Complex Search Research Articles

World Hyper-Heuristic: A novel reinforcement learning approach for dynamic exploration and exploitation

In the real world, there are many complex problems in engineering. Every problem has a level of computational complexity, starting from simple problems and reaching NP-hard problems. Np-hard problems do not have a definite answer. Therefore, hyper-heuristic algorithms try to optimize NP-hard problems. Hyper-heuristics optimize complex search problems using a combination plate of exploration and exploitation strategies. The current algorithms need more generalizability, handling specific data types, limitations to a particular search problem, and weak performance. We propose a World hyper-heuristic (World) to address the issues using a novel reinforcement learning method. World, in two steps of rewarding and selection, dynamically switches between exploration and exploitation strategies provided in an infinite pool of meta-heuristics. We evaluated the performance of our proposed method in three phases. First, we optimized the standard functions as artificial Np-hard problems. Then, we compared real engineering examples related to discrete Np-hard problems. Finally, we implemented and analyzed problems with the continuous Np-hard problems. Our extensive comparisons with the state-of-the-art algorithms demonstrate the World’s outperformance in handling varied search problems with any data type. Among all the findings, World finds the shortest path of length 4.33e + 05, far shorter than the results of the state-of-the-art work, in benchmarked data of 10,000 real cities.

INDICATORS OF INFORMATION FEATURES FOR RECOGNISING THE STATE OF SOURCES AND OBJECTS OF TELECOMMUNICATION NETWORKS AND SYSTEMS

Background. The majority of modern procedures for the recognition of radio sources and objects are based on the use of binary and multivalued logic, which have low specific features. The essence of the issues is to compare a priori knowledge and a posteriori data coming from the surveillance means and to make a decision on the recognition of a radio emission object. A priori knowledge and a posteriori data are formed both before and during the recognition process on the basis of sets of information features or information signatures. At the same time, when constructing an integral indicator for determining the affiliation and status of sources and objects, it is necessary to know the weighting coefficients of information features, the determination of which is a rather difficult task. Therefore, the issue of determining the weighting coefficients that characterise information features remains an urgent task in the field of statistical radio engineering.
 Objective. The purpose of the paper is to select and substantiate a simple and effective method for calculating the weighting coefficients of information features for the implementation of the methodology for recognising radio sources and objects.
 Methods. Decision-making on the value of the weighting coefficients of information features of the recognition objects belonging to a certain class is based on the results of calculations using one of the three Fishburne formulae, which, in comparison with the known methods of expert assessments, are very simple and understandable, do not require any additional research and complex calculations.
 Results. The procedure is proposed and an example of using the Fishburne method (three formulae) in calculating the value of the weighting coefficients of information features for recognising sources and objects of radio monitoring is considered.
 Conclusions. Comparison of the method of calculating the weighting coefficients using Fishburne's formulae with other known methods of expert assessments shows that there is no need to interview experts and process their analysis results; there are no restrictive implementation conditions; it is easy to take into account additional information about the indicators, if necessary; no software implementation with a complex search algorithm is required; it is easy to make any changes as additional information indicators.

Hyperbolic decision-making based on RBF for diabetic/COVID-19 iris

The Iris is the most complex tissue of the body that shows thousands of nerves, blood vessels, muscles, and other tissues. This study presents techniques for searching points on the iris by visual query language based on the Gaussian radial basis function (GRBF) and Hyperbolic decision tree; there are several types of iris belonging to diabetic patients; we consider the behavior of COVID-19 on diabetes; our solution detects similarities in symptoms for two different conditions. It can estimate signs of new patterns of diseases on the next cycle of the time interval of illness for virus mutation. It can match the zones in the iris by Delaunay triangulation in d3. js. Then it can design colored models with the same color for spatial diseases. The best algorithm is used for community detection for a group of signs clustered and reducing the error with K-NN by neo4j on the Cypher platform. It detects congestion of infection and inflammation of chronic diseases as a background disease; Covid-19 disorder is illustrated with a centrality algorithm based on a shared relationship for two diseases graph. Our techniques calculate the accuracy of our query design for the target shared symptoms model for new diseases. We reduced complexity algorithms and search time by decreasing the redundancy of data points. We provide the abstract layer in the target model to reduce the dimensionality of data. We present a model from a graph with a description of knowledge that it can generate to determine the target user. Algorithms optimize this model. This model can map the points of the circle into the query and hyperbolic tree, and decide for their classification in the next mutation of diseases. Our solution improved the performance by about 97% in memory usage and time-consuming of search data in complex mixed diseases with different colors and signs.

