Spacing Metrics Research Articles

Repeatability of Adaptive Optics Automated Cone Measurements in Subjects With Retinitis Pigmentosa and Novel Metrics for Assessment of Image Quality.

PurposeWe determine the intersession repeatability of cone measurements via flood-illuminated adaptive optics (AO) imaging in patients with retinitis pigmentosa (RP), to better differentiate variation due to imaging inaccuracies versus pathology-driven change.MethodsA total of 25 4° × 4° AO images were acquired three times on the same day in 10 subjects with RP, registered in i2K Retina, and cones were identified using a custom-built MATLAB algorithm. Nine equally spaced regions of interest were selected for each imaging set. A subset of subjectively “poor” and “good” quality images was selected by three independent graders, analyzed using cone density, cone location similarity (CLS) and cone spacing, and compared to age-matched normals.ResultsThe coefficient of variation (CoV), repeatability, and percent repeatability of automated cone density were slightly higher in patients with RP compared to age-matched normals, but showed no statistically significant difference. The standard deviation of CLS and cone spacing of nearest-neighbor distance demonstrated a statistically significant difference between good- and poor-quality images.ConclusionsRepeatability of automated cone density measurements in patients with RP is comparable to normals. Misidentification of cones due to image quality variability is a major limitation of automated cone counting algorithms in patients with RP. Our study suggests that CLS and cone spacing metrics could be used to help define image quality and, thus, increase confidence in automated cone counts in patients with RP.Translational RelevanceThe novel AO image quality assessment metrics described in our study could help to improve patient image interpretation, prognosis, and longitudinal care.

Multi-objective and discrete Elephants Herding Optimization algorithm for QoS aware web service composition

The goal of QoS aware web service composition (QoS-WSC) is to provide new functionalities and find a best combination of services to meet complex needs of users. QoS of the resulting composite service should be optimized. QoS-WSC is a global multi-objective optimization problem belonging to NP-hard class given the number of available services. Most of existing approaches reduce this problem to a single-objective problem by aggregating different objectives, which leads to a loss of information. An alternative issue is to use Pareto-based approaches. The Pareto-optimal set contains solutions that ensure the best trade-off between conflicting objectives. In this paper, a new multi-objective meta-heuristic bio-inspired Pareto-based approach is presented to address the QoS-WSC, it is based on Elephants Herding Optimization (EHO) algorithm. EHO is characterised by a strategy of dividing and combining the population to sub population (clan) which allows exchange of information between local searches to get a global optimum. However, the application of others evolutionary algorithms to this problem cannot avoids the early stagnancy in a local optimum. In this paper a discrete and multi-objective version of EHO will be presented based on a crossover operator. Compared with SPEA2 (Strength Pareto Evolutionary Algorithm 2) and MOPSO (Multi-Objective Particle Swarm Optimization algorithm), the results of experimental evaluation show that our improvements significantly outperform the existing algorithms in term of Hypervolume, Set Coverage and Spacing metrics.

An MO‐GVNS algorithm for solving a multiobjective hybrid flow shop scheduling problem

AbstractThis paper addresses the multiobjective hybrid flow shop (MOHFS) scheduling problem. In the MOHFS problem considered here, we have a set of jobs that must be performed in a set of stages. At each stage, we have a set of unrelated parallel machines. Some jobs may skip stages. The evaluation criteria are the minimizations of makespan, the weighted sum of the tardiness, and the weighted sum of the earliness. For solving it, an algorithm based on the multiobjective general variable neighborhood search (MO‐GVNS) metaheuristic, named adapted MO‐GVNS, is proposed. This work also presents and compares the results obtained by the adapted MO‐GVNS with those of four other algorithms: multiobjective reduced variable neighborhood search, nondominated sorting genetic algorithm II (NSGA‐II), and NSGA‐III, and another MO‐GVNS from the literature. The results were evaluated based on the Hypervolume, Epsilon, and Spacing metrics, and statistically validated by the Levene test and confidence interval charts. The results showed the efficiency of the proposed algorithm for solving the MOHFS problem.

