Performance analysis of real-coded evolutionary algorithms under a computationally expensive optimization scenario: 3D–2D Comparative Radiography

Comparative radiography is the forensic anthropology technique in which ante-mortem (AM) and post-mortem (PM) radiographic materials (e.g., X-ray images or CTs) are compared in order to determine the identity of a deceased human being. One of the most commonly used anatomical structures in comparative radiography are the frontal sinuses. The frontal sinuses are osseous cavities located in the skull, which are used in forensic identification tasks due to their singularity and high identification power. In order to automate the comparison of frontal sinuses in AM and PM materials, it is necessary to perform the registration of these materials (i.e., it is necessary to carry out the alignment of these anatomical regions). However, the manual alignment of these structures is a time-consuming and subjective process. In order to tackle this problem, this paper presents an automatic frontal sinuses registration method in comparative radiography using real-coded evolutionary algorithms (RCEAs). The task is formulated as a 2D-3D image registration problem using a 9 Degrees of Freedom perspective transformation model; two RCEAs (DE and MVMOSH) are compared in the minimization of the registration cost function, and the best of them (MVMO-SH) is applied to an identification scenario including 50X-ray images and 50 CTs. The results obtained show that the proposed automatic identification system is able to filter more than 80% of the sample.

Three-dimensional to two-dimensional (3D-2D) image registration is a key to fusion and simultaneous visualization of valuable information contained in 3D pre-interventional and 2D intra-interventional images with the final goal of image guidance of a procedure. In this paper, the authors focus on 3D-2D image registration within the context of intracranial endovascular image-guided interventions (EIGIs), where the 3D and 2D images are generally acquired with the same C-arm system. The accuracy and robustness of any 3D-2D registration method, to be used in a clinical setting, is influenced by (1) the method itself, (2) uncertainty of initial pose of the 3D image from which registration starts, (3) uncertainty of C-arm's geometry and pose, and (4) the number of 2D intra-interventional images used for registration, which is generally one and at most two. The study of these influences requires rigorous and objective validation of any 3D-2D registration method against a highly accurate reference or "gold standard" registration, performed on clinical image datasets acquired in the context of the intervention. The registration process is split into two sequential, i.e., initial and final, registration stages. The initial stage is either machine-based or template matching. The latter aims to reduce possibly large in-plane translation errors by matching a projection of the 3D vessel model and 2D image. In the final registration stage, four state-of-the-art intrinsic image-based 3D-2D registration methods, which involve simultaneous refinement of rigid-body and C-arm parameters, are evaluated. For objective validation, the authors acquired an image database of 15 patients undergoing cerebral EIGI, for which accurate gold standard registrations were established by fiducial marker coregistration. Based on target registration error, the obtained success rates of 3D to a single 2D image registration after initial machine-based and template matching and final registration involving C-arm calibration were 36%, 73%, and 93%, respectively, while registration accuracy of 0.59 mm was the best after final registration. By compensating in-plane translation errors by initial template matching, the success rates achieved after the final stage improved consistently for all methods, especially if C-arm calibration was performed simultaneously with the 3D-2D image registration. Because the tested methods perform simultaneous C-arm calibration and 3D-2D registration based solely on anatomical information, they have a high potential for automation and thus for an immediate integration into current interventional workflow. One of the authors' main contributions is also comprehensive and representative validation performed under realistic conditions as encountered during cerebral EIGI.

Judgement of nasolabial aesthetics in cleft lip and palate (CLP) is a vital component of assessment of treatment outcome. It is usually performed based on two-dimensional (2D) facial photographs. An increasing use of three-dimensional (3D) imaging warrants an assessment if 3D images can substitute 2D photographs during aesthetic evaluation. The aim of this study was to compare reliability of rating nasolabial appearance on 3D images and standard 2D photographs in prepubertal children. Forty subjects (age: 8.8-12) with unilateral CLP treated according to a standardized protocol, who had 2D and 3D facial images were selected. Eight lay raters assessed nasal form, nasal deviation, vermilion border, and nasolabial profile on cropped 2D and 3D images using a 100-mm visual analogue scale (VAS). Additionally, raters answer two questions: 1. Do 2D or 3D images provide more information on nasolabial aesthetics? and 2. Is aesthetic evaluation easier on 2D or 3D images? Intrarater agreement demonstrated a better reliability of ratings performed on 3D images than 2D images (correlation coefficients for 3D images ranged from 0.733 to 0.857; for 2D images from 0.151 to 0.611). The mean scores showed, however, no difference between 2D and 3D formats (>0.05). 3D images were regarded more informative than 2D images (P = 0.001) but probably more difficult to evaluate (P = 0.06). Basal view of the nose was not assessed. 3D images seem better than 2D images for rating nasolabial aesthetics but raters should familiarize themselves with them prior to rating.

