Golden Ball Research Articles

Isolation and Identification of Alternaria alternata from Potato Plants Affected by Leaf Spot Disease in Korea: Selection of Effective Fungicides.

Brown leaf spot disease caused by Alternaria spp. is among the most common diseases of potato crops. Typical brown spot symptoms were observed in commercial potato-cultivation areas of northern Korea from June to August 2020-2021. In total, 68 isolates were collected, and based on sequence analysis of the internal transcribed spacer (ITS) region, the collected isolates were identified as Alternaria spp. (80.9%). Phylogenetic analysis revealed that a majority of these isolates clustered within a clade that included A. alternata. Additionally, the ITS region and rpb2 yielded the most informative sequences for the identification of A. alternata. Pathogenicity tests confirmed that the collected pathogens elicited symptoms identical to those observed in the field. In pathogenicity tests performed on seven commercial cultivars, the pathogens exhibited strong virulence in both wound and non-wound inoculations. Among the cultivars tested, Arirang-1ho, Arirang-2ho, and Golden Ball were resistant to the pathogens. Furthermore, among the fungicides tested in vitro, mancozeb and difenoconazole were found to be effective for inhibiting mycelial growth. In summary, our findings suggest that A. alternata plays a critical role in leaf disease in potato-growing regions and emphasise the necessity of continuous monitoring and management to protect against this disease in Korea.

Vehicle re-routing under disruption in cross-dock network with time constraints

This study expands on the investigation of the vehicle routing problem with cross-dock (VRPCD) by incorporating disruption in a network with suppliers, customers, and a single cross-dock. It is assumed that the disruption (e.g., traffic accident or mechanical failure) impacts the scheduled pickup times of vehicles carrying freight; hence, pickup or delivery is affected by late vehicle arrivals to suppliers, cross-docks, and customers as well as increased transportation cost for the carrier. To help decision makers mitigate the impact of this type of disruption, mixed-integer linear programs (MILPs) are developed to model VRPCD with time-window constraints under both normal and disrupted conditions that will quantify the impact of disruption. The Golden Ball metaheuristic is adapted to provide an optimal or near-optimal solution of the MILPs in a short time period. To demonstrate the effectiveness of the proposed models and algorithms, numerical experiments are performed on hypothetical networks containing a realistic number of pickup and delivery locations. Results from numerical experiments and ANOVA analysis showed that: 1) the cross-dock is able to cope with the disruption when the number of disrupted nodes is small, 2) early breakdown of an inbound vehicle with fewer nodes on its routes causes smaller service delays, 3) as the number of disrupted nodes increases, it is advantageous to reroute outbound vehicles, and 4) when the breakdown occurs earlier in the planning horizon, it is better to reroute both inbound and outbound vehicles whereas only outbound vehicles should be rerouted when the breakdown happens later in the day.

Vulcanization failure mechanism analysis of lead-frame LED package

Light-emitting diodes (LED) failure can be attributed to various factors such as vulcanization, which is considered as one of the primary causes. The main consequence of vulcanization is the production of black silver sulfide, a result of silver and sulfur ion interaction within the plating layer. The consequences of this reaction can be quite severe, leading to a reduction in luminous flux, color temperature drift, golden ball and support fracture. In this work, following an analysis of the process structure characteristics of LED support, four vulcanization failure paths have been identified. To determine the main vulcanization path of the LED stent, various tests, such as the vulcanization corrosion test, the red ink test, and the cross-section test, have been conducted. This work provides an innovative and constructive approach for directing failure research in LED package.

Football player ranking generated by multivariate methods

The objective was to build a ranking of football players based on multivariate methods. The players evaluated were the winners of FIFA; UEFA; Ballon D'Or; FIFPro; World Soccer; Golden Ball World Cup and King of América awards. A total of 95 players were evaluated based on the variables: games played, wins, draws, losses, goals scored, yellow/red cards, awards, dream team, title in national leagues/ continental championships in a football club, title in world cups/ continental championship in a national team, Olympics gold medal. We performed the multivariate clustering and principal components analyses. The grouping technique resulted in three groups: regionally renowned players (Group 1), renowned players for their extraordinary performance (Group 2), and globally renowned players (Group 3). The players in Group 2, except for Maradona, reached the highest indexes. The leaders of the ranking were Messi, Cristiano Ronaldo, Xavi, Pelé, and Iniesta.