A Multi-objective Variable Neighborhood Search algorithm for solving the Hybrid Flow Shop Problem

This paper addresses the Hybrid Flow Shop Problem (HFSP) through the Multi-objective Variable Neighborhood Search metaheuristic (MOVNS). In this problem, we have a set of jobs that must be performed on a set of stages. At each stage, we have a set of unrelated parallel machines. Some jobs may skip stages. In this paper we considere two evaluation criteria under simultaneous analysis: the minimization of the makespan and the minimization of the weighted sum of tardiness. Instances of the HFSP from literature are solved by four versions of the MOVNS algorithm. The results are evaluated using the Hypervolume, Epsilon, Spacing and Sphere counting metrics.

Location optimization for multiple types of charging stations for electric scooters

The difference between traditional scooters and electric scooters is the convenience of refueling and charging process. Designing a complete infrastructure system is a necessary step if efforts to promote e-scooters are to meet with success. This study discusses the optimal location problem of locating charging stations—which is generally considered a location-allocation problem. There are two types of charging stations: charge stations and battery-exchange stations. The only one model in determining the location of either type of station tends to decrease the traditional utility compared to compound model traditional as this method tends not to set stations where they would serve the greatest number of customers. In addition, population density and land cost should be taken into account in determining where stations are set. We, therefore, propose a method that accounts for differences in population density and land cost in order to solve a multi-objective problem with maximum utility at minimum cost. A mathematical model is developed in which constraints pertaining to capacity and distance are considered. To find an optimal parameter for Multi-Objective Particle Swarm Optimization (MOPSO), generational distance (GD), maximum spread, spacing, and diversity metrics are applied. Finally, we research an angle-based focus method and determine the extent to which stations would be used in order to determine the optimal proportions of charging stations and battery-exchange stations. Moreover, according to the analysis we found that the installed ratio model of BES/BCS (Battery-exchange stations/Battery charging stations) is 6:5 in the downtown area and 1:6 in the outskirts of high population density areas. Besides, the BES/BCS ratio model is 1:13 in the downtown and only BCSs are installed in the outskirts of the low population density area.

Indicator and reference points co-guided evolutionary algorithm for many-objective optimization problems

A key issue for many-objective optimization is how to balance both convergence and diversity. In this paper, we propose indicator and reference points co-guided evolutionary algorithm, called IREA, to solve many-objective optimization. Indicator Iɛ+ can promote good performance on convergence, while reference points can maintain good performance on diversity. Thus, we innovatively combine them through association operator. Association operator first assigns solutions in population to a reference point. Solutions associated with the same reference point constitute a cluster. Then, new population is updated by solutions selected layer by layer from each cluster based on indicator. In addition, to produce better offspring, a binary tournament mating selection is adopted. Finally, the proposed algorithm is compared with six state-of-the-art algorithms on the two well-known test problems. Experimental results indicate that the proposed algorithm can achieve promising performance in terms of generational distance, spacing and Hypervolume metrics. Especially, for the problem with irregular Pareto front, the proposed algorithm also obtains competitive performance.

Using the size and position of drumlins to understand how they grow, interact and evolve

AbstractDrumlins are subglacial bedforms streamlined in the direction of ice flow. Common in deglaciated landscapes, they have been widely studied providing rich information on their internal geology, size, shape, and spacing. In contrast with bedform investigations elsewhere in geomorphology (aeolian and fluvial dunes and ripples for example) most drumlin studies derive observations from relict, and thus static features. This has made it difficult to gain information and insights about their evolution over time, which likely hampers our understanding of the process(es) of drumlin formation. Here we take a morphological approach, studying drumlin size and spacing metrics. Unlike previous studies which have focussed on databases derived from entire ice sheet beds, we adopt a space‐for‐time substitution approach using individual drumlin flow‐sets distributed in space as proxies for different development times/periods. Framed and assisted by insights from aeolian and fluvial geomorphology, we use our metric data to explore possible scenarios of drumlin growth, evolution and interaction. We study the metrics of the size and spacing of 36 222 drumlins, distributed amongst 71 flow‐sets, left behind by the former British‐Irish Ice Sheet, and ask whether behaviour common to other bedform phenomena can be derived through statistical analysis. Through characterizing and analysing the shape of the probability distribution functions of size and spacing metrics for each flow‐set we argue that drumlins grow, and potentially migrate, as they evolve leading to pattern coarsening. Furthermore, our findings add support to the notion that no upper limit to drumlin size exists, and to the idea that perpetual coarsening could occur if given sufficient time. We propose that the framework of process and patterning commonly applied to non‐glacial bedforms is potentially powerful for understanding drumlin formation and for deciphering glacial landscapes. © 2017 The Authors. Earth Surface Processes and Landforms published by John Wiley & Sons Ltd.

