This paper presents an optimal radome design using particle swarm optimization (PSO) which has recently drawn considerable attention in a wide range of applications. The frequency characteristics of the transmission c...
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This paper presents an optimal radome design using particle swarm optimization (PSO) which has recently drawn considerable attention in a wide range of applications. The frequency characteristics of the transmission coefficient for the radome are adopted as the objective function, and the radome wall thickness and radome shape are optimized. Furthermore, in order to enhance the reliability of the original PSO, we introduce a concept analogous to 'mutation' in genetic algorithms. Numerical experiments reveal that the mutation operation is capable of considerably enhancing the stability and the reliability of the PSO. We also confirm that the PSO is successfully applied to the problem of a practical radome design, and the transmission coefficient attained by the PSO is above the targeted value of -1 dB for the entire frequency bandwidth and for all evaluated beam scan angles. (c) 2012 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.
Barrier coverage with wireless sensors aims at detecting intruders who attempt to cross a specific area, where wireless sensors are distributed remotely at random. This paper considers limited-power sensors with adjus...
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Barrier coverage with wireless sensors aims at detecting intruders who attempt to cross a specific area, where wireless sensors are distributed remotely at random. This paper considers limited-power sensors with adjustable ranges deployed along a linear domain to form a barrier to detect intruding incidents. We introduce three objectives to minimize: 1) total power consumption while satisfying full coverage;2) the number of active sensors to improve the reliability;and 3) the active sensor nodes' maximum sensing range to maintain fairness. We refer to the problem as the tradeoff barrier coverage (TBC) problem. With the aim of obtaining a better tradeoff among the three objectives, we present a multiobjective optimization framework based on multiobjective evolutionary algorithm (MOEA)/D, which is called problem specific MOEA/D (PS-MOEA/D). Specifically, we define a 2-tuple encoding scheme and introduce a cover-shrink algorithm to produce feasible and relatively optimal solutions. Subsequently, we incorporate problem-specific knowledge into local search, which allows search procedures for neighboring subproblems collaborate each other. By considering the problem characteristics, we analyze the complexity and incorporate a strategy of computational resource allocation into our algorithm. We validate our approach by comparing with four competitors through several most-used metrics. The experimental results demonstrate that PS-MOEA/D is effective and outperforms the four competitors in all the cases, which indicates that our approach is promising in dealing with TBC.
The evolutionary design of time series forecasters is a field that has been explored for several years now. In this paper, a complete design and training of ARMA (Auto-Regressive Moving Average) and ANN (Artificial Ne...
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The evolutionary design of time series forecasters is a field that has been explored for several years now. In this paper, a complete design and training of ARMA (Auto-Regressive Moving Average) and ANN (Artificial Neural Networks) models through the use of evolutionary Computation is presented. That is, given a time series, our proposal (EDFM - evolutionary Design of Forecasting Models) qualitatively and quantitatively identifies a competitive model to perform the forecasting task. In the qualitative phase of the model identification, EDFM identifies the variables relevant to the process: i.e. the subset of variables, within a given window width, that provides the best forecasting, following the parsimony criterion. In the quantitative phase of the identification process, all free parameters are numerically instantiated;i.e. the coefficient of the ARMA models, or the ANN weights are determined. The results show that ANN yield better forecasts than ARMA models in all the cases presented in this paper. (C) 2012 Elsevier Ltd. All rights reserved.
In this paper, we assume that a team of drones equipped with sensing and networking capabilities explore an unknown area via onboard sensors for surveillance, monitoring, target search or data collection purposes and ...
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In this paper, we assume that a team of drones equipped with sensing and networking capabilities explore an unknown area via onboard sensors for surveillance, monitoring, target search or data collection purposes and deliver the sensed data to a ground control station (GCS) over multi -hop links. We propose a multidrone path planner that jointly optimizes area coverage time and connectivity among the drones. We propose a novel connectivity metric that includes not only percentage connectivity of the drones to GCS, but also the maximum duration of consecutive time that the drones are disconnected from the GCS. To solve this optimization formulation, we propose a multi -objective evolutionary algorithm with novel operations. We use our solver to test single, two and many objective path planning problems and compare our Pareto-optimal solutions to benchmark weighted -sum based solutions. We show that as opposed to the single solution that weighted -sum methods provide based on prior information from the user, the proposed evolutionary multiobjective optimizers can provide a diverse set of solutions that cover a range of mission time and connectivity performance illustrating the trade-off between these conflicting objectives. The end -user can then choose the best path solution based on the mission priorities during operation.
Purpose Impedance data obtained by electrochemical impedance spectroscopy (EIS) are fitted to a relevant electrical equivalent circuit to evaluate parameters directly related to the resistance and the durability of me...
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Purpose Impedance data obtained by electrochemical impedance spectroscopy (EIS) are fitted to a relevant electrical equivalent circuit to evaluate parameters directly related to the resistance and the durability of metal-coating systems. The purpose of this study is to present a novel and more efficient computational strategy for the modelling of EIS measurements using the Differential Evolution paradigm. Design/methodology/approach An alternative method to non-linear regression algorithms for the analysis of measured data in terms of equivalent circuit parameters is provided by evolutionary algorithms, particularly the Differential Evolution (DE) algorithms (standard DE and a representative of the self-adaptive DE paradigm were used). Findings The results obtained with DE algorithms were compared with those yielding from commercial fitting software, achieving a more accurate solution, and a better parameter identification, in all the cases treated. Further, an enhanced fitting power for the modelling of metal-coating systems was obtained. Originality/value The great potential of the developed tool has been demonstrated in the analysis of the evolution of EIS spectra due to progressive degradation of metal-coating systems. Open codes of the different differential algorithms used are included, and also, examples tackled in the document are open. It allows the complete use, or improvement, of the developed tool by researchers.