Deep architectures for the segmentation of frontal sinuses in X-ray images: Towards an automatic forensic identification system in comparative radiography

Differential evolution (DE) is significantly faster and robust for solving numerical optimization problem and is more likely to find true global optimum of functions. It has solved many real-world optimization problems. However, DE has sometimes been shown slow convergence and low accuracy of solutions when the solution space is hard to explore. Population initialization is very important to the performance of differential evolution. A good initialization method can help in finding better solutions and improving convergence rate. In this paper, a uniform-differential evolution algorithm (UDE) is proposed. It incorporates uniform design initialization method into differential evolution to accelerate its convergence speed and improve the stability. UDE is compared with other two algorithms of standard differential evolution (SDE) and orthogonal differential evolution (ODE). Experiments have been conducted on 23 benchmark problems of diverse complexities. The results indicate that our approach has the stronger ability and higher calculation accuracy to find better solutions than other two algorithms.

This study investigates and compares the reliability and reproducibility of two facial anthropometric methods about external nasal angles, 3D imaging and conventional 2D photography. 2D photograph images and 3D images about external nose of 30 volunteers were taken using digital camera and Morpheus 3D scanner. To evaluate intra-rater reliability, each images were taken over two different days for each subject by the same researcher. To evaluate inter-rater reliability, another researcher took each images for each subject on the first day. The reliability of each method for measuring 4 external nasal angle is obtained using intraclass correlation coefficient (ICC) and compared. Inter-rater and intra-rater reliability of both 3D imaging and 2D photography had excellent agreement in all 4 nasal angles. In the nasofacial angular parameter, Inter-rater ICC, 2D photography was significantly higher than 3D imaging. Result of intra-rater ICC also showed both 3D imaging and 2D photography had good reliability in all 4 nasal angles. Similar to those of inter-rater ICC, nasofacial angular parameter showed statistically significant differences between 3D imaging and 2D photography. In terms of reliability, both 2D and 3D showed appropriate anthropometric results and considering its own advantage, each methods can be used complementarily.

A GA–DE hybrid evolutionary algorithm for path synthesis of four-bar linkage

Differential evolution (DE), as a class of biologically inspired and meta-heuristic techniques, has attained increasing popularity in solving many real world optimization problems. However, DE is not always successful. It can easily get stuck in a local optimum or an undesired stagnation condition. This paper proposes a new DE algorithm Differential Evolution with Alopex-Based Local Search (DEALS), for enhancing DE performance. Alopex uses local correlations between changes in individual parameters and changes in function values to estimate the gradient of the landscape. It also contains the idea of simulated annealing that uses temperature to control the probability of move directions during the search process. The results from experiments demonstrate that the use of Alopex as local search in DE brings substantial performance improvement over the standard DE algorithm. The proposed DEALS algorithm has also been shown to be strongly competitive (best rank) against several other DE variants with local search.

Many real-world optimization problems are dynamic in nature. The interest in the Evolutionary Algorithms (EAs) community in applying EA variants to dynamic optimization problems has increased greatly. Differential Evolution (DE) belongs to the group of evolutionary algorithms which operate in continuous search spaces. DE has been successfully applied to many stationary problem domains. Recently there has been some research into applying DE to dynamic optimization problems too. Many real-world problems consist of decision variables which require the optimization algorithm to work with binary parameters. This makes it impossible to apply DE in its basic form. For this purpose, binary differential evolution (BDE) approaches have been introduced. The main focus of this paper is to perform a series of experiments to test the behavior of a simple BDE under different change conditions. A simple bit-matching problem is chosen as the test environment. The results of this preliminary study show that further study is needed to make BDEs suitable to work in dynamic environments.

In recent years, Differential Evolution (DE) algorithm that is one of the most important delegate of the evolutionary algorithms have been successfully used for solving different optimization problems with its both serial and parallel variants. In this study, the performance of DE algorithm powered with cooperative emigrant creation strategy was investigated over the different types of numerical problems and obtained results were compared with the results of the serial DE and parallel DE algorithm supported with conventional emigrant creation strategy. Experimental studies showed that the convergence speed of the parallel DE algorithm is significantly improved with the cooperative emigrant creation strategy and the proposed parallel DE algorithm produces better results compared to the serial DE algorithm and parallel DE algorithm with the conventional emigrant creation strategy.