A new method for logic optimization of QCA-based circuits using a golden ball algorithm

Quantum-dot Cellular Automata (QCA) is an approach for constructing low-power digital circuits and various high-performance calculations at the nano-scale. This technique has little power dissipation due to its nature. Implementing circuits using QCA is an active research topic because of the necessary qualities, like short switching time, high device density, and comparatively low power. The primary gates in this technology are the inverter and majority gates, which can function as universal logic gates to build any circuit. This study provides an effective approach to synthesize multi-output Boolean functions using QCA circuits. Due to the NP-hard nature of the logic optimization problem, a golden ball algorithm-based method is utilized to decrease delay time and energy usage in the design process. The simulations utilizing MATLAB software are used to test the functionality of the suggested approach. The empirical findings indicate that the suggested technique performs better than prior methods in numerous instances.

A learning enhanced golden ball algorithm for the vehicle routing problem with backhauls and time windows

The vehicle routing problem and its variants are staples in addressing logistics problems. This widespread practicality combined with the computation complexity of these problems has led to a significant amount of research through the years. In this paper, a learning enhanced golden ball algorithm is developed to address the vehicle routing problem with backhaul and time windows. The meta-heuristic is inspired by competitive soccer league concepts. Many modifications to previous golden ball algorithm implementations were required but three new elements have been developed. First, a forbidden list technique improves the initial solutions used by the algorithm. Second, a new operator, flexible crossover, is proposed to improve solutions. Finally, a structural change to the sequence of operations was implemented to increase the search domain and thereby reduce the chance of having the algorithm get stuck at a local optimum. The algorithm was applied to 45 well-known standard problems from the literature that other researchers have used to test different approaches. So, each instance has best-known solutions that have been improved through the years using a variety of approaches. The algorithm proposed here found equivalent or superior solutions to 40 of the 45 problems. Further, stable solutions were found for larger problems using a desktop computer in less than two hours so this could be useful in practice.

Copy-move forgery detection of medical images using golden ball optimization

The medical images can be tampered with by attackers with a malevolent goal of hiding or creating multiple copies of lesions, resulting in wrong treatment, false insurance claim, defame public figures, and so on. Such forgery demands authentication of digital images before starting the diagnosis and treatment of the patients. The existing key-point (KP) based forgery detection methods may not uniformly distribute the KPs on the images, thereby making the detection process to fail for images with forgeries in smooth regions. This paper attempts to employ a minimum Eigen-value algorithm that distributes the KPs in the entire image region and apply speeded up robust features and singular value decomposition for obtaining reduced descriptors at identified KPs. Besides, the method applies golden ball-based optimization, inspired by the behaviour of players in team-based sports games, for optimal clustering of the evaluated features. This paper studies the performances of the suggested method on 300 medical images and compares the results with the existing methods. It exhibits that the precision, specificity, sensitivity, and accuracy of the developed method are superior to the existing methods. Though the suggested method outperforms, it can further be improved by combining it with block-based methods.

ВАЛЕРІЙ ЛОБАНОВСЬКИЙ ЯК ПЕДАГОГ

 The article is devoted to the pedagogical directions of the activity of the outstanding Ukrainian coach Valeriy Lobanovskyi. The pur­pose of the study is to highlight the facets of the phenomenon of the out­standing Ukrainian football player, coach Valeriy Vasilyevich Lobanovskyi, namely his personality as an educator. Material and research methods. The use of various sources of information, but primarily literary and docu­mentary, helped to minimize subjective views and judgments. We also used a critical approach to the choice of available sources of informa­tion: the selection took place after their search in the database Scopus, SPORT Discus, Web of Science; chose sources whose content concerned the activities of Valeriy Lobanovskyi. The following keywords were used in the search: pedagogical activity, football coach, sports cybernetics, game models, as well as corresponding abbreviations and combinations. The time aspect of the appearance of information on a certain topic was also taken into account. Similarly, there was a direct study of printed litera­ture sources, including information from journal articles on lists of used literature. Thus, more than 50 sources of information were selected, after which the last review was conducted and 28 sources of information were identified, the material of which was processed using adequate general scientific methods, including analysis and synthesis, systematization, generalization. Results. The article focuses on the advanced tactical and methodological and pedagogical techniques used by Valeriy Lobanovskyi in his professional activity. The use of such techniques allowed him to become one of the creators of the modern system of football training and enter the constellation of the most outstanding coaches who changed the history of football. One of the results of his work as a teacher was the upbringing of three Golden Ball winners from France Football. Con­clusions. Valeriy Lobanovskyi is one of the most authoritative football coaches in the world and the founder of methodological and pedagogi­cal ideas that have ensured the transformation of football methods to the realities and challenges of today. He is an extraordinary person with emotional aspirations, the charisma of character, meticulous adherence to the developed norms of behavior and demanding in this regard from his students, a scientifically proven approach to taking into account every detail when organizing a football game.

