Low Diversity Population Research Articles

To be, or not tu-be? Population structure and connectivity in three Arctic kinorhynch species: Echinoderes aquilonius, Echinoderes eximus and Echinoderes remanei

The Arctic, despite being profoundly affected by global climate change, remains largely understudied. Its local ecosystems and fauna are undergoing significant changes, including marine meiobenthic invertebrates, such as kinorhynchs. However, although they are an important component of the benthic meiofauna, our understanding of their communities remains relatively limited. The present study focuses on the structure and connectivity of the populations of three Echinoderes species: Echinoderes aquilonius, Echinoderes eximus and Echinoderes remanei, inhabiting the coastal area of Disko Island (Western Greenland). A hypothesis of low population diversity was tested through analyses based on both morphology and the molecular cox1 marker. For the first time the distribution and relationships between and within species were described. Phylogenetic analysis and haplotype networks reflected the results of population statistics (nucleotide and haplotype diversity, Tajima's D and Fu's Fs), revealing expected homogeneity and low diversity, without any noticeable patterns of geographical distribution. The results indicate that Arctic kinorhynchs form progressively developing and expanding communities. Additionally, the analyses revealed a taxonomically important example of intraspecific polymorphism. Presence or absence of tubes are usually considered to be species diagnostic characters, and the finding an isolated population of E. eximus-like specimens with sublateral tubes on segment 9, suggested that this could be a new, yet undescribed species. However, the genetic divergence between these specimens and other populations of E. eximus without tubes on segment 9 was so low that they all have to be considered conspecific. This is the first time molecular sequence data has provided evidence for intraspecific tube polymorphism among kinorhynchs.

The Application of an Improved LESS Dung Beetle Optimization in the Intelligent Topological Reconfiguration of ShipPower Systems

To address the shortcomings of the Dung Beetle Optimization (DBO) algorithm in ship power-system fault reconfiguration, such as low population diversity and an imbalance between global exploration and local exploitation, the authors of this paper propose an improved Dung Beetle Optimization (LESSDBO) algorithm. The improvements include optimizing the initial population using Latin hypercube sampling and an elite population strategy, optimizing parameters with an improved sigmoid activation function, introducing the sine–cosine algorithm (SCA) for position update optimization, and performing multi-population mutation operations based on individual quality. The LESSDBO algorithm was applied to simulate the fault reconfiguration of a ship power system, and it was compared with the traditional DBO, Genetic Algorithm (GA), and Modified Particle Swarm Optimization (MSCPSO) methods. The simulation results showed that LESSDBO outperformed the other algorithms in terms of convergence accuracy, convergence speed, and global search capability. Specifically, in the reconfiguration under Fault 1, LESSDBO achieved optimal convergence in seven iterations, reducing convergence iterations by more than 30% compared with the other algorithms. In the reconfiguration under Fault 2, LESSDBO achieved optimal convergence in eight iterations, reducing convergence iterations by more than 23% compared with the other algorithms. Additionally, in the reconfiguration under Fault Condition 1, LESSDBO achieved a minimum of four switch actions, which is 33% fewer than the other algorithms, on average. In the reconfiguration under Fault Condition 2, LESSDBO achieved a minimum of eight switch actions, which is a 5.9% reduction compared with the other algorithms. Furthermore, LESSDBO obtained the optimal reconfiguration solution in all 50 trials for both Faults 1 and 2, demonstrating a 100% optimal convergence probability and significantly enhancing the reliability and stability of the algorithm. The proposed method effectively overcomes the limitations of the traditional DBO in fault reconfiguration, providing an efficient and stable solution for the intelligent topology reconfiguration of ship power systems.

An enhanced seagull algorithm for multi-threshold image segmentation based on Kapur entropy

Abstract To address the shortcomings of the Seagull Optimization Algorithm (SOA), such as poor distribution of individuals’ positions and low population diversity leading to susceptibility to local optima after the collision avoidance phase, an Enhanced Seagull Algorithm (ESOA) is proposed. Firstly, a chaos-guided mechanism is utilized to replace the original individual positions after collision avoidance with the mean of individual optima and current positions, effectively improving the distribution of the population. Secondly, a circular-topology neighborhood structure with random weights is introduced, allowing interaction between the current individual’s position and the information from its two adjacent optimal positions, thereby improving population clustering and enhancing diversity. Thirdly, a dynamic lens mapping strategy is employed to direct the optimal seagull individuals toward their newly generated mapped points, enhancing their ability to avoid local optima. ESOA is applied to the problem of multi-threshold image segmentation based on Kapur entropy, and experimental results demonstrate its effectiveness in improving segmentation performance.

