Continuous function optimization using hybrid ant colony approach with orthogonal design scheme

A hybrid Orthogonal Scheme Ant Colony Optimization (OSACO) algorithm for continuous function optimization (CFO) is presented in this paper. The methodology integrates the advantages of Ant Colony Optimization (ACO) and Orthogonal Design Scheme (ODS). OSACO is based on the following principles: a) each independent variable space (IVS) of CFO is dispersed into a number of random and movable nodes; b) the carriers of pheromone of ACO are shifted to the nodes; c) solution path can be obtained by choosing one appropriate node from each IVS by ant; d) with the ODS, the best solved path is further improved. The proposed algorithm has been successfully applied to 10 benchmark test functions. The performance and a comparison with CACO and FEP have been studied

Resolved Kinematics of Runaway and Field OB Stars in the Small Magellanic Cloud

We use GAIA DR2 proper motions of the RIOTS4 field OB stars in the Small Magellanic Cloud (SMC) to study the kinematics of runaway stars. The data reveal that the SMC Wing has a systemic peculiar motion relative to the SMC Bar of (v_RA, v_Dec) = (62 +/-7, -18+/-5) km/s and relative radial velocity +4.5 +/- 5.0 km/s. This unambiguously demonstrates that these two regions are kinematically distinct: the Wing is moving away from the Bar, and towards the Large Magellanic Cloud with a 3-D velocity of 64 +/- 10 km/s. This is consistent with models for a recent, direct collision between the Clouds. We present transverse velocity distributions for our field OB stars, confirming that unbound runaways comprise on the order of half our sample, possibly more. Using eclipsing binaries and double-lined spectroscopic binaries as tracers of dynamically ejected runaways, and high-mass X-ray binaries (HMXBs) as tracers of runaways accelerated by supernova kicks, we find significant contributions from both populations. The data suggest that HMXBs have lower velocity dispersion relative to dynamically ejected binaries, consistent with the former corresponding to less energetic supernova kicks that failed to unbind the components. Evidence suggests that our fast runaways are dominated by dynamical, rather than supernova, ejections.Comment: Accepted to ApJ Letters. 10 pages, 4 figure

Runaway OB Stars in the Small Magellanic Cloud: Dynamical Versus Supernova Ejections

Runaway OB stars are ejected from their parent clusters via two mechanisms, both involving multiple stars: the dynamical ejection scenario (DES) and the binary supernova scenario (BSS). We constrain the relative contributions from these two ejection mechanisms in the Small Magellanic Cloud (SMC) using data for 304 field OB stars from the spatially complete, Runaways and Isolated O-Type Star Spectroscopic Survey of the SMC (RIOTS4). We obtain stellar masses and projected rotational velocities $v_r\sin i$ for the sample using RIOTS4 spectra, and use transverse velocities $v_{\rm loc}$ from $\it{Gaia}$ DR2 proper motions. Kinematic analyses of the masses, $v_r\sin i$, non-compact binaries, high-mass X-ray binaries, and Oe/Be stars largely support predictions for the statistical properties of the DES and BSS populations. We find that dynamical ejections dominate over supernova ejections by a factor of $\sim 2-3$ in the SMC, and our results suggest a high frequency of DES runaways and binary ejections. Objects seen as BSS runaways also include two-step ejections of binaries that are reaccelerated by SN kicks. We find that two-step runaways likely dominate the BSS runaway population. Our results further imply that any contribution from $\it{in-situ}$ field OB star formation is small. Finally, our data strongly support the post-mass-transfer model for the origin of classical Oe/Be stars, providing a simple explanation for the bimodality in the $v_r\sin i$ distribution and high, near-critical, Oe/Be rotation velocities. The close correspondence of Oe/Be stars with BSS predictions implies that the emission-line disks are long-lived.Comment: 20 pages, 7 figures, 3 tables. Accepted by Ap

Solving Optimization Problems by the Public Goods Game

This document is the Accepted Manuscript version of the following article: Marco Alberto Javarone, ‘Solving optimization problems by the public goods game’, The European Physical Journal B, 90:17, September 2017. Under embargo. Embargo end date: 18 September 2018. The final, published version is available online at doi: https://doi.org/10.1140/epjb/e2017-80346-6. Published by Springer Berlin Heidelberg.We introduce a method based on the Public Goods Game for solving optimization tasks. In particular, we focus on the Traveling Salesman Problem, i.e. a NP-hard problem whose search space exponentially grows increasing the number of cities. The proposed method considers a population whose agents are provided with a random solution to the given problem. In doing so, agents interact by playing the Public Goods Game using the fitness of their solution as currency of the game. Notably, agents with better solutions provide higher contributions, while those with lower ones tend to imitate the solution of richer agents for increasing their fitness. Numerical simulations show that the proposed method allows to compute exact solutions, and suboptimal ones, in the considered search spaces. As result, beyond to propose a new heuristic for combinatorial optimization problems, our work aims to highlight the potentiality of evolutionary game theory beyond its current horizons.Peer reviewedFinal Accepted Versio

