In the second phase, the particles are mutated and recombined according to a multi-strategy approach in the fashion of the ensemble of mutation strategies in Differential Evolution. The proposed algorithm is tested, at different dimensionalities, on two complete black-box optimization benchmarks proposed at the Congress on Evolutionary Computation 2010 and 2013. To demonstrate the applicability of the approach, we also test MS-CAP to train a Feedforward Neural Network modeling the kinematics of an 8-link robot manipulator. The numerical results show that MS-CAP, for the setting considered in this study, tends to outperform the state-of-the-art optimization algorithms on a large set of problems, thus resulting in a robust and versatile optimizer.

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