Automated Photoreceptor Cell Identification on Nonconfocal Adaptive Optics Images Using Multiscale Circular Voting.

PurposeAdaptive optics scanning light ophthalmoscopy (AOSLO) has enabled quantification of the photoreceptor mosaic in the living human eye using metrics such as cell density and average spacing. These rely on the identification of individual cells. Here, we demonstrate a novel approach for computer-aided identification of cone photoreceptors on nonconfocal split detection AOSLO images.MethodsAlgorithms for identification of cone photoreceptors were developed, based on multiscale circular voting (MSCV) in combination with a priori knowledge that split detection images resemble Nomarski differential interference contrast images, in which dark and bright regions are present on the two sides of each cell. The proposed algorithm locates dark and bright region pairs, iteratively refining the identification across multiple scales. Identification accuracy was assessed in data from 10 subjects by comparing automated identifications with manual labeling, followed by computation of density and spacing metrics for comparison to histology and published data.ResultsThere was good agreement between manual and automated cone identifications with overall recall, precision, and F1 score of 92.9%, 90.8%, and 91.8%, respectively. On average, computed density and spacing values using automated identification were within 10.7% and 11.2% of the expected histology values across eccentricities ranging from 0.5 to 6.2 mm. There was no statistically significant difference between MSCV-based and histology-based density measurements (P = 0.96, Kolmogorov-Smirnov 2-sample test).ConclusionsMSCV can accurately detect cone photoreceptors on split detection images across a range of eccentricities, enabling quick, objective estimation of photoreceptor mosaic metrics, which will be important for future clinical trials utilizing adaptive optics.

A New Multi-Objective Green Location Routing Problem with Heterogonous Fleet of Vehicles and Fuel Constraint

This paper introduces a new variant of Multi-Objective Green Location Routing Problem (MOGLRP) in which the start and end location of each route can be distinct. In this paper, the authors present a new mathematical formulation for the MOGLRP with consideration of environmentally issues. MOGLRP states for the problem of finding routes for vehicles to serve a set of customers while minimizes the total traveled distance, minimizes the total cost including vehicle fixed cost and variable travel cost and the co2 emissions. In order to solve the proposed model, two solution methods are used. Firstly, an exact method which is able to solve small sized problems is applied. Since the exact methods are not able to solve NP-hard problems in a reasonable time, the second method which is called multi-objective evolutionary algorithms (MOEA) are taken into account to deal with large instances. Furthermore, four well-known multi-objective evolutionary algorithms, including non-dominated sorting genetic algorithm (NSGA-II) and multi-objective particle swarm optimization (MOPSO), Strength Pareto Evolutionary Algorithm II (SPEA- II) and Pareto Envelope-based Selection Algorithm II (PESA-II) are used to compare obtained results. A comparison results show the proficiency of the proposed algorithm with respect to the four performance metrics, including quantity metric, diversification metric, spacing metrics and mean ideal distance. Finally, concluding remarks and future research directions are provided.

Reliability and Agreement Between Metrics of Cone Spacing in Adaptive Optics Images of the Human Retinal Photoreceptor Mosaic.

To assess reliability and agreement among three metrics used to evaluate the distribution of cell distances in adaptive optics (AO) images of the cone mosaic. Using an AO flood illumination retinal camera, we acquired images of the cone mosaic in 20 healthy subjects and 12 patients with retinal diseases. The three spacing metrics studied were the center-to-center spacing (Scc), the local cone spacing (LCS), and the density recovery profile distance (DRPD). Each metric was calculated in sampling areas of different sizes (64 × 64 μm and 204 × 204 μm) across the parafovea. Both Scc and LCS were able to discriminate between healthy subjects and patients with retinal diseases; DRPD did not reliably detect any abnormality in the distribution of cell distances in patients with retinal diseases. The agreement between Scc and LCS was high in healthy subjects (intraclass correlation coefficient [ICC] ≥ 0.79) and moderate in patients with retinal diseases (ICC ≤ 0.51). The DRPD had poor agreement with Scc (ICC ≤ 0.47) and LCS (ICC ≤ 0.37). The correlation between the spacing metrics of the two sampling areas was greater in healthy subjects than in patients with retinal diseases. The Scc and LCS provided interchangeable estimates of cone distance in AO retinal images of healthy subjects but could not be used interchangeably when investigating retinal diseases with significant cell reflectivity loss (≥30%). The DRPD was unreliable for describing cell distance in a human retinal cone mosaic and did not correlate with Scc and LCS. Caution is needed when comparing spacing metrics evaluated in sampling areas of different sizes.

