Stagnation Issues Research Articles

Stochastic optimal power flow framework with incorporation of wind turbines and solar PVs using improved liver cancer algorithm

AbstractThe present study introduces a nature inspired improved liver cancer algorithm (ILCA) for solving the non‐convex engineering optimization issues. The traditional LCA (t‐LCA) inspires from the conduct of liver tumours and integrates biological ethics during the optimization procedure. However, t‐LCA facing stagnation issues and may trap into local optima. To avoid such issues and provide the optimal solution, there are some modifications are implemented into the internal structure of t‐LCA based on Weibull flight operator, mutation‐based approach, quasi‐opposite‐based learning and gorilla troops exploitation‐based mechanisms to enhance the overall strength of the algorithm to obtain the global solution. For validation of ILCA, the non‐parametric and the statistical analysis are performed using benchmark standard functions. Moreover, ILCA is applied to resolve the stochastic renewable‐based (wind turbines + PVs) optimal power flow problem using a modified RER‐based IEEE 57‐bus. The objective of this work is to obtain the minimum predicted power losses and enhance the predicted voltage stability. By incorporation of renewable resources into the modified IEEE57‐bus network can help the system to reduce the power losses from 5.6622 to 3.8142 MW, while the voltage stability is enhanced from 0.1700 to 0.1164 p.u.

A differential evolution framework based on the fluid model for feature selection

Feature selection in machine learning is a crucial step to effectively address the issue of feature redundancy in classification problems. Numerous feature selection algorithms have been developed to minimize the number of features, reduce computational cost, and improve classification accuracy. Differential evolution algorithms have the advantage of being simple in structure, robust, fast in convergence, and frequently used to solve feature selection problems. However, it is worth noting that differential evolution algorithms are susceptible to local optimum and stagnation issues, particularly when applied to high-dimensional data. To address this issue, in this study, we propose a differential evolution framework based on the fluid model, named DEF-FM, for feature selection. DEF-FM has the capability to speed up the convergence of differential evolution algorithms and alleviate the effects of local optima. The proposed framework is validated and compared against eight popular differential evolution algorithms using 12 publicly available benchmark datasets and experimental results unequivocally demonstrate the superiority of the proposed framework.

Addressing the stagnation issue in accounting education research while incorporating mainstream publications and professional practice into the accounting curriculum

The accounting practice and academic research-oriented sides of the profession have had difficulties concerning the integration of their outputs. The practice side has maintained that very little of the “mainstream” research produced by academics has been of value in their day to day activities, and the research side has complained about the lack of cooperation as to the availability of data from the practice segment that is needed to conduct academic research. Add to this situation the teaching focused faculty of the profession, who contend that the research conducted in the “mainstream” is of little value to their efforts in teaching undergraduate and graduate students. Additionally, faculty in the classroom need input from the practice side to determine what information the students need to be successful after graduation. The problems were noted in 2012 by the AICPA and AAA supported Pathways Commission in their major report, with questionable efforts to date addressing the concerns. Take all of these components together and you have a view of our current “situation” in accounting.We attempt, in this paper, to further address the previously identified stagnation in accounting education research (Rebele and St. Pierre, 2015) and, at the same time, to bridge the gaps between these different accounting groups noted by the Pathways Commission. We do this by offering a potentially unique solution to address the current accounting environment. First, we analyze the findings from a mainstream research study that both examines the practice side of the equation and directly affects our classroom coverage of topics in several of the required undergraduate and graduate courses typically found in accounting programs around the country. Second, this paper breaks from many of the current education articles in that we examine what should be taught in the classroom based on actual real - world practice issues rather than hypothetical academic approaches to the topics covered. Third, our efforts in tying together the three components - mainstream research, practice, and classroom content - will hopefully move our colleagues in a direction that benefits our students and opens new accounting education research opportunities.

An Enhanced Ant Colony System Algorithm Based on Subpaths for Solving the Capacitated Vehicle Routing Problem

The capacitated vehicle routing problem (CVRP) is regarded as an NP-hard problem. Moreover, the CVRP is described as a model that can be used in many applications such as transport, logistics, and distribution. The exact algorithms can find exact optimal solutions on the small-sized problem instances; however, for large-sized instances it is difficult to find the exact optimal solutions in polynomial time. This reason motivated the researchers to present heuristic/metaheuristic algorithms to solve large-sized problem instances within a reasonable computational time. One of the good algorithms that deal with the CVRP is the ant colony optimization (ACO) algorithm. Several ACO algorithms have been suggested in the literature, such as the ant system (AS) algorithm, ant colony system (ACS) algorithm, and so on. On the other hand, ACO is designed to solve the path problem that finds the best way. However, this algorithm still lacks exploratory mechanisms, which results in premature convergence and stagnation issues. Therefore, we propose to develop an enhanced ACS (EACS) algorithm for solving the CVRP based on subpaths. In our proposed algorithm, we propose to utilize the K-nearest neighbour (KNN) algorithm for finding the best initial solution and then enhance the diversity mechanism of the proposed algorithm by avoiding the generation of the same solution using subpaths. This uses the diversity of the generated solution to find a better solution with a shorter route in a reasonable amount of computational time. Furthermore, we propose to apply the three-opt algorithm to the completed subtour and the k-opt algorithm to the subpath gained from the experience of the subpath. Finally, to verify the effectiveness of the proposed EACS algorithm, the algorithm is tested on some CVRP instances and is compared with one of the state-of-the-art methods, namely, the enhanced simulated annealing algorithm. The comparative study showed a better performance of our EACS compared to the enhanced simulated annealing algorithm.