Multi-Strategy Improved Dung Beetle Optimization Algorithm and Its Applications.

The dung beetle optimization (DBO) algorithm, a swarm intelligence-based metaheuristic, is renowned for its robust optimization capability and fast convergence speed. However, it also suffers from low population diversity, susceptibility to local optima solutions, and unsatisfactory convergence speed when facing complex optimization problems. In response, this paper proposes the multi-strategy improved dung beetle optimization algorithm (MDBO). The core improvements include using Latin hypercube sampling for better population initialization and the introduction of a novel differential variation strategy, termed "Mean Differential Variation", to enhance the algorithm's ability to evade local optima. Moreover, a strategy combining lens imaging reverse learning and dimension-by-dimension optimization was proposed and applied to the current optimal solution. Through comprehensive performance testing on standard benchmark functions from CEC2017 and CEC2020, MDBO demonstrates superior performance in terms of optimization accuracy, stability, and convergence speed compared with other classical metaheuristic optimization algorithms. Additionally, the efficacy of MDBO in addressing complex real-world engineering problems is validated through three representative engineering application scenarios namely extension/compression spring design problems, reducer design problems, and welded beam design problems.

Slime Mould Algorithm Based on a Gaussian Mutation for Solving Constrained Optimization Problems

The slime mould algorithm may not be enough and tends to trap into local optima, low population diversity, and suffers insufficient exploitation when real-world optimization problems become more complex. To overcome the limitations of SMA, the Gaussian mutation (GM) with a novel strategy is proposed to enhance SMA and it is named as SMA-GM. The GM is used to increase population diversity, which helps SMA come out of local optima and retain a robust local search capability. Additionally, the oscillatory parameter is updated and incorporated with GM to set the balance between exploration and exploitation. By using a greedy selection technique, this study retains an optimal slime mould position while ensuring the algorithm’s rapid convergence. The SMA-GM performance was evaluated by using unconstrained, constrained, and CEC2022 benchmark functions. The results show that the proposed SMA-GM has a more robust capacity for global search, improved stability, a faster rate of convergence, and the ability to solve constrained optimization problems. Additionally, the Wilcoxon rank sum test illustrates that there is a significant difference between the optimization outcomes of SMA-GM and each compared algorithm. Furthermore, the engineering problem such as industrial refrigeration system (IRS), optimal operation of the alkylation unit problem, welded beam and tension/compression spring design problem are solved, and results prove that the proposed algorithm has a better optimization efficiency to reach the optimum value.

Quantitative evaluation of the impact of relaxing eligibility criteria on the risk-benefit profile of drugs for lung cancer based on real-world data.

Restrictive eligibility criteria in cancer drug trials result in low enrollment rates and limited population diversity. Relaxed eligibility criteria (REC) based on solid evidence is becoming necessary for stakeholders worldwide. However, the absence of high-quality, favorable evidence remains a major challenge. This study presents a protocol to quantitatively evaluate the impact of relaxing eligibility criteria in common non-small cell lung cancer (NSCLC) protocols in China, on the risk-benefit profile. This involves a detailed explanation of the rationale, framework, and design of REC. To evaluate our REC in NSCLC drug trials, we will first construct a structured, cross-dimensional real-world NSCLC database using deep learning methods. We will then establish randomized virtual cohorts and perform benefit-risk assessment using Monte Carlo simulation and propensity matching. Shapley value will be utilized to quantitatively measure the effect of the change of each eligibility criterion on patient volume, clinical efficacy and safety. This study is one of the few that focuses on the problem of overly stringent eligibility criteria cancer drug clinical trials, providing quantitative evaluation of the effect of relaxing each NSCLC eligibility criterion. This study will not only provide scientific evidence for the rational design of population inclusion in lung cancer clinical trials, but also establish a data governance system, as well as a REC evaluation framework that can be generalized to other cancer studies.

