ACO for continuous function optimization: a performance analysis

The performance of the meta-heuristic algorithms often depends on their parameter settings. Appropriate tuning of the underlying parameters can drastically improve the performance of a meta-heuristic. The Ant Colony Optimization (ACO), a population based meta-heuristic algorithm inspired by the foraging behavior of the ants, is no different. Fundamentally, the ACO depends on the construction of new solutions, variable by variable basis using Gaussian sampling of the selected variables from an archive of solutions. A comprehensive performance analysis of the underlying parameters such as: selection strategy, distance measure metric and pheromone evaporation rate of the ACO suggests that the Roulette Wheel Selection strategy enhances the performance of the ACO due to its ability to provide non-uniformity and adequate diversity in the selection of a solution. On the other hand, the Squared Euclidean distance-measure metric offers better performance than other distance-measure metrics. It is observed from the analysis that the ACO is sensitive towards the evaporation rate. Experimental analysis between classical ACO and other meta-heuristic suggested that the performance of the well-tuned ACO surpasses its counterparts

Accurate inspiral-merger-ringdown gravitational waveforms for non-spinning black-hole binaries including the effect of subdominant modes

We present an analytical waveform family describing gravitational waves (GWs) from the inspiral, merger and ringdown of non-spinning black-hole binaries including the effect of several non-quadrupole modes [($\ell = 2, m = \pm 1), (\ell = 3, m = \pm 3), (\ell = 4, m = \pm 4)$ apart from $(\ell = 2, m=\pm2)$]. We first construct spin-weighted spherical harmonics modes of hybrid waveforms by matching numerical-relativity simulations (with mass ratio $1-10$) describing the late inspiral, merger and ringdown of the binary with post-Newtonian/effective-one-body waveforms describing the early inspiral. An analytical waveform family is constructed in frequency domain by modeling the Fourier transform of the hybrid waveforms making use of analytical functions inspired by perturbative calculations. The resulting highly accurate, ready-to-use waveforms are highly faithful (unfaithfulness $\simeq 10^{-4} - 10^{-2}$) for observation of GWs from non-spinning black hole binaries and are extremely inexpensive to generate.Comment: 10 pages, 5 figure

Multiobjective programming for type-2 hierarchical fuzzy inference trees

This paper proposes a design of hierarchical fuzzy inference tree (HFIT). An HFIT produces an optimum tree-like structure. Specifically, a natural hierarchical structure that accommodates simplicity by combining several low-dimensional fuzzy inference systems (FISs). Such a natural hierarchical structure provides a high degree of approximation accuracy. The construction of HFIT takes place in two phases. Firstly, a nondominated sorting based multiobjective genetic programming (MOGP) is applied to obtain a simple tree structure (low model’s complexity) with a high accuracy. Secondly, the differential evolution algorithm is applied to optimize the obtained tree’s parameters. In the obtained tree, each node has a different input’s combination, where the evolutionary process governs the input’s combination. Hence, HFIT nodes are heterogeneous in nature, which leads to a high diversity among the rules generated by the HFIT. Additionally, the HFIT provides an automatic feature selection because it uses MOGP for the tree’s structural optimization that accept inputs only relevant to the knowledge contained in data. The HFIT was studied in the context of both type-1 and type-2 FISs, and its performance was evaluated through six application problems. Moreover, the proposed multiobjective HFIT was compared both theoretically and empirically with recently proposed FISs methods from the literature, such as McIT2FIS, TSCIT2FNN, SIT2FNN, RIT2FNS-WB, eT2FIS, MRIT2NFS, IT2FNN-SVR, etc. From the obtained results, it was found that the HFIT provided less complex and highly accurate models compared to the models produced by most of the other methods. Hence, the proposed HFIT is an efficient and competitive alternative to the other FISs for function approximation and feature selectio