A two-archive model based evolutionary algorithm for multimodal multi-objective optimization problems

Optimization problems arise in various fields of science, engineering, and industry. In many occasions, such optimization problems, particularly in the present scenario, involve a variety of decision variables and complex structured objectives, and constraints. Often, the classical or traditional optimization techniques face difficulty in solving such real world optimization problems in their original form. Due to deficiencies of classical optimization algorithms in solving large-scale, highly non-linear, and often non-differentiable problems, there is a need to develop efficient and robust computational algorithms, which can solve problems, numerically irrespective of their sizes. Taking inspiration from nature to develop computationally efficient algorithms is one way to deal with real world optimization problems. Broadly, one can put these algorithms in the field of computational sciences and in particular, to computational intelligence. Formally, computational intelligence (CI) is a set of nature-inspired computational methodologies and approaches to solve complex real world problems. The major constituents of CI are Fuzzy Systems (FS), Neural Networks (NN), and Swarm Intelligence (SI) and Evolutionary Computation (EC). Computational intelligence techniques are powerful, efficient, flexible, and reliable. Swarm Intelligence and Evolutionary Computation are two very useful components of computational intelligence that are primarily used to solve optimization problems. This book primarily concerns with various swarm and evolutionary optimization algorithms. This chapter provides a brief introduction to swarm and evolutionary algorithms.

Artificial intelligence assistance for fetal head biometry: Assessment of automated measurement software.

Metaheuristic algorithms are constructed to solve optimization problems, but they cannot solve all the problems with best solutions. This work proposes a novel self-adaptive metaheuristic optimization algorithm, named Optimal Stochastic Process Optimizer (OSPO), which can solve different kinds of optimization problems with promising performance. Specifically, OSPO regards the procedure of optimization as a realization of stochastic process, and with the help of Subjective Probability Distribution Function (SPDF) and Receding Sampling Strategy proposed in this paper, OSPO can control the exploration-exploitation property online by the adaptive modification of the parameters in SPDF. This adaptive exploration-exploitation property of OSPO contributes to dealing with different kinds of problems; thus, it makes OSPO have the potential to solve at least a vast majority of optimization problems. The proposed algorithm is first benchmarked on uni-modal, multi-modal and composite test functions both in low and high dimensions. The results are verified by comparative studies with seven well-performed metaheuristic algorithms. Then, 21 real-world optimization problems are used to further investigate the effectiveness of OSPO. The winners of CEC2020 Competition on Real-World Single Objective Constrained Optimization, SASS algorithm, sCMAgES algorithm, EnMODE algorithm and COLSHADE algorithm are used as four comparative algorithms in real-world optimization problems. The analysis of simulations demonstrates that OSPO is able to provide very competitive performance compared to the comparative meta-heuristics both in benchmark functions and in real-world optimization problems; thus, the potential of OSPO to solve at least a vast majority of optimization problems is verified. A corresponding MATLAB APP demo is available on https://github.com/JiahongXu123/OSPO-algorithm.git.

Nature-inspired algorithms serve as the backbone of modern computing technology, and over the past three decades, the field has grown enormously. Many applications were solved by such algorithms and are replacing the traditional classical optimization processes. A recent naked mole-rat algorithm (NMRA) was proposed based on the mating patterns of naked mole-rats. This algorithm proved its worth in terms of competitiveness and application to various domains of research. The aim was to propose an algorithm based on NMRA, named enhanced NMRA (ENMRA), by mitigating the problems that this algorithm suffers from: slow convergence, poor exploration, and local optima stagnation. To enhance the exploration capabilities of basic NMRA, grey wolf optimization (GWO)-based search equations were employed. Exploitation was improved using population division methods based on local neighborhood search (LNS) and differential evolution (DE) equations. To avoid the local stagnation problem, a neighborhood search strategy around the best individual was utilized. Such improvements help the new variant to solve highly challenging optimization problems in contrast to existing algorithms. The efficacy of ENMRA was evaluated using CEC 2019 benchmark test suite. The results were statistically analyzed by the Wilcoxon rank-sum test and Friedman rank (f-rank) test. The resulting analysis proved that ENMRA is superior to the competitive algorithms for test functions CEC 2019 with overall effectiveness of 60.33%. Moreover, the real-world optimization problem from underground wireless sensor networks for an efficient cross-layer solution was also used to test the efficiency of ENMRA. The results of comparative study and statistical tests affirmed the efficient performance of the proposed algorithm.