A Multi-strategy Enhanced Arithmetic Optimization Algorithm and Its Application in Path Planning of Mobile Robots

A multi-strategy enhanced arithmetic optimization algorithm called MSEAOA is proposed to address the issues of low population diversity, imbalanced exploration and exploitation capabilities, and low accuracy of optimal solution in the Arithmetic Optimization Algorithm. Firstly, using the good point set strategy for population initialization to improve population diversity and thus accelerate convergence speed. Secondly, we integrate the exploration and exploition capabilities of differential self-learning strategy, best example learning strategy, and second-order differential perturbation strategy balancing algorithm. Finally, the introduction of somersault foraging strategy improves the accuracy of the optimal solution. We select 14 classical benchmark test functions and the CEC2019 function test set to test the optimization ability of MSEAOA, and apply MSEAOA to the path planning problem of mobile robots. MSEAOA is compared with other meta-heuristic optimization algorithms, and the experimental results are statistically analyzed by the Wilcoxon rank-sum test. The simulation experimental results show that MSEAOA performs the best among 14 benchmark functions, but for 10 CEC2019 functions, MSEAOA has the best optimization performance among 5 of them (50%). In the path optimization problem of mobile robots, the path obtained by MSEAOA is also the best among all algorithms, its path shortening rate exceeds 8.8% in 83% of environments. The results indicate that MSEAOA is a reliable algorithm suitable for function optimization and practical optimization problems.

Bonneville basin critical zones

Playa margin wetlands in the Bonneville basin are sustained by groundwater-fed brackish springs, which transport salts and other solutes into the playa basin. These wetlands are sensitive to changing water availability and quality, which are impacted by changing climate and land use, and whose sediments also provide important records of changing environmental conditions. Gastropods building their shells in these springs provide important recorders of water chemistry and may reflect changing aqueous conditions. In this paper, we analyze spring water chemistry, gastropod ecology and gastropod shell chemistry of Blue Lake (BL) and Horseshoe Springs (HRS), two groundwater-fed wetlands in the Great Salt Lake watershed. We report the physical parameters including pH, temperature, and specific conductivity across the spring pond at Horseshoe springs. There was a slight but statistically significant variation in these physical characteristics between the deeper and shallower parts of the pool, providing evidence that there are different subsite microclimates, which may impact the populations and the isotopic composition of gastropod shells. We measured gastropod population diversity amongst nearly 12,000 shells sampled at Horseshoe springs, finding low population diversity (Shannon’s Diversity Index of 0.432), although the populations of shallow and deep snails are slightly different. The dominant snail at HRS is the Pyrgulopsis which is imperiled, and we also note that we did not find living snails here. We evaluated the bulk shell variation of stable carbonate isotopes (δ13C, and δ18O) across sites and genera. We show that there were no significant subsite-level differences in gastropod δ13C compositions, suggesting that water depth and productivity were not impacting the isotopic signal. We found subsite- and genera-specific differences in snail δ18O compositions, which we interpret to be more dependent on the geography and microclimate of where the snail lived rather than the genera’s physiology (pulmonate versus gil-breathing). We report concentrations of alkali metals (Li, Na, K, Rb, Cs), alkali earth metals (Be, Mg, Ca, Sr, Ba), and metals and metalloids (Al, Sc, Mn, Fe, Cu, Ni, Zn, As) at spring site waters and in bulk shells as potential baseline data for interpreting future or past environmental changes as recorded in shell material. We found trace element concentration and certain elemental ratio differences between genera at the same site (particularly of note were Li, Zn, Mn and Al) that will be important to constrain if these shells are to be applied as a paleoenvironmental proxy and are sometimes attributed to land use change.

Adaptive dynamic self-learning grey wolf optimization algorithm for solving global optimization problems and engineering problems.