Evaluating Descriptive Metrics of the Human Cone Mosaic.

PurposeTo evaluate how metrics used to describe the cone mosaic change in response to simulated photoreceptor undersampling (i.e., cell loss or misidentification).MethodsUsing an adaptive optics ophthalmoscope, we acquired images of the cone mosaic from the center of fixation to 10° along the temporal, superior, inferior, and nasal meridians in 20 healthy subjects. Regions of interest (n = 1780) were extracted at regular intervals along each meridian. Cone mosaic geometry was assessed using a variety of metrics − density, density recovery profile distance (DRPD), nearest neighbor distance (NND), intercell distance (ICD), farthest neighbor distance (FND), percentage of six-sided Voronoi cells, nearest neighbor regularity (NNR), number of neighbors regularity (NoNR), and Voronoi cell area regularity (VCAR). The “performance” of each metric was evaluated by determining the level of simulated loss necessary to obtain 80% statistical power.ResultsOf the metrics assessed, NND and DRPD were the least sensitive to undersampling, classifying mosaics that lost 50% of their coordinates as indistinguishable from normal. The NoNR was the most sensitive, detecting a significant deviation from normal with only a 10% cell loss.ConclusionsThe robustness of cone spacing metrics makes them unsuitable for reliably detecting small deviations from normal or for tracking small changes in the mosaic over time. In contrast, regularity metrics are more sensitive to diffuse loss and, therefore, better suited for detecting such changes, provided the fraction of misidentified cells is minimal. Combining metrics with a variety of sensitivities may provide a more complete picture of the integrity of the photoreceptor mosaic.

A multi-objective invasive weeds optimization algorithm for solving multi-skill multi-mode resource constrained project scheduling problem

A new multi-skill multi-mode resource constrained project scheduling problem with three objectives is studied in this paper. The objectives are: (1) minimizing project's makespan, (2) minimizing total cost of allocating workers to skills, and (3) maximizing total quality of processing activities. A meta-heuristic algorithm called multi-objective invasive weeds optimization algorithm (MOIWO) with a new chromosome structure guaranteeing feasibility of solutions is developed to solve the proposed problem. Two other meta-heuristic algorithms called non-dominated sorting genetic algorithm (NSGA-II) and multi-objective particle swarm optimization algorithm (MOPSO) are used to validate the solutions obtained by the developed MOIWO. The parameters of the developed algorithms are calibrated using Taguchi method. The results of the experiments show that the MOIWO algorithm has better performance in terms of diversification metric, the MOPSO algorithm has better performance regarding mean ideal distance, while NSGA-II algorithm has better performance in terms of spread of non-dominance solution and spacing metrics.

Differential evolution for noisy multiobjective optimization

We propose an extension of multiobjective optimization realized with the differential evolution algorithm to handle the effect of noise in objective functions. The proposed extension offers three merits with respect to its traditional counterpart. First, an adaptive selection of the sample size for the periodic fitness evaluation of a trial solution based on the fitness variance in its local neighborhood is proposed. This avoids the computational complexity associated with the unnecessary reevaluation of quality solutions without disregarding the necessary evaluations for relatively poor solutions to ensure accuracy in fitness estimates. The second strategy is concerned with determining the expected value of the noisy fitness samples on the basis of their distribution, instead of their conventional averaging, as the fitness measure of the trial solutions. Finally, a new crowding-distance-induced probabilistic selection criterion is devised to promote quality solutions from the same rank candidate pool to the next generation, ensuring the population quality and diversity in the objective spaces. Computer simulations performed on a noisy version of a well-known set of 23 benchmark functions reveal that the proposed algorithm outperforms its competitors with respect to inverted generational distance, spacing, error ratio, and hypervolume ratio metrics.