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The instability of the political situation in the world physically and psychologically affects the population of the planet. Often this impact is negative. Today the problem of post-traumatic syndrome is extremely relevant in Ukraine, where genocide, usual life destruction and family ties rapture take place during the brutal war. Military actions are a psychologically traumatic factor for both direct participants in combat events and civilians who are located both in the area of hostilities and outside it. Every emergency caused by military operations changes people's daily lives, destroying their basic needs for security and a stable worldview. Although the human psyche is capable of adapting to any crisis, emotional stress can lead to both overcoming the stressful state and disruption and deterioration of vital functions. Psychological rehabilitation is an integral part of any type of rehabilitation including social. The article aims to define the place and importance of psychosocial rehabilitation in the context of social rehabilitation of people with disabilities and posttraumatic stress disorder and victims of war, captivity and torture and to identify promising ways of social rehabilitation at the present stage. For a complex search, we searched for relevant studies in databases DOAJ and EBSCOhost, for the last decade. The work includes articles devoted to the psychosocial rehabilitation of persons with disabilities, posttraumatic stress disorder, and victims of war, captivity and torture. It is established that psychological rehabilitation is an integral part of social rehabilitation and together they form psychosocial rehabilitation a prolonged process which needs highly qualified personnel. Without the restoration of a person’s trust in him/herself, his/her emotional status, possibility to relax, optimism etc., the efficacy of isolated social work will be minimal.

High‐salt diet enhances Helicobacter pylori infection and increases the prevalence of Alzheimer’s disease

AbstractBackgroundThe American Heart Association recommends that adults consume less than 2,300 milligrams of salt (sodium chloride) daily; anything surpassing this limit is classified as a “high‐salt diet (HSD)” [1]. Research has shown that excessive salt intake stimulates the gastric mucosa, favoring Helicobacter pylori (Hp) colonization [2]. In this review, the relationship between HSD and the susceptibility to Hp infection will be discussed. It is proposed that increased salt consumption will cause neurodegeneration, i.e. Alzheimer’s disease (AD) by way of increased expression of Hp strains in the gastric mucosa.MethodsA complex and specific literature search was conducted across various scientific, international databases for English, peer‐reviewed articles and reviews published in the last decade using the following terms: Helicobacter pylori, amyloid‐β, high‐salt diet, and Alzheimer’s disease. Case reports were excluded. The main objectives were predominantly focused on animal‐based studies.ResultsExperimental data shows HSD enhances Hp growth in mice. The subsequent microglial activation containing H. pylori‐specific IgG, IL‐8 and TNF‐α in the cerebrospinal fluid, induces oxidative damage in neuronal cells. In addition, urease in Hp increases cytosolic [Ca2+] apoptosis and autophagy of neurons. Hp filtrate induces tau hyperphosphorylation at AD‐tau phosphorylation sites such as Thr231 and Ser404. Hp ‐infected mice show a significantly larger GFAP‐positive astrocyte area than non‐infected mice. Immunostaining reveals microglia and astrocytes to be found adjacent to amyloid‐β plaques in Hp‐infected AD model mice. Injection of Hp filtrate impairs the maturation of dendritic spines, increasing Aβ42 production in the hippocampus and cortex, and enhancing expression of PS‐2, resulting in delayed spatial learning and impaired memory in rats.ConclusionThis review provides comprehensive evidence of potential synergistic effects of HSD and Hp on the brain resulting in neurodegeneration and AD progression. Understanding the pathologic consequences of salt consumption on this infection may be key in the prevention of dementia and AD. Thus, further research is required for substantiation of causality, the role of antibiotics and to examine other novel therapeutics.

Tasks, Copilots, and the Future of Search: A Keynote at SIGIR 2023

Search is far from being a solved problem. While search engines may cope well with simple tasks, searchers and systems struggle as task complexity increases. Task is central to the search process, motivating the search and driving search behavior. Complex search tasks require more than support for rudimentary fact finding or re-finding. Various support options have been offered by search systems over time (e.g., query suggestions, contextual search) to help search engine users more effectively tackle complex tasks. The recent emergence of generative artificial intelligence (AI) and the arrival of assistive agents, or copilots , based on this technology, has the potential to offer further assistance to searchers, especially those engaged in complex tasks. The implications from these advances for the design of intelligent systems and for the future of search itself are significant. This overview of the keynote that I gave at the 2023 ACM SIGIR Conference introduces AI copilots and briefly presents some of the challenges and opportunities for researching, developing, and deploying search copilots. Date : 26 July 2023.