The grey wolf optimization algorithm (GWO) is a new metaheuristic algorithm. The GWO has the advantages of simple structure, few parameters to adjust, and high efficiency, and has been applied in various optimization problems. However, the orginal GWO search process is guided entirely by the best three wolves, resulting in low population diversity, susceptibility to local optima, slow convergence rate, and imbalance in development and exploration. In order to address these shortcomings, this paper proposes an adaptive dynamic self-learning grey wolf optimization algorithm (ASGWO). First, the convergence factor was segmented and nonlinearized to balance the global search and local search of the algorithm and improve the convergence rate. Second, the wolves in the original GWO approach the leader in a straight line, which is too simple and ignores a lot of information on the path. Therefore, a dynamic logarithmic spiral that nonlinearly decreases with the number of iterations was introduced to expand the search range of the algorithm in the early stage and enhance local development in the later stage. Then, the fixed step size in the original GWO can lead to algorithm oscillations and an inability to escape local optima. A dynamic self-learning step size was designed to help the algorithm escape from local optima and prevent oscillations by reasonably learning the current evolution success rate and iteration count. Finally, the original GWO has low population diversity, which makes the algorithm highly susceptible to becoming trapped in local optima. A novel position update strategy was proposed, using the global optimum and randomly generated positions as learning samples, and dynamically controlling the influence of learning samples to increase population diversity and avoid premature convergence of the algorithm. Through comparison with traditional algorithms, such as GWO, PSO, WOA, and the new variant algorithms EOGWO and SOGWO on 23 classical test functions, ASGWO can effectively improve the convergence accuracy and convergence speed, and has a strong ability to escape from local optima. In addition, ASGWO also has good performance in engineering problems (gear train problem, ressure vessel problem, car crashworthiness problem) and feature selection.

The genome of a globally invasive passerine, the common myna, Acridotheres tristis.

In an era of global climate change, biodiversity conservation is receiving increased attention. Conservation efforts are greatly aided by genetic tools and approaches, which seek to understand patterns of genetic diversity and how they impact species health and their ability to persist under future climate regimes. Invasive species offer vital model systems in which to investigate questions regarding adaptive potential, with a particular focus on how changes in genetic diversity and effective population size interact with novel selection regimes. The common myna (Acridotheres tristis) is a globally invasive passerine and is an excellent model species for research both into the persistence of low-diversity populations and the mechanisms of biological invasion. To underpin research on the invasion genetics of this species, we present the genome assembly of the common myna. We describe the genomic landscape of this species, including genome wide allelic diversity, methylation, repeats, and recombination rate, as well as an examination of gene family evolution. Finally, we use demographic analysis to identify that some native regions underwent a dramatic population increase between the two most recent periods of glaciation, and reveal artefactual impacts of genetic bottlenecks on demographic analysis.

Into the Dark Serum Proteome: Personalized Features of IgG1 and IgA1 Repertoires in Severe COVID-19 Patients

Serum proteomics has matured and is now able to monitor hundreds of proteins quantitatively in large cohorts of patients. However, the fine characteristics of some of the most dominant proteins in serum, the immunoglobulins, are in these studies often ignored, due to their vast, and highly personalized, diversity in sequences. Here, we focus exclusively on these personalized features in the serum proteome and distinctively chose to study individual samples from a low diversity population: elderly donors infected by severe acute respiratory syndrome corona virus 2 (SARS-CoV-2). By using mass spectrometry-based methods, immunoglobulin IgG1 and IgA1 clonal repertoires were monitored quantitatively and longitudinally in more than 50 individual serum samples obtained from 17 Corona virus disease 2019 patients admitted to intensive care units. These clonal profiles were used to examine how each patient reacted to a severe SARS-CoV-2 infection. All 17 donors revealed unique polyclonal repertoires and substantial changes over time, with several new clones appearing following the infection, in a few cases leading to a few, very high, abundant clones dominating their repertoire. Several of these clones were de novo sequenced through combinations of top-down, middle-down, and bottom-up proteomics approaches. This revealed sequence features in line with sequences deposited in the SARS-CoV-specific antibody database. In other patients, the serological Ig profiles revealed the treatment with tocilizumab, that subsequently dominated their serological IgG1 repertoire. Tocilizumab clearance could be monitored, and a half-life of approximately 6 days was established. Overall, our longitudinal monitoring of IgG1 and IgA1 repertoires of individual donors reveals that antibody responses are highly personalized traits of each patient, affected by the disease and the chosen clinical treatment. The impact of these observations argues for a more personalized and longitudinal approach in patients’ diagnostics, both in serum proteomics as well as in monitoring immune responses.

