We study the problem of estimating the time delay between two signals representing delayed, irregularly sampled and noisy versions of the same underlying pattern. We propose and demonstrate an evolutionary algorithm f...
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We study the problem of estimating the time delay between two signals representing delayed, irregularly sampled and noisy versions of the same underlying pattern. We propose and demonstrate an evolutionary algorithm for the (hyper) parameter estimation of a kernel-based technique in the context of an astronomical problem, namely estimating the time delay between two gravitationally lensed signals from a distant quasar. Mixed types (integer and real) are used to represent variables within the evolutionary algorithm. We test the algorithm on several artificial data sets, and also on real astronomical observations of quasar Q0957 + 561. By carrying out a statistical analysis of the results we present a detailed comparison of our method with the most popular methods for time delay estimation in astrophysics. Our method yields more accurate and more stable time delay estimates. Our methodology can be readily applied to current state-of-the-art optical monitoring data in astronomy, but can also be applied in other disciplines involving similar time series data. (C) 2009 Elsevier Ltd. All rights reserved.
Salp swarm algorithm (SSA) is a recently developed meta-heuristic swarm intelligence optimization algorithm based on simulating the chain movement behavior of salps sailing and foraging in the sea. In this paper, a no...
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Salp swarm algorithm (SSA) is a recently developed meta-heuristic swarm intelligence optimization algorithm based on simulating the chain movement behavior of salps sailing and foraging in the sea. In this paper, a novel hybrid meta-heuristic algorithm called SSA-FGWO is proposed to overcome the shortcomings of the original SSA, including slow convergence speed in dealing with high-dimensional and multimodal landscapes, low precision, and global optimization problems. The essential idea of SSA-FGWO is to improve the salp swarm optimization algorithm (SSA) by utilizing the Grey Wolf Algorithm (GWO) strategy. The hybridization mechanism includes two steps: First, the strong exploitation of SSA is applied to update the leaders' position of the chain population. Second, the strong exploration strategy of GWO is used to update the followers' position to increase the population variance of the proposed optimizer. The proposed algorithm is expected to enhance exploration and exploitation ability significantly by the hybrid design. The effectiveness of SSA-FGWO is investigated through CEC2020, 23 representative benchmark cases, and feature selection problems (18 data sets) are solved. The algorithm has been examined for its computational complexity and convergent behavior. In addition to GWO, SSA and other swarm optimization algorithms are employed to compare with the proposed optimizer. The obtained experimental results show that the exploitation, exploration tendencies, and convergence patterns of SSA-FGWO have significantly improved. The results show that the proposed SSA-FGWO algorithm is a promising one that outperforms the basic SSA, GWO, and other algorithms in terms of efficacy.
The EASY-GOING deconvolution (EGdeconv) program is extended to enable fast and automated fitting of multiple quantum magic angle spinning (MQMAS) spectra guided by evolutionary algorithms. We implemented an analytical...
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The EASY-GOING deconvolution (EGdeconv) program is extended to enable fast and automated fitting of multiple quantum magic angle spinning (MQMAS) spectra guided by evolutionary algorithms. We implemented an analytical crystallite excitation model for spectrum simulation. Currently these efficiencies are limited to two-pulse and z-filtered 3QMAS spectra of spin 3/2 and 5/2 nuclei, whereas for higher spin-quantum numbers ideal excitation is assumed. The analytical expressions are explained in full to avoid ambiguity and facilitate others to use them. The EGdeconv program can fit intergtion parameter distributions. It currently includes a Gaussian distribution for the chemical shift and an (extended) Czjzek distribution for the quadrupolar interaction. We provide three case studies to illustrate EGdeconv's capabilities for fitting MQMAS spectra. The EGdeconv program is available as is on our website http://***.n1 for 64-bit Linux operating systems. (C) 2013 Published by Elsevier Inc.
This paper proposes an idea of using well studied and documented single-objective optimization methods in multiobjective evolutionary algorithms. It develops a hybrid algorithm which combines the multiobjective evolut...
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This paper proposes an idea of using well studied and documented single-objective optimization methods in multiobjective evolutionary algorithms. It develops a hybrid algorithm which combines the multiobjective evolutionary algorithm based on decomposition (MOEA/D) with guided local search (GLS), called MOEA/D-GLS. It needs to optimize multiple single-objective subproblems in a collaborative way by defining neighborhood relationship among them. The neighborhood information and problem-specific knowledge are explicitly utilized during the search. The proposed GLS alternates among subproblems to help escape local Pareto optimal solutions. The experimental results have demonstrated that MOEA/D-GLS outperforms MOEA/D on multiobjective traveling salesman problems.
Neural network models are essential tools in understanding how behavior arises from information processing in the brain. Recent advances in computing power and neural network algorithms have made more complex models p...
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Mutation and crossover are the main search operators of different variants of evolutionary algorithms. Despite the many discussions on the importance of crossover nobody has proved rigorously for some explicitly defin...