Efficient and Expressive Search Scheme over Encrypted Electronic Medical Records

In recent years, there has been rapid development in computer technology, leading to an increasing number of medical systems utilizing electronic medical records (EMRs) to store their clinical data. Because EMRs are very private, healthcare institutions usually encrypt these data before transferring them to cloud servers. A technique known as searchable encryption (SE) can be used by healthcare institutions to encrypt EMR data. This technique enables searching within the encrypted data without the need for decryption. However, most existing SE schemes only support keyword or range searches, which are highly inadequate for EMR data as they contain both textual and digital content. To address this issue, we have developed a novel searchable symmetric encryption scheme called SSE-RK, which is specifically designed to support both range and keyword searches, and it is easily applicable to EMR data. We accomplish this by creating a conversion technique that turns keywords and ranges into vectors. These vectors are then used to construct index tree building and search algorithms that enable simultaneous range and keyword searches. We encrypt the index tree using a secure K-Nearest Neighbor technique, which results in an effective SSE-RK approach with a search complexity that is quicker than a linear approach. Theoretical and experimental study further demonstrates that our proposed scheme surpasses previous similar schemes in terms of efficiency. Formal security analysis demonstrates that SSE-RK protects privacy for both data and queries during the search process. Consequently, it holds significant potential for a wide range of applications in EMR data. Overall, our SSE-RK scheme, which offers improved functionality and efficiency while protecting the privacy of EMR data, generally solves the shortcomings of the current SE schemes.

ON THE EXISTENCE OF STRONG PROOF COMPLEXITY GENERATORS

AbstractCook and Reckhow [5] pointed out that $\mathcal {N}\mathcal {P} \neq co\mathcal {N}\mathcal {P}$ iff there is no propositional proof system that admits polynomial size proofs of all tautologies. The theory of proof complexity generators aims at constructing sets of tautologies hard for strong and possibly for all proof systems. We focus on a conjecture from [16] in foundations of the theory that there is a proof complexity generator hard for all proof systems. This can be equivalently formulated (for p-time generators) without a reference to proof complexity notions as follows:•There exists a p-time function g stretching each input by one bit such that its range $rng(g)$ intersects all infinite $\mathcal {N}\mathcal {P}$ sets.We consider several facets of this conjecture, including its links to bounded arithmetic (witnessing and independence results), to time-bounded Kolmogorov complexity, to feasible disjunction property of propositional proof systems and to complexity of proof search. We argue that a specific gadget generator from [18] is a good candidate for g. We define a new hardness property of generators, the $\bigvee $ -hardness, and show that one specific gadget generator is the $\bigvee $ -hardest (w.r.t. any sufficiently strong proof system). We define the class of feasibly infinite $\mathcal {N}\mathcal {P}$ sets and show, assuming a hypothesis from circuit complexity, that the conjecture holds for all feasibly infinite $\mathcal {N}\mathcal {P}$ sets.

Constructing and meta-evaluating state-aware evaluation metrics for interactive search systems

Evaluation metrics such as precision, recall and normalized discounted cumulative gain have been widely applied in ad hoc retrieval experiments. They have facilitated the assessment of system performance in various topics over the past decade. However, the effectiveness of such metrics in capturing users’ in-situ search experience, especially in complex search tasks that trigger interactive search sessions, is limited. To address this challenge, it is necessary to adaptively adjust the evaluation strategies of search systems to better respond to users’ changing information needs and evaluation criteria. In this work, we adopt a taxonomy of search task states that a user goes through in different scenarios and moments of search sessions, and perform a meta-evaluation of existing metrics to better understand their effectiveness in measuring user satisfaction. We then built models for predicting task states behind queries based on in-session signals. Furthermore, we constructed and meta-evaluated new state-aware evaluation metrics. Our analysis and experimental evaluation are performed on two datasets collected from a field study and a laboratory study, respectively. Results demonstrate that the effectiveness of individual evaluation metrics varies across task states. Meanwhile, task states can be detected from in-session signals. Our new state-aware evaluation metrics could better reflect in-situ user satisfaction than an extensive list of the widely used measures we analyzed in this work in certain states. Findings of our research can inspire the design and meta-evaluation of user-centered adaptive evaluation metrics, and also shed light on the development of state-aware interactive search systems.