An Enhanced Slime Mould Algorithm Combines Multiple Strategies

In recent years, due to the growing complexity of real-world problems, researchers have been favoring stochastic search algorithms as their preferred method for problem solving. The slime mould algorithm is a high-performance, stochastic search algorithm inspired by the foraging behavior of slime moulds. However, it faces challenges such as low population diversity, high randomness, and susceptibility to falling into local optima. Therefore, this paper presents an enhanced slime mould algorithm that combines multiple strategies, called the ESMA. The incorporation of selective average position and Lévy flights with jumps in the global exploration phase improves the flexibility of the search approach. A dynamic lens learning approach is employed to adjust the position of the optimal slime mould individual, guiding the entire population to move towards the correct position within the given search space. In the updating method, an improved crisscross strategy is adopted to reorganize the slime mould individuals, which makes the search method of the slime mould population more refined. Finally, the performance of the ESMA is evaluated using 40 well-known benchmark functions, including those from CEC2017 and CEC2013 test suites. It is also recognized by Friedman’s test as statistically significant. The analysis of the results on two real-world engineering problems demonstrates that the ESMA presents a substantial advantage in terms of search capability.

Differential evolution for population diversity mechanism based on covariance matrix

Differential evolution (DE) is a heuristic global search algorithm based on population. It has exhibited great adaptability in solving continuous-domain problems, but sometimes suffered from insufficient local search ability and being trapped in local optimum when dealing with complicated optimization problems. To solve these problems, an improved differential evolution algorithm with population diversity mechanism based on covariance matrix (CM-DE) is proposed. First, a new parameter adaptation strategy is used to adapt the control parameters, in which the scale factor F is updated according to the improved wavelet basis function in the early stage and Cauchy distribution in the later stage and the crossover rate CR is generated according to normal distribution. The diversity of population and convergence speed are improved by employing the method above. Second, the perturbation strategy is incorporated into crossover operator to enhance the search ability of DE. Finally, the covariance matrix of the population is constructed, where the variance in the covariance matrix is used as indicator to measure the similarity between individuals in the population in order to prevent the algorithm from falling into local optimum resulted by low population diversity. The CM-DE is compared with the state-of-art DE variants including LSHADE (Tanabe and Fukunaga, 2014), jSO [1], LPalmDE [2], PaDE [3] and LSHADE-cnEpSin [4] under 88 test functions from CEC2013 [5], CEC2014 [6] and CEC2017 (Wu et al., 2017) test suites. From the experiment results, it is obvious that among 30 benchmark functions from CEC2017 on 50D optimization, the CM-DE algorithm has 22, 20, 24, 23, 28 better performances comparing with LSHADE, jSO, LPalmDE, PaDE, and LSHADE-cnEpsin. For CEC2017 on 30D optimization, the proposed algorithm secures better performance on 19 out of 30 benchmark functions in terms of convergence speed. In addition, a real-world application is also used to verify the feasibility of the proposed algorithm. The experiment results validate the highly competitive performance in terms of solution accuracy and convergence speed.

Chaotic Sand Cat Swarm Optimization

In this study, a new hybrid metaheuristic algorithm named Chaotic Sand Cat Swarm Optimization (CSCSO) is proposed for constrained and complex optimization problems. This algorithm combines the features of the recently introduced SCSO with the concept of chaos. The basic aim of the proposed algorithm is to integrate the chaos feature of non-recurring locations into SCSO’s core search process to improve global search performance and convergence behavior. Thus, randomness in SCSO can be replaced by a chaotic map due to similar randomness features with better statistical and dynamic properties. In addition to these advantages, low search consistency, local optimum trap, inefficiency search, and low population diversity issues are also provided. In the proposed CSCSO, several chaotic maps are implemented for more efficient behavior in the exploration and exploitation phases. Experiments are conducted on a wide variety of well-known test functions to increase the reliability of the results, as well as real-world problems. In this study, the proposed algorithm was applied to a total of 39 functions and multidisciplinary problems. It found 76.3% better responses compared to a best-developed SCSO variant and other chaotic-based metaheuristics tested. This extensive experiment indicates that the CSCSO algorithm excels in providing acceptable results.