Infrastructure information management of bridges at local authorities in the UK

Behind the largest infrastructure construction projects currently underway is a system of managing information known as Building Information Modelling (BIM). This represents a collaborative approach to civil engineering and makes use of advances in computer technology to link seamlessly many information repositories together across organisational boundaries. Alongside the developments in BIM, the world of asset management has also seen a major leap forward with the release of ISO 5500x – the family of international standards for asset management. This is now being adopted by many industries – particularly those in the infrastructure sectors – to maximise the value which is returned from their assets. In addition, the Highways Maintenance Efficiency Programme has released a guidance for highway authorities wishing to improve their asset management systems. However, infrastructure managers in local authorities such as county councils are significantly less engaged in both of these developments than their counterparts in strategic infrastructure networks. This paper presents the findings of a study of the ‘information system landscape’ at local authorities from across England, UK. The study reveals a number of recurring information management challenges that are frequently present. The paper finally provides a number of recommendations with specific reference to information management and encourages councils to consider adopting the standards. EPSRC/Innovate U

Toward Closing the Loop between Infrastructure Investments and Societal and Economic Impacts

The long-term value proposition of transportation infrastructure investments can be significantly distorted if the short term effects of spatial externalities on land-use patterns, economic expansions, and migration patterns are not properly included in the analysis. Some of these effects occur over a short period of time and soon after the investment materializes, while others take longer and follow more steady patterns. In this paper, we develop a novel dynamical model of a primal society with constructs that are specifically geared toward transportation infrastructure expansions and investments. The model quantifies the impact of these expansions on some key performance indicators and on the overall utility and production capacity of the society. We argue that traditional analytical models that work on the premises of stationary behavior and a static response of society to changes in infrastructure do not correctly capture these effects. The land use patterns and spatial expansion computed from the model are validated against existing theory on land use. Preliminary results on how to use the model for value proposition analysis are also presented using simple case studies

Testing the no-hair nature of binary black holes using the consistency of multipolar gravitational radiation

Gravitational-wave (GW) observations of binary black holes offer the best probes of the relativistic, strong-field regime of gravity. Gravitational radiation in the leading order is quadrupolar. However, nonquadrupole (higher order) modes make appreciable contribution to the radiation from binary black holes with large mass ratios and misaligned spins. The multipolar structure of the radiation is fully determined by the intrinsic parameters (masses and spin angular momenta of the companion black holes) of a binary in quasicircular orbit. Following our previous work [S. Dhanpal, A. Ghosh, A. K. Mehta, P. Ajith, and B. S. Sathyaprakash, Phys. Rev. D 99, 104056 (2019).], we develop multiple ways of testing the consistency of the observed GW signal with the expected multipolar structure of radiation from binary black holes in general relativity. We call this a no-hair test of binary black holes as this is similar to testing the no-hair theorem for isolated black holes through mutual consistency of the quasinormal mode spectrum. We use Bayesian inference on simulated GW signals that are consistent/inconsistent with binary black holes in general relativity to demonstrate the power of the proposed tests. We also make estimate systematic errors arising as a result of neglecting companion spins

Dimensionality reduction, and function approximation of poly(lactic-co-glycolic acid) micro- and nanoparticle dissolution rate

Prediction of poly(lactic-co-glycolic acid) (PLGA) micro- and nanoparticles’ dissolution rates plays a significant role in pharmaceutical and medical industries. The prediction of PLGA dissolution rate is crucial for drug manufacturing. Therefore, a model that predicts the PLGA dissolution rate could be beneficial. PLGA dissolution is influenced by numerous factors (features), and counting the known features leads to a dataset with 300 features. This large number of features and high redundancy within the dataset makes the prediction task very difficult and inaccurate. In this study, dimensionality reduction techniques were applied in order to simplify the task and eliminate irrelevant and redundant features. A heterogeneous pool of several regression algorithms were independently tested and evaluated. In addition, several ensemble methods were tested in order to improve the accuracy of prediction. The empirical results revealed that the proposed evolutionary weighted ensemble method offered the lowest margin of error and significantly outperformed the individual algorithms and the other ensemble techniques.Web of Science101129111