Unimodal Test Research Articles

A new penguin fossil from Seymour Island and reassessment of taxonomy and diversity of Eocene Antarctic penguins

Eocene penguins from Seymour Island play an important role in studies related to the taxonomy and evolution of the Sphenisciformes stem group. Among these penguins, the Palaeeudyptes species are particularly noteworthy for their unusually large size and the contentious nature of their classification criteria. In this study, we describe a new penguin skeleton with a well-preserved tarsometatarsus discovered in the Upper Eocene of Seymour Island, Antarctica. The new fossil exhibits tarsometatarsal characteristics of Palaeeudyptes but differs from two species of Palaeeudyptes previously found on Seymour Island, providing insights on the morphological diversity and evolutionary history of early penguins. We conduct normality and unimodality tests on Palaeeudyptes taxa from Seymour Island to reassess the hypothesis that size differences between the two species of this genus could be attributed to sexual dimorphism in a single species. The results revealed that size differences are unlikely due to sexual dimorphism. We also use the linear discriminant analysis to evaluate the taxonomic criteria for the two Palaeeudyptes species discovered in the Antarctic region. The data showed an overlap in the size distribution, indicating weakness in the classification criteria. Reassessing previous samples and establishing an additional diagnosis based on critical anatomical features could potentially resolve this issue.

DAT: A robust Discriminant Analysis-based Test of unimodality for unknown input distributions

This letter introduces a Discriminant Analysis-based unimodality Test (DAT) for automatically identifying whether a dataset is unimodal or multimodal, detecting deviations in time series datasets, estimating statistical parameters, and identifying the skewness of the data. DAT is effective in classifying datasets under both unimodal and multimodal conditions and is suitable for bi-classification applications. The performance of DAT was compared to two well-known unimodality tests, namely the dip and folding tests, and is shown to perform better. We then extended DAT to the development of a fault detection technique, which was tested against five different machine learning classifiers using data from the Case Western Reserve University (CWRU) ball bearing database. The results obtained show that the proposed approach is effective, achieving 99.999% accuracy for detecting small ball bearing fault sizes (0.007 inches). Our conclusion indicates a significant potential of the proposed test for improving anomaly detection in industrial and other related fields.

Yellow Ground Squirrel Algorithm (YGSA): A Novel Metaheuristic Algorithm for Global Optimization

In recent years, numerous methods have emerged for optimizing problems inspired by nature. Each of these methods, according to their nature, has applications for solving specific problems with unique characteristics. Given the wide array of optimization problems across various fields, introducing novel algorithms with distinct behaviors can be both appealing and practical. In this article, a nature-inspired meta-heuristic optimization algorithm called Yellow Ground Squirrel Algorithm (YGSA) is introduced. The main idea behind YGSA is to simulate the natural behavior of a yellow ground squirrel in terms of pursuit and evasion from a farmer (the predator) and reaching the nest. The objective of the proposed algorithm is to enhance and attain a more equitable equilibrium between the fundamental stages of exploration and exploitation in contrast to preceding approaches. This balance arises from the fact that the yellow ground squirrel, during its escape, simultaneously tries to increase the distance from the farmer (the predator) and decrease the distance to its nest (burrow). To validate the proposed algorithm, 56 benchmark evaluation functions from various unimodal and multimodal types have been evaluated. The evaluation results are compared with the HBO, EBO-CMAR, FO, SCA, PSO, CS, GSA, MVO, and WSO algorithms. The optimization results for unimodal test functions demonstrate the high exploitation capability of YGSA in approaching the optimal solution, while the optimization results for multimodal test functions indicate the strong exploration ability of YGSA in finding the main optimal region in the search space.

A New Lagrangian Problem Crossover—A Systematic Review and Meta-Analysis of Crossover Standards

The performance of most evolutionary metaheuristic algorithms relies on various operators. The crossover operator is a standard based on population-based algorithms, which is divided into two types: application-dependent and application-independent crossover operators. In the process of optimization, these standards always help to select the best-fit point. The high efficiency of crossover operators allows engineers to minimize errors in engineering application optimization while saving time and avoiding overpricing. There are two crucial objectives behind this paper; first, we provide an overview of the crossover standards classification that has been used by researchers for solving engineering operations and problem representation. This paper proposes a novel standard crossover based on the Lagrangian Dual Function (LDF) to enhance the formulation of the Lagrangian Problem Crossover (LPX). The LPX for 100 generations of different pairs parent chromosomes is compared to Simulated Binary Crossover (SBX) standards and Blended Crossover (BX) for real-coded crossovers. Three unimodal test functions with various random values show that LPX has better performance in most cases and comparative results in other cases. Moreover, the LPB algorithm is used to compare LPX with SBX, BX, and Qubit Crossover (Qubit-X) operators to demonstrate accuracy and performance during exploitation evaluations. Finally, the proposed crossover stand operator results are demonstrated, proved, and analyzed statistically by the Wilcoxon signed-rank sum test.