An emerging Panton–Valentine leukocidin-positive CC5-meticillin-resistant Staphylococcus aureus-IVc clone recovered from hospital and community settings over a 17-year period from 12 countries investigated by whole-genome sequencing

A novel Panton-Valentine leukocidin (PVL)-positive meticillin-resistant Staphylococcus aureus (MRSA) clonal complex (CC)5-MRSA-IVc ('Sri Lankan' clone) was recently described from Sri Lanka. Similar isolates caused a recent Irish hospital outbreak. To investigate the international dissemination and diversity of PVL-positive CC5-MRSA-IVc isolates from hospital and community settings using whole-genome sequencing (WGS). Core-genome single nucleotide polymorphism (cgSNP) analysis, core-genome multi-locus sequence typing (cgMLST) and microarray-based detection of antimicrobial-resistance and virulence genes were used to investigate PVL-positive CC5-MRSA-IVc (N= 214 including 46 'Sri Lankan' clone) from hospital and community settings in 12 countries over 17 years. Comparators included 29 PVL-positive and 23 PVL-negative CC5/ST5-MRSA-I/II/IVa/IVc/IVg/V. Maximum-likelihood cgSNP analysis grouped 209/214 (97.7%) CC5-MRSA-IVc into Clade I; average of 110 cgSNPs between isolates. Clade III contained the five remaining CC5-MRSA-IVc; average of 92 cgSNPs between isolates. Clade II contained seven PVL-positive CC5-MRSA-IVa comparators, whereas the remaining 45 comparators formed an outlier group. Minimum-spanning cgMLST analysis revealed a comparably low average of 57 allelic differences between all CC5/ST5-MRSA-IVc. All 214 CC5/ST5-MRSA-IVc were identified as 'Sri Lankan' clone, predominantly spa type t002 (186/214) with low population diversity and harboured a similar range of virulence genes and variable antimicrobial-resistance genes. All 214 Sri Lankan clone isolates and Clade II comparators harboured a 9616-bp chromosomal PVL-encoding phage remnant, suggesting both arose from a PVL-positive meticillin-susceptible ancestor. Over half of Sri Lankan clone isolates were from infections (142/214), and where detailed metadata were available (168/214), most were community associated (85/168). Stable chromosomal retention of pvl may facilitate Sri-Lankan clone dissemination.

A Modified Reptile Search Algorithm for Numerical Optimization Problems.

The reptile search algorithm (RSA) is a swarm-based metaheuristic algorithm inspired by the encirclement and hunt mechanisms of crocodiles. Compared with other algorithms, RSA is competitive but still suffers from low population diversity, unbalanced exploitation and exploration, and the tendency to fall into local optima. To overcome these shortcomings, a modified variant of RSA, named MRSA, is proposed in this paper. First, an adaptive chaotic reverse learning strategy is employed to enhance the population diversity. Second, an elite alternative pooling strategy is proposed to balance exploitation and exploration. Finally, a shifted distribution estimation strategy is used to correct the evolutionary direction and improve the algorithm performance. Subsequently, the superiority of MRSA is verified using 23 benchmark functions, IEEE CEC2017 benchmark functions, and robot path planning problems. The Friedman test, the Wilcoxon signed-rank test, and simulation results show that the proposed MRSA outperforms other comparative algorithms in terms of convergence accuracy, convergence speed, and stability.

Advanced orthogonal learning and Gaussian barebone hunger games for engineering design

AbstractThe hunger games search (HGS) algorithm is a recently proposed population-based optimization algorithm that mimics a common phenomenon of animals searching for food due to hunger stimuli and has a simple and easy-to- understand structure. However, the original HGS still suffers from shortcomings, such as low population diversity and the tendency to fall into local optima. To remedy these shortcomings, an improved HGS, called OCBHGS, is proposed, which introduces three main strategies, namely the chaotic initialization strategy, the Gaussian barebone mechanism, and the orthogonal learning strategy. Firstly, chaotic mapping is used for initialization to improve the quality of the initialized population. Secondly, the embedding of the Gaussian barebone mechanism effectively improves the diversity of the population, facilitates the communication between members, and helps the population avoid falling into local optima. Finally, the orthogonal learning strategy can extend the domain exploration and improve the solution accuracy of the algorithm. We conducted extensive experiments in the CEC2014 competition benchmark function, comparing OCBHGS with nine other metaheuristics and 12 improved algorithms. Also, the experimental results were evaluated using Wilcoxon signed-rank tests to analyze the experimental results comprehensively. In addition, OCBHGS was used to solve three constrained real-world engineering problems. The experimental results show that OCBHGS has a significant advantage in convergence speed and accuracy. As a result, OCBHGS ranks first in overall performance compared to other optimizers.