A dynamic MOPSO algorithm for multiobjective optimal design of hybrid renewable energy systems

SUMMARY In this paper, a dynamic multiobjective particle swarm optimization (DMOPSO) method is presented for the optimal design of hybrid renewable energy systems (HRESs). The main goal of the design is to minimize simultaneously the total net present cost (NPC) of the system, unmet load, and fuel emission. A DMOPSO-simulation based approach has been used to approximate a worthy Pareto front (PF) to help decision makers in selecting an optimal configuration for an HRES. The proposed method is examined for a case study including wind turbines, photovoltaic (PV) panels, diesel generators, batteries, fuel cells, electrolyzer, and hydrogen tanks. Well-known metrics are used to evaluate the generated PF. The average spacing and diversification metrics obtained by the proposed approach are 1386 and 4656, respectively. Additionally, the set coverage metric value shows that at least 67% of Pareto solutions obtained by DMOPSO dominate the solutions resulted by other reported algorithms. By using a sensitivity analysis for the case study, it is found that if the PV panel and wind turbine capital cost are decreased by 50%, the total NPC of the system would be decreased by 18.8 and 3.7%, respectively. Copyright © 2014 John Wiley & Sons, Ltd.

A particle swarm optimization for a fuzzy multi-objective unrelated parallel machines scheduling problem

This paper proposes a novel multi-objective model for an unrelated parallel machine scheduling problem considering inherent uncertainty in processing times and due dates. The problem is characterized by non-zero ready times, sequence and machine-dependent setup times, and secondary resource constraints for jobs. Each job can be processed only if its required machine and secondary resource (if any) are available at the same time. Finding optimal solution for this complex problem in a reasonable time using exact optimization tools is prohibitive. This paper presents an effective multi-objective particle swarm optimization (MOPSO) algorithm to find a good approximation of Pareto frontier where total weighted flow time, total weighted tardiness, and total machine load variation are to be minimized simultaneously. The proposed MOPSO exploits new selection regimes for preserving global as well as personal best solutions. Moreover, a generalized dominance concept in a fuzzy environment is employed to find locally Pareto-optimal frontier. Performance of the proposed MOPSO is compared against a conventional multi-objective particle swarm optimization (CMOPSO) algorithm over a number of randomly generated test problems. Statistical analyses based on the effect of each algorithm on each objective space show that the proposed MOPSO outperforms the CMOPSO in terms of quality, diversity and spacing metrics.

Multi-objective approaches for the open-pit mining operational planning problem

This work presents three multi-objective heuristic algorithms based on Two-phase Pareto Local Search with VNS (2PPLS-VNS), Multi-objective Variable Neighborhood Search (MOVNS) and Non-dominated Sorting Genetic Algorithm II (NSGA-II). The algorithms were applied to the open-pit-mining operational planning problem with dynamic truck allocation (OPMOP). Approximations to Pareto sets generated by the developed algorithms were compared considering the hypervolume and spacing metrics. Computational experiments have shown the superiority of the algorithms based on VNS methods, which were able to find better sets of non-dominated solutions, more diversified and with an improved convergence.

Multi-objective evolutionary approaches for intelligent design of sensor networks in the petrochemical industry

The design of optimal sensor networks for an industrial process is a complex problem that requires the resolution of several tasks with a high level of expertise. The first of these subproblems consists in selecting an initial sensor network as the starting point for the instrumentation design. This particular task constitutes a combinatorial optimization problem, where several goals are prosecuted by the designer. Therefore, the initialization procedure can be defined as a multi-objective optimization problem. In this paper, the use of multi-objective evolutionary approaches to assist experts in the design of an initial sensor network is proposed and analyzed. The aim is to contrast the advantages and limitations of Pareto and non-Pareto techniques in the context of this industrial application. The algorithms consider objectives related to cost, reliability and level of information associated with a sensor network. The techniques were evaluated by means of a comparative analysis for a strongly non-linear mathematical model that represents an ammonia synthesis plant. Results have been contrasted in terms of the set coverage and spacing metrics. As a final conclusion, the non-Pareto strategy converged closer to the Pareto front than the Pareto-based algorithms. In contrast, the Pareto-based algorithms achieved better relative distance among solutions than the non-Pareto method. In all cases, the use of evolutionary computation is useful for the expert to take the final decision on the preferred initial sensor network.