Does multisensory study benefit memory for pictures and sounds?

Studies have found a multisensory memory benefit: higher recognition accuracy for unimodal test items that were studied as bimodal items than for those studied as unimodal items. This is a surprising finding because the encoding specificity principle predicts that memory performance should be better with greater overlap between processing during study and test. We used Thelen, Talsma, and Murray's (2015) method who previously found a multisensory memory benefit. Items were presented as unimodal (picture or sound) or bimodal (picture and sound) items in a continuous recognition task in which only one modality was task-relevant. In four experiments we obtained little evidence for a difference in memory performance between items studied as unimodal or bimodal stimuli, but there was a benefit of study-test overlap in format if sound was the task-relevant modality. Task-induced attention for the irrelevant modality or response bias may have played a role in previous studies. We conclude that the multisensory memory benefit may not be a general finding, but rather one that is found only under conditions that induce participants to pay attention to the task-irrelevant modality.

Improvement of the Nelder-Mead method using Direct Inversion in Iterative Subspace

The Nelder-Mead (NM) method is a popular derivative-free optimization algorithm owing to its fast convergence and robustness. However, it is known that the method often fails to converge or costs a long time for a large-scale optimization. In the present study, the NM method has been improved using direct inversion in iterative subspace (DIIS). DIIS is a technique to accelerate an optimization method, extrapolating a better intermediate solution from linear-combination of the known ones. We compared runtimes of the new method (NM-DIIS) and the conventional NM method using unimodal test functions with various dimensions. The NM-DIIS method showed better results than the original NM on average when the dimension of the objective function is high. Long tails of the runtime distributions in the NM method have disappeared when DIIS was applied. DIIS has also been implemented in the quasi-gradient method, which is an improved version of the NM method developed by Pham et al. [IEEE Trans. Ind. Informatics, 7 (2011) 592]. The combined method also performed well especially in an upwardly convex test function. The present study proposes a practical optimization strategy and proves the versatility of DIIS.

An oppositional Salp Swarm: Jaya algorithm for thermal design optimization of an Organic Rankine Cycle

This study proposes a hybrid metaheuristic algorithm to tackle both single and multi objective optimization problems that are subjected to hard constraints. Twenty-four single objective optimization benchmark problems comprising unimodal and multi modal test functions have been solved by the proposed hybrid algorithm (OPSSAJ) and numerical results have been compared with those acquired by some of the new emerged metaheuristic optimizers. The proposed OPSSAJ shows a significant accuracy and robustness in most of the cases and proves its efficiency in solving high dimensional problems. As a real-world case study, seventeen operational design parameters of an organic rankine cycle (ORC) operating with a binary mixture of R227EA and R600 refrigerants are optimized by the proposed hybrid OPSSAJ to obtain the optimum values of contradicting dual objectives of second law efficiency and Specific Investment Cost. A Pareto curve composed of non-dominated solutions is constructed through the weighted sum method and the final solution is chosen by the reputed TOPSIS decision-maker. The pareto curve and best-compromising result obtained by utilizing the OPPSAJ are compared with that of acquired by using nondominated sorting genetic algorithm II (NSGA-II) and multiple objective particle swarm optimization (MOPSO) algorithms. The multi-objective ORC design obtained with the OPSSAJ yields a significant improvement in thermal efficiency and cost values compared to designs found by the NSGA-II and MOPSO algorithms. Furthermore, a sensitivity analysis is performed to observe the influences of the selected design variables on problem objectives.