EXPERIMENTAL PROGRAMME ON THE UDMURT LANGUAGE AND CULTURE FOR PRESCHOOL CHILDREN IN A POLYETHNIC REGION

The paper discusses the issues of teaching nationality languages of Russia to preschool children, taking the Udmurt language and culture as an example. The relevance of the study is dermined by the scientific data according to which ethnic stereotypes in behaviour are formed most intensively in preschool age. Getting acquainted with new cultures and languages is particularly important for preschool education in multinational regions as it prevents the development of ethnocentric personalities. The paper aimed at a detailed analysis of the didactic and substantive content of the educational programme “Zarni bugor” (“Golden ball”) (hereinafter referred as the Programme) with the elements of teaching the Udmurt language to children from 4 to 7; the Programme is to be realised within three years in the Russian group of kindergarten № 5 in the town of Agryz, Republic of Tatarstan. The novelty of the Programme lies in introducing the Udmurt language (along with Tatar and Russian) into the educational process of the kindergarten. The research methods are theoretical (the study of academic literature on the problems of intercultural education in a multi-ethnic region) and empirical (observation and behavioral analysis of preschoolers). The system of exercises included into the Programme, its content, the forms and methods of teaching preschoolers present a consistant didactic material for teaching the Udmurt language and culture in this age group. The programme is expected to provide a high level of intercultural competence corresponding to the age of children, it is supposed to arouse their interest in the Udmurt language and culture, make them open for perception of other cultures.

A Fuzzy Inference System for Players Evaluation in Multi-Player Sports: The Football Study Case

Decision support systems often involve taking into account many factors that influence the choice of existing options. Besides, given the expert’s uncertainty on how to express the relationships between the collected data, it is not easy to define how to choose optimal solutions. Such problems also arise in sport, where coaches or players have many variants to choose from when conducting training or selecting the composition of players for competitions. In this paper, an objective fuzzy inference system based on fuzzy logic to evaluate players in team sports is proposed on the example of football. Based on the Characteristic Objects Method (COMET), a multi-criteria model has been developed to evaluate players on the positions of forwards based on their match statistics. The study has shown that this method can be used effectively in assessing players based on their performance. The COMET method was chosen because of its unique properties. It is one of the few methods that allow identifying the model without giving weightings of decision criteria. Symmetrical and asymmetrical fuzzy triangular numbers were used in model identification. Using the calculated derivatives in the point, it turned out that the criteria weights change in the problem state space. This prevents the use of other multi-criteria decision analysis (MCDA) methods. However, we compare the obtained model with the Technique of Order Preference Similarity (TOPSIS) method in order to better show the advantage of the proposed approach. The results from the objectified COMET model were compared with subjective rankings such as Golden Ball and player value.

Identification of Players Ranking in E-Sport

Human activity is moving steadily to virtual reality. More and more, people from all over the world are keen on growing fascination with e-sport. In practice, e-sport is a type of sport in which players compete using computer games. The competitions in games, like FIFA, Dota2, the League of Legends, and Counter-Strike, are prestigious tournaments with a global reach and a budget of millions of dollars. On the other hand, reliable player ranking is a critical issue in both classic and e-sport. For example, the “Golden Ball” is the most valuable prize for an individual football player in the whole football history. Moreover, the entire players’ world wants to know who the best player is. The position of each player in the ranking depends on the assessment of his skills and predispositions. In this paper, we studied identification of players evaluation and ranking obtained using the multiple-criteria decision-making based method called Characteristic Objects METhod (COMET) on the example of the popular game Counter-Strike: Global Offensive (CS: GO). We present a range of advantages of the player evaluation model created using the COMET method and, therefore, prove the practicality of using multi-criteria decision analysis (MCDA) methods to build multi-criteria assessment models in emerging areas of eSports. Thus, we provide a methodical and practical background for building a decision support system engine for the evaluation of players in several eSports.