Alternative hybrid reconstruction for bilateral common and internal iliac artery aneurysms associated with external iliac artery occlusion

Purpose: To describe an alternative reconstruction for bilateral common (CIA) and internal (IIA) iliac artery aneurysms associated with external iliac artery (EIA) occlusion in a patient unfit for open surgery. Case Report: A high-risk 81-year-old man presented with contained rupture of a left CIA aneurysm in the presence of bilateral CIA and IIA aneurysms associated with complete occlusion of the left EIA and normal patency of both common femoral arteries. In an emergent procedure, the left EIA was recanalized subintimally, and the right IIA was embolized with a 14-mm Amplatzer Plug. The main body of a standard Excluder endograft was deployed just distal to the origin of the left renal artery, and the ipsilateral leg was extended into the proximal right EIA. On the contralateral side, a short 10-mm-diameter limb was inserted through a 12-F sheath and deployed in the CIA, proximal to the iliac bifurcation. Via a percutaneous left brachial artery access, 3 covered stents (9359 mm, 10359 mm, 10359 mm) were deployed from the distal IIA to the endograft contralateral limb. A right-to-left femorofemoral crossover bypass graft concluded the operation. The patient was discharged on the 5th postoperative day without complications; follow-up imaging at 6 months showed patency of the stent-graft and crossover bypass, with complete exclusion of the aneurysms and no evidence of endoleak. Conclusion: This case demonstrates an effective solution for complex aortoiliac lesions using commercially available devices, underlining how an accurate knowledge of alternative endovascular techniques and materials is crucial in the management of complex cases

Neuronal assembly dynamics in supervised and unsupervised learning scenarios

The dynamic formation of groups of neurons—neuronal assemblies—is believed to mediate cognitive phenomena at many levels, but their detailed operation and mechanisms of interaction are still to be uncovered. One hypothesis suggests that synchronized oscillations underpin their formation and functioning, with a focus on the temporal structure of neuronal signals. In this context, we investigate neuronal assembly dynamics in two complementary scenarios: the first, a supervised spike pattern classification task, in which noisy variations of a collection of spikes have to be correctly labeled; the second, an unsupervised, minimally cognitive evolutionary robotics tasks, in which an evolved agent has to cope with multiple, possibly conflicting, objectives. In both cases, the more traditional dynamical analysis of the system’s variables is paired with information-theoretic techniques in order to get a broader picture of the ongoing interactions with and within the network. The neural network model is inspired by the Kuramoto model of coupled phase oscillators and allows one to fine-tune the network synchronization dynamics and assembly configuration. The experiments explore the computational power, redundancy, and generalization capability of neuronal circuits, demonstrating that performance depends nonlinearly on the number of assemblies and neurons in the network and showing that the framework can be exploited to generate minimally cognitive behaviors, with dynamic assembly formation accounting for varying degrees of stimuli modulation of the sensorimotor interactions

Orthogonal methods based ant colony search for solving continuous optimization problems

Research into ant colony algorithms for solving continuous optimization problems forms one of the most significant and promising areas in swarm computation. Although traditional ant algorithms are designed for combinatorial optimization, they have shown great potential in solving a wide range of optimization problems, including continuous optimization. Aimed at solving continuous problems effectively, this paper develops a novel ant algorithm termed "continuous orthogonal ant colony" (COAC), whose pheromone deposit mechanisms would enable ants to search for solutions collaboratively and effectively. By using the orthogonal design method, ants in the feasible domain can explore their chosen regions rapidly and e±ciently. By implementing an "adaptive regional radius" method, the proposed algorithm can reduce the probability of being trapped in local optima and therefore enhance the global search capability and accuracy. An elitist strategy is also employed to reserve the most valuable points. The performance of the COAC is compared with two other ant algorithms for continuous optimization of API and CACO by testing seventeen functions in the continuous domain. The results demonstrate that the proposed COAC algorithm outperforms the others

Eco-intelligent factories: Timescales for environmental decision support

Manufacturing decisions are currently made based on considerations of cost, time and quality. However there is increasing pressure to also routinely incorporate environmental considerations into the decision making processes. Despite the existence of a number of tools for environmental analysis of manu-facturing activities, there does not appear to be a structured approach for gener-ating relevant environmental information that can be fed into manufacturing decision making. This research proposes an overarching structure that leads to three approaches, pertaining to different timescales that enable the generation of environmental information, suitable for consideration during decision making. The approaches are demonstrated through three industrial case studies