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Mutation and crossover are the main search operators of different variants of evolutionary algorithms. Despite the many discussions on the importance of crossover nobody has proved rigorously for some explicitly defined fitness functions f(n) : {0, 1}(n) -> (R that a genetic algorithm with crossover can optimize f(n) in expected polynomial time while all evolution strategies based only on mutation (and selection) need expected exponential time. Here such functions and proofs are presented for a genetic algorithm without any idealization. For some functions one-point crossover is appropriate while for others uniform crossover is the right choice. (c) 2005 Elsevier B.V All rights reserved.
evolutionary algorithms (EAs) are well-known optimization approaches to deal with nonlinear and complex problems. However, these population-based algorithms are computationally expensive due to the slow nature of the ...
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evolutionary algorithms (EAs) are well-known optimization approaches to deal with nonlinear and complex problems. However, these population-based algorithms are computationally expensive due to the slow nature of the evolutionary process. This paper presents a novel algorithm to accelerate the differential evolution (DE). The proposed opposition-based DE (ODE) employs opposition-based learning (OBL) for population initialization and also for generation jumping. In this work, opposite numbers have been utilized to improve the convergence rate of DE. A comprehensive set of 58 complex benchmark functions including a wide range of dimensions is employed for experimental verification. The influence of dimensionality, population size, jumping rate, and various mutation strategies are also investigated. Additionally, the contribution of opposite numbers is empirically verified. We also provide a comparison of ODE to fuzzy adaptive DE (FADE). Experimental results confirm that the ODE outperforms the original DE and FADE in terms of convergence speed and solution accuracy.
Pharmacophore methods provide a way of establishing a structure--activity relationship for a series of known active ligands. Often, there are several plausible hypotheses that could explain the same set of ligands and...
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Pharmacophore methods provide a way of establishing a structure--activity relationship for a series of known active ligands. Often, there are several plausible hypotheses that could explain the same set of ligands and, in such cases, it is important that the chemist is presented with alternatives that can be tested with different synthetic compounds. Existing pharmacophore methods involve either generating an ensemble of conformers and considering each conformer of each ligand in turn or exploring conformational space on-the-fly. The ensemble methods tend to produce a large number of hypotheses and require considerable effort to analyse the results, whereas methods that vary conformation on-the-fly typically generate a single solution that represents one possible hypothesis, even though several might exist. We describe a new method for generating multiple pharmacophore hypotheses with full conformational flexibility being explored on-the-fly. The method is based on multiobjective evolutionary algorithm techniques and is designed to search for an ensemble of diverse yet plausible overlays which can then be presented to the chemist for further investigation.
The scope of the paper is to investigate different strategies for the design of a multi-energy system considered as a systemic optimization problem. The objective is to determine the best sizes of the energy assets su...
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The scope of the paper is to investigate different strategies for the design of a multi-energy system considered as a systemic optimization problem. The objective is to determine the best sizes of the energy assets such as electrochemical and thermal storages, cogeneration units, solar generators and chillers. In these cases, the techno-economic optimization is a tradeoff between the operating costs and the capital expenditures in the form of integrated management and design of the system. The paper addresses the challenges of these optimization problems in two steps. The former implements generic piecewise linearization techniques based on non-linear models. That approach allows a significant reduction of the computational time for the management loop of the assets (i.e. optimal power dispatch). The latter takes into consideration the integration of that management loop in different architectures for optimal system planning. The main contribution of the paper toward filling the gap in the literature is to investigate a wide range of optimization frameworks - with bi-level optimizations (using both deterministic and evolutionary methods), Monte-Carlo simulations as well as a performant 'all-in-one' approach in which both sizes and controls are variables of a single mathematical problem formulation. Finally, a thorough results analysis highlights that the best solution tends to be the same whether the objective to optimize is the traditional net present value at the end of the system lifespan or the total yearly cost of ownership.
Coupling sensors in a sensor network with mobility mechanism can boost the performance of wireless sensor networks (WSNs). In this paper, we address the problem of self-deploying mobile sensors to reach high coverage....
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Coupling sensors in a sensor network with mobility mechanism can boost the performance of wireless sensor networks (WSNs). In this paper, we address the problem of self-deploying mobile sensors to reach high coverage. The problem is modeled as a multi-objective optimization that simultaneously minimizes two contradictory parameters;the total sensor moving distance and the total uncovered area. In order to resolve the aforementioned deployment problem, this study investigates the use of biologically inspired mechanisms, including evolutionary algorithms and swarm intelligence, with their state-of-the-art algorithms. Unlike most of the existing works, the coverage parameter is expressed as a probabilistic inference model due to uncertainty in sensor readings. To the best of our knowledge, probabilistic coverage of mobile sensor networks has not been addressed in the context of multi-objective bio-inspired algorithms. Performance evaluations on deployment quality and deployment cost are measured and analyzed through extensive simulations, showing the effectiveness of each algorithm under the developed objective functions. Simulations reveal that only one multi-objective evolutionary algorithm;the so-called multi-objective evolutionary algorithm with decomposition survives to effectively tackle the probabilistic coverage deployment problem. It gathers more than 78 % signals from all of the targets (and in some cases reaches 100 % certainty). On the other hand, non-dominated sorting genetic algorithm II, multi-objective particle swarm optimization, and non-dominated sorting particle swarm optimization show inferior performance down to 16-32 %, necessitating further modifications in their internal mechanisms.
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