A Grammar-based multi-objective neuroevolutionary algorithm to generate fully convolutional networks with novel topologies

The design of complex and deep neural networks is often performed by identifying and combining building blocks and progressively selecting the most promising combination. Neuroevolution automates this process by employing evolutionary algorithms to guide the search. Within this field, grammar-based evolutionary algorithms have been demonstrated to be powerful tools to describe and thus encode complex neural architectures effectively. Following this trend, the present work proposes a novel grammar-based multi-objective neuroevolutionary for generating Fully Convolutional Networks. The proposed method, named Multi-Objective gRammatical Evolution for FUlly convolutional Networks (MOREFUN), includes a new efficient way to encode skip connections, facilitating the description of complex search spaces and the injection of domain knowledge in the search procedure, generation of fully convolutional networks, upsampling of lower-resolution inputs in multi-input layers, usage of multi-objective fitness, and inclusion of data augmentation and optimiser settings to the grammar. Our best networks outperformed previous grammar evolution algorithms, achieving 90.5% accuracy on CIFAR-10 without using transfer learning, ensembles, or test-time data augmentation. Our best models had 13.39±5.25 trainable parameters and the evolutionary process required 90 min per generation.

Fast and private multi-dimensional range search over encrypted data

For businesses looking to outsource their data to remote servers, cloud-based data storage is a popular choice. It is popular due to its flexibility, cost-effectiveness, and widespread availability. However, ensuring the confidentiality of data is a critical challenge that must be addressed. As a response to this issue, searchable encryption techniques have been developed. These techniques enable search queries to be performed on encrypted data while still keeping the plaintext confidential. While most existing symmetric searchable encryption schemes are designed for one-dimensional data records or document-keyword inverted indices, this paper introduces MDRSSE, a novel symmetric searchable encryption scheme specifically tailored for multi-dimensional range search. MDRSSE stands out as one of the pioneering SSE schemes to support multi-dimensional range search efficiently, without incurring undetermined additional communication or computation costs. By employing a single round of communication between the client and server, MDRSSE enables an honest-but-curious server to respond to multi-dimensional range queries without gaining knowledge of the data records or revealing the search query. Notably, MDRSSE boasts the lowest overall search complexity compared to existing state-of-the-art symmetric searchable encryption schemes designed for multi-dimensional range search. Extensive experimental tests were conducted to validate the robustness and practicality of our proposed scheme. The results demonstrate that, for a dataset consisting of 100K records with 12 dimensions (with each leaf node holding 500 records), it takes only 2.2 seconds to generate the encrypted dataset, and the overall setup phase completes within 2.5 seconds. Furthermore, for a range query encompassing 50 nodes, the search time is less than 2 ms and 3 ms for the client and server, respectively. MDRSSE achieves semantic security under the IND-CPA assumption, all without requiring additional storage size at the server.

Complex case of systemic vasculitis

Vasculitis is a general term that defines a combined group of diseases based on immunopathological inflammation of blood vessels — arteries, arterioles, capillaries, venules, and veins. As a rule, the consequence of this disease is a change in the structure and function of organs supplied with blood by inflamed vessels. The causes of the disease remain unknown. A combination of genetic predisposition with environmental factors, as well as infections involving Staphylococcus aureus or hepatitis virus, are suspected. The article presents a case of systemic vasculitis, which required a complex differential diagnostic search and timely choice of treatment tactics.

Equity in Information Retrieval

Medical librarians and information specialists have unique evidence retrieval skills that can help health technology assessment organizations reduce bias in the evidence they use: They identify gaps in mainstream evidence sources and seek out alternatives that provide the perspectives of underrepresented populations. They create complex and efficient database searches that encompass the wide variety of synonyms and phrases used to describe health disparities and inequities. They use sound, logical, and efficient ways to access evidence from paywalled systems while respecting rights of ownership. They critically appraise information systems to determine if they emphasize biases and inequities or create new ones.