Comprehensive learning Jaya algorithm for engineering design optimization problems

Jaya algorithm (JAYA) is a recently developed metaheuristic algorithm for global optimization problems. JAYA has a very simple structure and only needs the essential population size and terminal condition for solving optimization problems. However, JAYA is easy to get trapped in the local optimum for solving complex global optimization problems due to its single learning strategy. Motivated by this disadvantage of JAYA, this paper presents an improved JAYA, named comprehensive learning JAYA algorithm (CLJAYA), for solving engineering design optimization problems. The core idea of CLJAYA is the designed comprehensive learning mechanism by making full use of population information. The designed comprehensive learning mechanism consists of three different learning strategies to improve the global search ability of JAYA. To investigate the performance of CLJAYA, CLJAYA is first evaluated by the well-known CEC 2013 and CEC 2014 test suites, which include 50 multimodal test functions and eight unimodal test functions. Then CLJAYA is employed to solve five real-world engineering optimization problems. Experimental results demonstrate that CLJAYA can achieve better solutions for most test problems than JAYA and the other compared algorithms, which indicates the designed comprehensive learning mechanism is very effective. In addition, the source code of the proposed CLJAYA can be loaded from https://www.mathworks.com/matlabcentral/fileexchange/82134-the-source-code-for-cljaya .

MLO: Multi Leader Optimizer

Optimization is a topic that has always been discussed in all different fields of science. One of the most effective techniques for solving such problems is optimization algorithms. In this paper, a new optimizer called Multi-Leader optimizer (MLO) is developed in which multiple leaders guide members of the population towards the optimal answer. MLO is mathematically modelled based on the process of advancing members of the population and following the leaders. MLO performance in optimization is examined on twenty-three standard objective functions. The results of this optimization are compared with the results of the other eight existing optimization algorithms including Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Teaching-Learning-Based Optimization (TLBO), Gray Wolf Optimizer (GWO), Grasshopper Optimization Algorithm (GOA), Emperor Penguin Optimizer (EPO), Shell Game Optimization (SGO), and Hide Objects Game Optimization (HOGO). Based on the analysis of the simulation results on unimodal test functions to evaluate exploitation ability and multimodal test functions in order to evaluate exploration ability, it has been determined that MLO has a higher ability to solve optimization problems than existing optimization algorithms.

A Modified Gravitational Search Algorithm for Discrete Optimization Problem

This paper presents a modified Gravitational Search Algorithm (GSA) called Discrete Gravitational Search Algorithm (DGSA) for discrete optimization problems. In DGSA, an agent’s position is updated based on its direction and velocity. Both the direction and velocity determine the candidates of integer values for the position update of an agent and then the selection is done randomly. Unimodal test functions are used to evaluate the performance of the proposed DGSA. The experimental result shows that the FDGSA able to find better solutions and converges faster compared to the Binary Gravitational Search Algorithm.

Personal Best Cuckoo Search Algorithm for Global Optimization

Real-life optimization problems demand robust algorithms that perform efficient search in the environment without trapping in local optimal locations. Such algorithms are equipped with balanced explorative and exploitative capabilities. Cuckoo search (CS) algorithm is also one of such optimization algorithms, which is inspired from nature. Despite effective search strategies using Levy flights and solution switching approach, CS suffers from lack of population diversity when implemented in hard optimization problems. In this paper, enhanced local and global search strategies have been proposed in CS algorithm. The proposed variant employs personal best information in solution generation process, hence called Personal Best Cuckoo Search (pBestCS). Moreover, instead of constant value for switching parameter, pBestCS dynamically updates switching parameter as the iterations proceed. The prior approach enhances local search ability, whereas the later modification enforces effective global search ability in the algorithm. The experimental results on both unimodal and multimodal test functions with different dimensionalities validated the efficiency of the proposed modification. Based on comprehensive statistical analysis and comparisons, pBestCS outperformed the standard CS algorithm, as well as, other popular swarm-based metaheuristic algorithms particle swarm optimization (PSO) and artificial bee colony (ABC).

A new causal discovery heuristic

Probabilistic methods for causal discovery are based on the detection of patterns of correlation between variables. They are based on statistical theory and have revolutionised the study of causality. However, when correlation itself is unreliable, so are probabilistic methods: unusual data can lead to spurious causal links, while nonmonotonic functional relationships between variables can prevent the detection of causal links. We describe a new heuristic method for inferring causality between two continuous variables, based on randomness and unimodality tests and making few assumptions about the data. We evaluate the method against probabilistic and additive noise algorithms on real and artificial datasets, and show that it performs competitively.