Enhanced whale optimization algorithm for medical feature selection: A COVID-19 case study

The whale optimization algorithm (WOA) is a prominent problem solver which is broadly applied to solve NP-hard problems such as feature selection. However, it and most of its variants suffer from low population diversity and poor search strategy. Introducing efficient strategies is highly demanded to mitigate these core drawbacks of WOA particularly for dealing with the feature selection problem. Therefore, this paper is devoted to proposing an enhanced whale optimization algorithm named E-WOA using a pooling mechanism and three effective search strategies named migrating, preferential selecting, and enriched encircling prey. The performance of E-WOA is evaluated and compared with well-known WOA variants to solve global optimization problems. The obtained results proved that the E-WOA outperforms WOA's variants. After E-WOA showed a sufficient performance, then, it was used to propose a binary E-WOA named BE-WOA to select effective features, particularly from medical datasets. The BE-WOA is validated using medical diseases datasets and compared with the latest high-performing optimization algorithms in terms of fitness, accuracy, sensitivity, precision, and number of features. Moreover, the BE-WOA is applied to detect coronavirus disease 2019 (COVID-19) disease. The experimental and statistical results prove the efficiency of the BE-WOA in searching the problem space and selecting the most effective features compared to comparative optimization algorithms.

Boosted Sine Cosine Algorithm with Application to Medical Diagnosis.

The sine cosine algorithm (SCA) was proposed for solving optimization tasks, of which the way to obtain the optimal solution is mainly through the continuous iteration of the sine and cosine update formulas. However, SCA also faces low population diversity and stagnation of locally optimal solutions. Hence, we try to eliminate these problems by proposing an enhanced version of SCA, named ESCA_PSO. ESCA_PSO is proposed based on hybrid SCA and particle swarm optimization (PSO) by incorporating multiple mutation strategies into the original SCA_PSO. To validate the effect of ESCA_PSO in handling global optimization problems, ESCA_PSO was compared with quality algorithms on various types of benchmark functions. In addition, the proposed ESCA_PSO was employed to tune the best parameters of support vector machines for dealing with medical diagnosis tasks. The results prove the efficiency of the proposed algorithms in solving optimization problems.

A Hybrid Arithmetic Optimization and Golden Sine Algorithm for Solving Industrial Engineering Design Problems

Arithmetic Optimization Algorithm (AOA) is a physically inspired optimization algorithm that mimics arithmetic operators in mathematical calculation. Although the AOA has an acceptable exploration and exploitation ability, it also has some shortcomings such as low population diversity, premature convergence, and easy stagnation into local optimal solutions. The Golden Sine Algorithm (Gold-SA) has strong local searchability and fewer coefficients. To alleviate the above issues and improve the performance of AOA, in this paper, we present a hybrid AOA with Gold-SA called HAGSA for solving industrial engineering design problems. We divide the whole population into two subgroups and optimize them using AOA and Gold-SA during the searching process. By dividing these two subgroups, we can exchange and share profitable information and utilize their advantages to find a satisfactory global optimal solution. Furthermore, we used the Levy flight and proposed a new strategy called Brownian mutation to enhance the searchability of the hybrid algorithm. To evaluate the efficiency of the proposed work, HAGSA, we selected the CEC 2014 competition test suite as a benchmark function and compared HAGSA against other well-known algorithms. Moreover, five industrial engineering design problems were introduced to verify the ability of algorithms to solve real-world problems. The experimental results demonstrate that the proposed work HAGSA is significantly better than original AOA, Gold-SA, and other compared algorithms in terms of optimization accuracy and convergence speed.