EFFECT OF INTEGRATED MANAGEMENT OF FYM AND UREA IN CABBAGE GROWN ON CLAY LOAM SOIL, DANG, NEPAL

A field experiment was conducted at the field of CAMPUS OF LIVE SCIENCES, Dang having clay loam soil to evaluate effect of integrated management of FYM and urea in cabbage. This experiment was carried out in Randomized Complete Block Design (RCBD) having six treatments with three replications. Golden Ball variety of cabbage was selected for this research as a test crop. The application of FYM and urea shows significant result on the plant height, number of leaves, length of plant, head diameter and biomass yield. The maximum head diameter was observed in treatment 50%FYM +50% UREA (14.31cm).The highest total yield (9.63Mt/ha) and highest economic yield (6.16Mt/ha) was obtained from treatment 50%UREA +50 %FYM which was similar with recommend rate of fertilizer for cabbage. The highest soil pH (6.40), soil moister content (40.46%), the lowest bulk density (1.12g/cm3) was obtained from FYM application. The highest organic matter content (1.46) was also observed from FYM application. The most of the soil properties were enhanced by application of FYM and UREA. Furthermore, FYM played important role in maintaining soil physical property. In addition, this study indicates the possibility of FYM application to reduce nutrient risk by enhancing physio-chemical properties of clay loam soil. This result will helpful for the student and scientists in further research to increase the productivity and for the improvement of soil property.

Similar Arbuscular Mycorrhizal Fungal Communities in 31 Durum Wheat Cultivars (Triticum turgidum L. var. durum) Under Field Conditions in Eastern Canada.

Wheat is among the important crops harnessed by humans whose breeding efforts resulted in a diversity of genotypes with contrasting traits. The goal of this study was to determine whether different old and new cultivars of durum wheat (Triticum turgidum L. var. durum) recruit specific arbuscular mycorrhizal (AM) fungal communities from indigenous AM fungal populations of soil under field conditions. A historical set of five landraces and 26 durum wheat cultivars were field cultivated in a humid climate in Eastern Canada, under phosphorus-limiting conditions. To characterize the community of AMF inhabiting bulk soil, rhizosphere, and roots, MiSeq amplicon sequencing targeting the 18S rRNA gene (SSU) was performed on total DNAs using a nested PCR approach. Mycorrhizal colonization was estimated using root staining and microscope observations. A total of 317 amplicon sequence variants (ASVs) were identified as belonging to Glomeromycota. The core AM fungal community (i.e., ASVs present in > 50% of the samples) in the soil, rhizosphere, and root included 29, 30, and 29 ASVs, respectively. ASVs from the genera Funneliformis, Claroideoglomus, and Rhizophagus represented 37%, 18.6%, and 14.7% of the sequences recovered in the rarefied dataset, respectively. The two most abundant ASVs had sequence homology with the 18S sequences from well-identified herbarium cultures of Funneliformis mosseae BEG12 and Rhizophagus irregularis DAOM 197198, while the third most abundant ASV was assigned to the genus Paraglomus. Cultivars showed no significant difference of the percentage of root colonization ranging from 57.8% in Arnautka to 84.0% in AC Navigator. Cultivars were generally associated with similar soil, rhizosphere, and root communities, but the abundance of F. mosseae, R. irregularis, and Claroideoglomus sp. sequences varied in Eurostar, Golden Ball, and Wakooma. Although these results were obtained in one field trial using a non-restricted pool of durum wheat and at the time of sampling, that may have filtered the community in biotopes. The low genetic variation between durum wheat cultivars for the diversity of AM symbiosis at the species level suggests breeding resources need not be committed to leveraging plant selective influence through the use of traditional methods for genotype development.

A computer vision approach for recognition of the engagement of pigs with different enrichment objects