A modified PSO algorithm with dynamic parameters for solving complex engineering design problem

ABSTRACTThis paper proposed a new approach of particle swarm optimization (PSO). The proposed modified PSO algorithm is equipped with some specially designed mechanisms of adaptively updating algorithm parameters to preserve the diversity of the swarm and to keep the balance between exploration and exploitation searches. All these mechanisms help the algorithm to avoid the premature convergence and to strengthen its robustness. Experiments are conducted on different complicated, unimodal and multimodal test functions, as well as a typical engineering inverse problem, the TEAM Workshop problem 22. The numerical results illustrate that the proposed PSO shows better performance as compared to other well developed evolutionary algorithms.

A modified whale optimization algorithm-based adaptive fuzzy logic PID controller for load frequency control of autonomous power generation systems

ABSTRACTAn autonomous power generation system (APGS) contains units such as diesel energy generator, solar photovoltaic units, wind turbine generator and fuel cells along with energy-storing units such as the flywheel energy storage system and battery energy storage system. The components either run at lower/higher power output or may turn on/off at different instants of their operation. Due to this, the conventional controllers will not provide desired performance under varied load conditions. This paper proposes an adaptive fuzzy logic PID (AFPID) controller for load frequency control. In order to achieve an improved performance, a modified whale optimization algorithm (mWOA) was also proposed in this paper for tuning of the AFPID parameters. The proposed algorithm was first evaluated using standard test functions and compared with other recent algorithms to authenticate the competence of algorithm. The proposed mWOA algorithm outperforms PSO, GSA, DE and FEP algorithms in five out of seven unimodal test functions and four out of six multimodal test functions. The effectiveness of the AFPID compared with the conventional PID and the proposed AFPID provides better performance. Reduction of 39.13% in error criteria (objective function) compared with WOA-PID controller. The proposed approach was also compared with some recently proposed frequency control approaches in a widely used two-area test system.

Recognizing sexual dimorphism in the fossil record: lessons from nonavian dinosaurs

AbstractThe demonstration of sexual dimorphism in the fossil record can provide vital information about the role that sexual selection has played in the evolution of life. However, statistically robust inferences of sexual dimorphism in fossil organisms are exceedingly difficult to establish, owing to issues of sample size, experimental control, and methodology. This is particularly so in the case of dinosaurs, for which sexual dimorphism has been posited in many species, yet quantifiable data are often lacking. This study presents the first statistical investigation of sexual dimorphism across Dinosauria. It revisits prior analyses that purport to find quantitative evidence for sexual dimorphism in nine dinosaur species. After the available morphological data were subjected to a suite of statistical tests (normality and unimodality tests and mixture modeling), no evidence for sexual dimorphism was found in any of the examined taxa, contrary to conventional wisdom. This is not to say that dinosaurs were not sexually dimorphic (phylogenetic inference suggests they may well have been), only that the available evidence precludes its detection. A priori knowledge of the sexes would greatly facilitate the assessment of sexual dimorphism in the fossil record, and it is suggested that unambiguous indicators of sex (e.g., presence of eggs, embryos, medullary bone) be used to this end.

Role of hippocampus in polymodal-cue guided tasks in rats

To examine how signals from different sensory modalities are integrated to generate an appropriate goal-oriented behavior, we trained rats in an eight-arm radial maze to visit a cue arm provided with intramaze cues from different sensory modalities, i.e. visual, tactile and auditory, in order to obtain a reward. When the same rats were then examined on test trials in which the cue arm contained one of the stimuli that the animals were trained with (i.e. light, sound or rough sheet), they showed a significant impairment with respect to the performance on the polymodal-cue task. The contribution of the dorsal hippocampus to the acquisition and retention of polymodal-cue guided task was also examined. We found that rats with dorsal hippocampal lesions before training showed a significant deficit in the acquisition of polymodal-cue oriented task that improved with overtraining. The selective lesion of the dorsal hippocampus after training disrupted memory retention, but the animals' performance improved following retraining of the polymodal task. All hippocampal lesioned rats displayed an impaired performance on the unimodal test. These findings suggest that the dorsal hippocampus contributes to the processing of multimodal sensory information for the associative memory formation and consolidation.

Intramodal Olfactory Priming of Positive and Negative Odors in Humans Using Respiration-Triggered Olfactory Stimulation (RETROS).