Prediction of daily tourism volume based on maximum correlation minimum redundancy feature selection and long short‐term memory network

AbstractHistorical tourism volume, search engine data, and weather calendar data have close causal relationship with daily tourism volume. However, when used in the prediction of daily tourism volume, the feature variables of the huge and complex search engine data do not have strong independence. These repetitive and highly relevant data must be analyzed and selected; otherwise, they will increase the training burden of neural network and reduce the prediction effect. This study proposes a daily tourism volume prediction model, maximum correlation minimum redundancy feature selection and long short‐term memory, on the basis of feature selection and deep learning. Firstly, the multivariate high‐dimensional features, including search engine data and weather factors, are selected to identify the key influencing factors. Secondly, the deep neural network is used to make a multistep forward rolling prediction of daily tourism volume. Results show that keywords of famous scenic spots, weather, historical tourism volume, and tourism strategies in the search engine data significantly improve the prediction accuracy of daily tourism volume. The proposed maximum correlation minimum redundancy feature selection and long short‐term memory model performs better than other models, such as autoregressive integrated moving average, multiple regression, support vector machine, and long short‐term memory.

The investment component of the development of the agrarian sector of the economy of Ukraine in the conditions of european integration: problems and prospects

The article examines the problems of investment support in the agricultural sector of Ukraine's economy in modern conditions and identifies promising directions for improving the investment climate. It has been established that in the process of creating a market economy in Ukraine and European integration processes, there is a need for complex searches for ways of development and determination of the role and place of the state in these economic transformations. The experience of developed democratic countries shows that there is no single solution to this problem, and the degree of state intervention in the economy of different countries varies. The directions of the transformation of the agrarian-industrial complex in Ukraine since independence have been determined. It has been proven that before the start of the full-scale war, the agricultural sector in Ukraine was one of the most profitable branches of the Ukrainian economy. It contributed to a significant contribution to the GDP and preservation of land resources in a satisfactory condition. A number of problems in the agrarian sector of Ukraine's economy, which inhibit its development, are highlighted: insufficient transparency in matters of land ownership; non-compliance with quality and safety standards established by international organizations; limited investment level; insufficient adaptation of Ukrainian legislation to the requirements of the European Union; the problem of reforming the market and its structure; dependence on state funding; lack of innovative activity; problems in the field of logistics and supplies. The assessment of direct losses in the field of agro-industrial complex and agricultural infrastructure, which were the result of constant attacks by the Russian armed forces, is presented. Among the components of these losses, the largest ones that have become the most financially significant should be indicated: agricultural machinery, finished agricultural products, and grain storages. It is these losses that can be recognized as significant and such that they do not allow us to talk about the rapid improvement of the situation in the agricultural sector of Ukraine and the attraction of investments. The main vectors of increasing the investment attractiveness of the agrarian sector of the Ukrainian economy are given and a list of measures that must be taken to solve the main problems of the agrarian sector of Ukraine during and after the war, as well as improving the investment component of the development of the agrarian sector of the economy and continuing the European integration processes, is presented.

Інноваційна діагностика розладів нейророзвитку в дітей

Today, genetic diagnosis methods are a powerful tool for a practicing doctor, which allows not only to establish the etiology of neurodevelopmental disorders in children, but also influences the further tactics of patient management, the choice of further diagnostic and therapeutic interventions, helps in predicting subsequent pregnancies for the family. Purpose - to analyze the information of modern specialized literature regarding the features of the use of modern cytogenetic methods of diagnosis, in particular, chromosomal micrometric analysis (СMA); give a clinical example of a child with a neurodevelopmental disorder and established chromosomal etiology. A review of the scientific literature regarding the advantages and features of using innovative methods of cytogenetic diagnostics (karyotyping, СMA), clinical manifestations of 2q13 microdeletion syndrome is given. A description of the clinical case of diagnosis of a child with 2q13 microdeletion syndrome is presented. A chromosomal micrometric analysis was performed, which revealed a heterozygous microdeletion on 2q13 with a size of 122 kilobases, which led to the loss of the NPHP1 and MALL genes. The obtained result of the CMA made it possible to optimize the tactics of monitoring the child, taking into account the increased risk of the development of kidney pathology and leukoencephalopathy, the recommended annual determination of the level of creatinine and urea in the blood, conducting an ultrasound of the kidneys and an MRI of the brain. Conclusions. The given clinical observation confirms the complexity of the diagnostic search for neurodevelopmental disorders in children. In children with autism spectrum disorder, developmental delay, intellectual disability, in the absence of epileptic seizures, regardless of the presence of dysmorphic facial features, it is recommended to begin the examination with CMA, and in children with epileptic encephalopathies, it is optimal to begin the examination with the next generation sequencing method (NGS), namely whole exome sequencing. The research was carried out in accordance with the principles of the Declaration of Helsinki. Informed consent of the children’s parents was obtained for the research. No conflict of interests was declared by the authors.