As providing objects that pigs prefer can reduce the occurrence of tail-biting and aggression and consequently improve animal welfare, automatic recognition of pigs’ engagement with different objects can have practical value. Therefore, aim of this study was to develop a computer vision based approach that utilised a recurrent neural network-based deep learning algorithm to recognise pig enrichment engagement (EE) behaviours and preliminarily determine the preference to objects. Two pig pens were studied. 1 day of video was recorded in pen 1, which generated 2400 1 s EE and 2400 1 s non-EE episodes. 80% of these data was randomly selected as training set and the remaining 20% as validation set. Moreover, 4 days of video were recorded and used as the test set in pen 2. Firstly, the HSV (Hue, Saturation, Value) colour space-based tracking algorithm was developed to locate object region of interest. Secondly, the convolutional neural network (CNN) architecture InceptionV3 was used to extract spatial features from each frame. These features were input into the long short-term memory (LSTM) framework to extract spatial-temporal features from each episode. Through the fully connected layer, the prediction function Softmax was finally used to classify these episodes as EE or non-EE behaviour. In the validation set, the proposed algorithm could recognise EE with blue ball, golden ball and wooden beam with an accuracy of 95.2%, 95.4% and 97.3%, respectively. By shortening the radius of the region of interest into a half of the average length of pig body, the corresponding accuracy could be improved into 96.9%, 97.1% and 97.9%, respectively. In the test set, the proposed algorithm could recognise EE with each of these 3 objects with an accuracy of 96.5%, 96.8% and 97.6%, respectively. The proportion of EE with each of these 3 objects was 75.8%, 6.0% and 18.2%, respectively. These results indicate that the proposed method can be used to recognise EE behaviours of pigs, and halving the radius of the region of interest can improve the recognition accuracy of EE behaviours. Moreover, the preference of pigs to objects based on EE duration were preliminarily determined as blue ball > wooden beam > golden ball. The obtained duration of EE behaviours can help farmers to evaluate the enrichment used and thereby to increase the health and welfare of the pigs in their care. Furthermore, the proposed algorithm has reference value for the classification of the behaviours with similar motion patterns.

SERS-based immunocapture and detection of pathogenic bacteria using aboronic acid-functionalized polydopamine-coated Au@Ag nanoprobe.

A surface-enhanced Raman scattering (SERS)-based immunocapture nanoprobe is described for the detection of pathogenic bacteria. The probe uses boronic acid-functionalized polydopamine-coated Au@Ag nanoparticles as an advanced SERS nanotag. Modified magnetic IgG@Fe3O4 nanoparticles are used for magnetic separation. Au@Ag@PDA nanoparticles, where PDA stands for polydopamine, were functionalized with boronic acid to bind to pathogenic bacteria and induce signal amplification. The Raman signal is amplified 108 times when the SERS tag binds the surface of bacteria. The SERS spectra exhibit fingerprint-like patterns that enable bacterial classification. The results of principal component analysis (PCA) and hierarchical cluster analysis (HCA) of the spectral regions were compared. The bacterial surface protein and glycan signals (1300-1450cm-1) were the best regions for bacterial classification. Staphylococcus aureus, Escherichia coli, Shigella dysenteriae, Pseudomonas aeruginosa, and Klebsiella pneumonia were successfully classified by this method. The lowest detection limit was 10 colonies/mL (CFU·mL). The assay can be completed within 30min. Conceivably, this method may be extended to the quantitative detection or classification of bacteria under various other conditions. Graphical abstract Schematic representation of immunocapture and detection of pathogenic bacteria using boronic acid-functionalized polydopamine-coated Au@Ag nanoprobe through the bacterial surface protein and glycan signals. Green arrow: laser; black arrow: SERS; red ball: bacteria; grey ball: IgG@Fe3O4; golden ball: boronic acid-functionalized Au@Ag@PDA.

Development of technology for a fermented drink with cancer protective properties

Some plant sources of biologically active substances particularly cruciferous vegetables, both in native form and as extracts, display effects towards carcinogenesis that make these food sources cancer protective and recurrence preventive agents. In the currently developing tendency towards functional foodstuffs like dairy products targeted at nutritional-dependent diseases, development of a dairy beverage containing sulforaphane deserves attention. Creating a recipe for such a product includes: selecting a plant sulforaphane source, testing its compatibility with the milk base, choosing the addition dosage and methods and experimental beverage preparation. In pursuit of these goals in the study, firstly, turnip (Brassica rapa l.) root, an affordable sulforaphane provider, was chosen. Glucoraphanin content in various turnip varieties was determined and the Golden Ball variety selected. Sulforaphane was introduced to milk in turnip purée and aqueous extract forms. The effect of purée/extract dosage on milk fermentation process and the final producťs quality parameters was investigated. Both purée and extract containing products had a pleasant taste with a vegetable note and uniform consistency. Sulforaphane content in 200 g beverage servings was (23.5 ± 2.1) mg and (29.5 ± 0.5) mg (extract), both values approximately one tenth of therapeutic dose estimates, which is enough for cancer preventive purposes.