Priming describes the principle of modified stimulus perception that occurs due to a previously presented stimulus. Although we have begun to understand the mechanisms of crossmodal priming, the concept of intramodal olfactory priming remains relatively unexplored. Therefore, we applied positive and negative odors using respiration-triggered olfactory stimulation (RETROS), enabling us to record the skin conductance response (SCR) and breathing data without a crossmodal cueing error and measure reaction times (RTs) for olfactory tasks. RT, SCR, and breathing data revealed that negative odors were perceived significantly more arousing than positive ones. In a second experiment, 2 odors were applied during consecutive respirations. Here, we observed intramodal olfactory priming effects: A negative odor preceded by a positive odor was rated as more pleasant than when the same odor was preceded by a negative odor. Additionally, a longer identification RT was found for the second compared with the first odor. We interpret this as increased "perceptual load" due to incomplete first odor processing while the second odor was presented. Furthermore, intramodal priming can be considered a possible reason for the increase of identification RT. The use of RETROS led to these novel insights into olfactory processing beyond crossmodal interaction by providing a noncued unimodal olfactory test, and therefore, RETROS can be used in the experimental design of future olfactory studies.

Influences of selective adaptation on perception of audiovisual speech

Research suggests that selective adaptation in speech is a low-level process dependent on sensory-specific information shared between the adaptor and test-stimuli. However, previous research has only examined how adaptors shift perception of unimodal test stimuli, either auditory or visual. In the current series of experiments, we investigated whether adaptation to cross-sensory phonetic information can influence perception of integrated audio-visual phonetic information. We examined how selective adaptation to audio and visual adaptors shift perception of speech along an audiovisual test continuum. This test-continuum consisted of nine audio-/ba/-visual-/va/ stimuli, ranging in visual clarity of the mouth. When the mouth was clearly visible, perceivers “heard” the audio-visual stimulus as an integrated “va” percept 93.7% of the time (e.g., McGurk & MacDonald, 1976). As visibility of the mouth became less clear across the nine-item continuum, the audio-visual “va” percept weakened, resulting in a continuum ranging in audio-visual percepts from /va/ to /ba/. Perception of the test-stimuli was tested before and after adaptation. Changes in audiovisual speech perception were observed following adaptation to visual-/va/ and audiovisual-/va/, but not following adaptation to auditory-/va/, auditory-/ba/, or visual-/ba/. Adaptation modulates perception of integrated audio-visual speech by modulating the processing of sensory-specific information. The results suggest that auditory and visual speech information are not completely integrated at the level of selective adaptation.

Comparative Study of Krill Herd, Firefly and Cuckoo Search Algorithms for Unimodal and Multimodal Optimization

Today, in computer science, a computational challenge exists in finding a globally optimized solution from an enormously large search space. Various metaheuristic methods can be used for finding the solution in a large search space.These methods can be explained as iterative search processes that efficiently perform the exploration and exploitation in the solution space. In this context, three such nature inspired metaheuristic algorithms namely Krill Herd Algorithm (KH), Firefly Algorithm (FA) and Cuckoo search Algorithm (CS) can be used to find optimal solutions of various mathematical optimization problems. In this paper, the proposed algorithms were used to find the optimal solution of fifteen unimodal and multimodal benchmark test functions commonly used in the field of optimization and then compare their performances on the basis of efficiency, convergence, time and conclude that for both unimodal and multimodal optimization Cuckoo Search Algorithm via Levy flight has outperformed others and for multimodal optimization Krill Herd algorithm is superior than Firefly algorithm but for unimodal optimization Firefly is superior than Krill Herd algorithm.

Hybrid differential evolution and gravitation search algorithm for unconstrained optimization

This paper proposed an algorithm called DE-GSA. The proposed algorithm incorporates both the concepts from Differential evolution algorithm (DE) and Gravitation search algorithm (GSA), updating particles not only by DE operators but also by GSA mechanisms. The proposed algorithm is tested on several benchmark functions including unimodal and multimodal test functions, multimodal test function with fix dimension, and some real life problems. Then, experimental results have shown that the proposed algorithm is both efficient and effective. Key words: Gravitation search algorithm, differential evolution algorithm, differentialevolution with gravitation search algorithm (DE-GSA), hybrid meta-heuristic, algorithm.