Levy Flight and Chaos Theory-Based Gravitational Search Algorithm for Image Segmentation

Image segmentation is one of the pivotal steps in image processing due to its enormous application potential in medical image analysis, data mining, and pattern recognition. In fact, image segmentation is the process of splitting an image into multiple parts in order to provide detailed information on different aspects of the image. Traditional image segmentation techniques suffer from local minima and premature convergence issues when exploring complex search spaces. Additionally, these techniques also take considerable runtime to find the optimal pixels as the threshold levels are increased. Therefore, in order to overcome the computational overhead and convergence problems of the multilevel thresholding process, a robust optimizer, namely the Levy flight and Chaos theory-based Gravitational Search Algorithm (LCGSA), is employed to perform the segmentation of the COVID-19 chest CT scan images. In LCGSA, exploration is carried out by Levy flight, while chaotic maps guarantee the exploitation of the search space. Meanwhile, Kapur’s entropy method is utilized for segmenting the image into various regions based on the pixel intensity values. To investigate the segmentation performance of ten chaotic versions of LCGSA, firstly, several benchmark images from the USC-SIPI database are considered for the numerical analysis. Secondly, the applicability of LCGSA for solving real-world image processing problems is examined by using various COVID-19 chest CT scan imaging datasets from the Kaggle database. Further, an ablation study is carried out on different chest CT scan images by considering ground truth images. Moreover, various qualitative and quantitative metrics are used for the performance evaluation. The overall analysis of the experimental results indicated the efficient performance of LCGSA over other peer algorithms in terms of taking less computational time and providing optimal values for image quality metrics.

Human Object Detection for Real-Time Camera using Mobilenet-SSD

Technology development is very rapid, so all fields are required to develop technology to increase the effectiveness and efficiency of work. One of the focuses is related to image processing technology. We can benefit from this system, so various fields have implemented image processing systems, such as security, health, and education. One of the current obstacles is safety, namely in searching for people, which is still done manually. Searching for teams to find people is often challenging because of the significant search area, low light conditions, and complex search fields. Therefore, we need a tool capable of detecting humans to assist in finding people. Therefore, to detect human objects, the authors try to research human object detection using a simple device for the human object detection system. The authors use the MobilenetV2-SSD, where this algorithm has high detection and accuracy. Using the mobilenetV2-SSD simulation method for human object recognition, a detection rate of 100% is obtained with an FPS value of 5.

Solver-In-The-Loop Cluster Resource Management for Database-as-a-Service

In Database-as-a-Service (DBaaS) clusters, resource management is a complex optimization problem that assigns tenants to nodes, subject to various constraints and objectives. Tenants share resources within a node, however, their resource demands can change over time and exhibit high variance. As tenants may accumulate large state, moving them to a different node becomes disruptive, making intelligent placement decisions crucial to avoid service disruption. Placement decisions need to account for dynamic changes in tenant resource demands, different causes of service disruption, and various placement constraints, giving rise to a complex search space. In this paper, we show how to bring combinatorial solvers to bear on this problem, formulating the objective of minimizing service disruption as an optimization problem amenable to fast solutions. We implemented our approach in the Service Fabric cluster manager codebase. Experiments show significant reductions in constraint violations and tenant moves, compared to the previous state-of-the-art, including the unmodified Service Fabric cluster manager, as well as recent research on DBaaS tenant placement.

Dual Elite Groups-Guided Differential Evolution for Global Numerical Optimization

Differential evolution (DE) has shown remarkable performance in solving continuous optimization problems. However, its optimization performance still encounters limitations when confronted with complex optimization problems with lots of local regions. To address this issue, this paper proposes a dual elite groups-guided mutation strategy called “DE/current-to-duelite/1” for DE. As a result, a novel DE variant called DEGGDE is developed. Instead of only using the elites in the current population to direct the evolution of all individuals, DEGGDE additionally maintains an archive to store the obsolete parent individuals and then assembles the elites in both the current population and the archive to guide the mutation of all individuals. In this way, the diversity of the guiding exemplars in the mutation is expectedly promoted. With the guidance of these diverse elites, a good balance between exploration of the complex search space and exploitation of the found promising regions is hopefully maintained in DEGGDE. As a result, DEGGDE expectedly achieves good optimization performance in solving complex optimization problems. A large number of experiments are conducted on the CEC’2017 benchmark set with three different dimension sizes to demonstrate the effectiveness of DEGGDE. Experimental results have confirmed that DEGGDE performs competitively with or even significantly better than eleven state-of-the-art and representative DE variants.