FEATURES OF BRASSICA RAPA'S GROWTH AND DEVELOPMENT, DEPENDING ON THE TERRITORY OF SOWING THE SEED

In order to study the effect of the time of sowing seeds on the passage of morphogenesis and productivity of rapeseed, the scheme included experiments where the seeds were sown in I, II, III in April, I and II in May. The experiment used varieties of Golden Ball, Purpurleopop, Purpurov and Geisha, and control was the variant in which the seeds of the Golden Ball varieties were sown in the second decade of April with a row spacing of 45 cm. laboratory - to determine the total yield; statistical - to establish the reliability of the investigated factors. Seed germination analysis used in the experiment ranged from 78.9% to 81.1% on average. The highest indicator of seed germination was characterized by the Purpulepop variety, where the studied value was 83.2%, which was dominated by the seed germination of the control variant by 2.1%. In the early stages of sowing seeds, the period of seedling formation was shorter. Seeding of seeds in the first two decades of April on the surface of the soil was observed for 8 days. The short sprouting period was characterized by all varieties of turnips. As a result of sowing turnip seeds at a later date, namely in the third decade of April - the first and second decade of May and the setting of a higher soil temperature, the period of germination was extended and ranged from 8 to 10 days. By using the term of sowing seeds I-II decade of April, the short interphase period was characterized by Purpleupop and Purpurova varieties, where the specified period was only 22 days. The sowing of turnip seeds at a later date ensured the formation of an interphase period of "seedlings - leaf rosette" in 23-25 days except Purpulopop variety. The interphase period "leaf rosette - the beginning of rooting" and "the beginning of rooting - technical maturity" determined the benefits of early seeding, namely in the first decade of April on varieties Purpulopop and Purpurov.The short vegetation period was characterized by the Golden Ball plants for sowing seeds in the first and third decades of April and the first of May. During the specified seeding period the duration of the growing season was only 40 days. A longer vegetation period was characterized by plants of the Geisha variety for sowing seeds in the first two decades of April. As a result of the use of the researched elements of technology to grow turnips in open soil, the total yield varied from 11.0 t / ha to 24.0 t / ha. The most productive is the first sowing of rapeseed, where its value ranged from 20.6 t / ha to 22.7 t / ha. At the same time, when sowing seeds in the first decade of April, the yield of turnip varieties Purpulepop was the highest and was 22.7 t / ha, or the increase was 2.4 t / ha.

The Sun Ray as a Tool to Design an Architectural Form

The regularities of constructing crucial architectural forms are shown based on the architecture of Ancient Egypt, Western Europe, and Russia: obelisks, pyramids, statues, tents, and spires. It is shown that the source of projection for these and some other architectural forms was the Sun 1 or its image in the shape of a golden ball. The sun ray is serving as a tool for solar elevation.

A Hybrid Meta heuristic Algorithm for the Balanced Line Production under Uncertainty

This study proposes a hybrid Golden Ball Algorithm for solving a balanced line production for a garment firm in Thailand. At present, production lines are those in which the timing of the job movement between stations is coordinated in such a way that all of the jobs are indexed simultaneously via some heuristic sequencing or dispatching rules. This research studies the balanced line production problem with some stochastic patterns, and develops a Golden Ball Algorithm or GBA and its variants to solve the problem. To assess the effectiveness of the proposed hybrid algorithm, a computational study is conducted for both deterministic and stochastic patterns of the problem. The comparisons are made for two different levels of processing times and due date. It can be concluded that the variant HGBA2 of the algorithm by adjusting answers of the successor function on both custom training and successor phases, is slightly more effective than the other hybrid approaches in terms of quality of solutions under uncertainty.

Hybrid Algorithm for Solving the Quadratic Assignment Problem

The Quadratic Assignment Problem (QAP) is a combinatorial optimization problem; it belongs to the class of NP-hard problems. This problem is applied in various fields such as hospital layout, scheduling parallel production lines and analyzing chemical reactions for organic compounds. In this paper we propose an application of Golden Ball algorithm mixed with Simulated Annealing (GBSA) to solve QAP. This algorithm is based on different concepts of football. The simulated annealing search can be blocked in a local optimum due to the unacceptable movements; our proposed strategy guides the simulated annealing search to escape from the local optima and to explore in an efficient way the search space. To validate the proposed approach, numerous simulations were conducted on 64 instances of QAPLIB to compare GBSA with existing algorithms in the literature of QAP. The obtained numerical results show that the GBSA produces optimal solutions in reasonable time; it has the better computational time. This work demonstrates that our proposed adaptation is effective in solving the quadratic assignment problem.