The use of simulation technology as a tool for planning and control is of increasing significance in most fields of production. The main part of the expenditure concerning simulation analyses is the modelling of the c...
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The use of simulation technology as a tool for planning and control is of increasing significance in most fields of production. The main part of the expenditure concerning simulation analyses is the modelling of the considered production. Despite the use of modern building-block-oriented modelling technology, this modelling can often not be done by the user, but only by external experts. Against this backdrop, an adaptive simulation system is being developed by the Institute for Industrial Manufacturing and Management (IFF) at the University of Stuttgart. It independently adapts to real production processes, i.e. it learns about the interdependencies of production processes, and, in this way, supports the user in constructing and maintaining the model. In terms of information technology, the research in the field of artificial intelligence, especially in the subdomain of machine learning, is the basis for the realization of such adaptive systems.
Traditional optimization solvers tend to start the search from scratch by assuming zero prior knowledge about the task at hand. Generally speaking, the capabilities of solvers do not automatically grow with experience...
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Traditional optimization solvers tend to start the search from scratch by assuming zero prior knowledge about the task at hand. Generally speaking, the capabilities of solvers do not automatically grow with experience. In contrast, however, humans routinely make use of a pool of knowledge drawn from past experiences whenever faced with a new task. This is often an effective approach in practice as real-world problems seldom exist in isolation. Similarly, practically useful artificial systems are expected to face a large number of problems in their lifetime, many of which will either he repetitive or share domain-specific similarities. This view naturally motivates advanced optimizers that mimic human cognitive capabilities;leveraging on what has been seen before to accelerate the search toward optimal solutions of never before seen tasks. With this in mind, this paper sheds light on recent research advances in the field of global black-box optimization that champion the theme of automatic knowledge transfer across problems. We introduce a general formalization of transfer optimization, based on which the conceptual realizations of the paradigm are classified into three distinct categories, namely sequential transfer, multitasking, and multiform optimization. In addition, we carry out a survey of different methodological perspectives spanning Bayesian optimization and nature-inspired computational intelligence procedures for efficient encoding and transfer of knowledge building blocks. Finally, real-world applications of the techniques are identified, demonstrating the future impact of optimization engines that evolve as better problem-solvers over time by learning from the past and from one another.
An optimization platform with multilevel structure, which is capable of efficiently solving design-optimization problems in aerodynamics, is proposed. The multilevel structure relies on a two-way regular exchange of i...
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An optimization platform with multilevel structure, which is capable of efficiently solving design-optimization problems in aerodynamics, is proposed. The multilevel structure relies on a two-way regular exchange of information between successive search levels. Each level can be associated with a different evaluation software, different problem parameterization and/or different search tool, for the minimization of the same objective function(s). The combination of some or all of the aforementioned strategies is possible, although beyond the scope of this paper. The basic optimization tool, which is associated with at least one of the levels, is a metamodel-assisted evolutionary algorithm;candidate solutions that have previously been examined are memorized and serve to train radial basis function networks, operating as local surrogate evaluation models. To handle multiobjective optimization problems with different search techniques at each level, we hybridize an evolutionary algorithm that computes the front of Pareto optimal solutions at the lower level(s) with a gradient-based method at the upper level, for the purpose of refinement through local search. In aerodynamic optimization, the adjoint method is used to compute the gradient of the objective function. For the gradient method to apply to a front of solutions rather than a single individual, an approximation of the SPEA-2 utility function gradient needs to be devised. The multilevel platform is demonstrated on mathematical problems as well as the design of isolated and compressor cascade airfoils. (C) 2008 Elsevier B.V. All rights reserved.
In this paper, the utilization of different chaotic systems as pseudo-random number generators (PRNGs) for velocity calculation in the PSO algorithm are proposed. Two chaos-based PRNGs are used alternately within one ...
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In this paper, the utilization of different chaotic systems as pseudo-random number generators (PRNGs) for velocity calculation in the PSO algorithm are proposed. Two chaos-based PRNGs are used alternately within one run of the PSO algorithm and dynamically switched over when a certain criterion is met. By using this unique technique, it is possible to improve the performance of PSO algorithm as it is demonstrated on different benchmark functions.
Most nature-inspired algorithms simulate intelligent behaviors of animals and insects that can move spontaneously and independently. The survival wisdom of plants, as another species of biology, has been neglected to ...
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Most nature-inspired algorithms simulate intelligent behaviors of animals and insects that can move spontaneously and independently. The survival wisdom of plants, as another species of biology, has been neglected to some extent even though they have evolved for a longer period of time. This paper presents a new plant-inspired algorithm which is called root growth optimizer (RGO). RGO simulates the iterative growth behaviors of plant roots to optimize continuous space search. In growing process, main roots and lateral roots, classified by fitness values, implement different strategies. Main roots carry out exploitation tasks by self-similar propagation in relatively nutrient-rich areas, while lateral roots explore other places to seek for better chance. Inhibition mechanism of plant hormones is applied to main roots in case of explosive propagation in some local optimal areas. Once resources in a location are exhausted, roots would shrink away from infertile conditions to preserve their activity. In order to validate optimization effect of the algorithm, twelve benchmark functions, including eight classic functions and four CEC2005 test functions, are tested in the experiments. We compared RGO with other existing evolutionary algorithms including artificial bee colony, particle swarm optimizer, and differential evolution algorithm. The experimental results show that RGO outperforms other algorithms on most benchmark functions.
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