Enhancing Swarm Intelligence for Obstacle Avoidance with Multi-Strategy and Improved Dung Beetle Optimization Algorithm in Mobile Robot Navigation

The Dung Beetle Optimization (DBO) algorithm is a powerful metaheuristic algorithm that is widely used for optimization problems. However, the DBO algorithm has limitations in balancing global exploration and local exploitation capabilities, often leading to getting stuck in local optima. To overcome these limitations and address global optimization problems, this study introduces the Multi-Strategy and Improved DBO (MSIDBO) Algorithm. The MSIDBO algorithm incorporates several advanced computational techniques to enhance its performance. Firstly, it introduces a random reverse learning strategy to improve population diversity and mitigate early convergence or local stagnation issues present in the DBO algorithm. Additionally, a fitness-distance balancing strategy is employed to better manage the trade-off between diversity and convergence within the population. Furthermore, the algorithm utilizes a spiral foraging strategy to enhance precision, promote strong exploratory capabilities, and prevent being trapped in local optima. To further enhance the global search ability and particle utilization of the MSIDBO algorithm, it combines the Optimal Dimension-Wise Gaussian Mutation strategy. By minimizing premature convergence, population diversity is increased, and the convergence of the algorithm is accelerated. This expansion of the search space reduces the likelihood of being trapped in local optima during the evolutionary process. To demonstrate the effectiveness of the MSIDBO algorithm, extensive experiments are conducted using benchmark test functions, comparing its performance against other well-known metaheuristic algorithms. The results highlight the feasibility and superiority of MSIDBO in solving optimization problems. Moreover, the MSIDBO algorithm is applied to path planning simulation experiments to showcase its practical application potential. A comparison with the DBO algorithm shows that MSIDBO generates shorter and faster paths, effectively addressing real-world application problems.

Appling the Roulette Wheel Selection Approach to Address the Issues of Premature Convergence and Stagnation in the Discrete Differential Evolution Algorithm

The discrete differential evolution (DDE) algorithm is an evolutionary algorithm (EA) that has effectively solved challenging optimization problems. However, like many other EAs, it still faces problems such as premature convergence and stagnation during the iterative process. To address these concerns in the DDE algorithm, this work aims to achieve the following objectives: (i) investigate the causes of premature convergence and stagnation in the DDE algorithm; (ii) propose techniques to prevent premature convergence and stagnation in DDE, including a quantitative measurement of premature convergence based on the level of mismatching between the population solutions and then divide the population into individual groups based on the level of mismatching between the population solutions and the best solution; and applying the roulette wheel selection (RWS) approach to determine whether a higher degree of nonmatching is more suitable for choosing a population of separate groups to be able to produce a new solution with more options to prevent the occurrence of premature convergence; (iii) evaluate the effectiveness of the proposed techniques through employing the DDE algorithm to solve the quadratic assignment problem (QAP) as a standard to evaluate our results and their effect on avoiding premature convergence and stagnation issues, which led to the enhancement of the algorithm’s accuracy. Our comparative study based on the statistical analysis shows that the DDE algorithm that uses the proposed techniques is more efficient than the traditional DDE algorithm and the state-of-the-art methods.

Rotary Ripper: A Possible Solution to Increase the Efficiency of Tillage Operations

Manufacturers are looking for innovative solutions to improve the sustainability of their products in terms of environmental, economic, and social issues. Many studies demonstrate that conservative tillage techniques can be more advantageous for the environment and farmer profits than conventional tillage techniques. However, conservative tillage tools have certain disadvantages, including challenging weed control and stagnation issues in humid conditions due to low soil porosity at depth. In this study, field tests were conducted comparing the performances of a conventional tillage technique, using a ripper and a rotary tiller, and the usage of an innovative rotary ripper (Rotoripper). The comparison was performed in terms of energy requirements, through data acquisition during tillage operations, tilled soil quality, through soil sieving and cone penetration tests, and ownership costs, through acquired field data and literature databases. The results indicate that increased porosity of the soil in the deepest layer and increased cost-effectiveness are the main advantages attainable with the use of the Rotoripper instead of conventional tillage equipment. However, because of the low soil segregation level achieved with the Rotoripper, additional tillage activities are required before planting.

Lagrange radial basis function collocation method for boundary value problems in $ 1 $D

<abstract><p>This paper introduces the Lagrange collocation method with radial basis functions (LRBF) as a novel approach to solving 1D partial differential equations. Our method addresses the trade-off principle, which is a key challenge in standard RBF collocation methods, by maintaining the accuracy and convergence of the numerical solution, while improving the stability and efficiency. We prove the existence and uniqueness of the numerical solution for specific differential operators, such as the Laplacian operator, and for positive definite RBFs. Additionally, we introduce a perturbation into the main matrix, thereby developing the perturbed LRBF method (PLRBF); this allows for the application of Cholesky decomposition, which significantly reduces the condition number of the matrix to its square root, resulting in the CPLRBF method. In return, this enables us to choose a large value for the shape parameter without compromising stability and accuracy, provided that the perturbation is carefully selected. By doing so, highly accurate solutions can be achieved at an early level, significantly reducing central processing unit (CPU) time. Furthermore, to overcome stagnation issues in the RBF collocation method, we combine LRBF and CPLRBF with multilevel techniques and obtain the Multilevel PLRBF (MuCPLRBF) technique. We illustrate the stability, accuracy, convergence, and efficiency of the presented methods in numerical experiments with a 1D Poisson equation. Although our approach is presented for 1D, we expect to be able to extend it to higher dimensions in future work.</p></abstract>

An Improved Machine Learning Model with Hybrid Technique in VANET for Robust Communication

The vehicular ad hoc network, VANET, is one of the most popular and promising technologies in intelligent transportation today. However, VANET is susceptible to several vulnerabilities that result in an intrusion. This intrusion must be solved before VANET technology can be adopted. In this study, we suggest a unique machine learning technique to improve VANET’s effectiveness. The proposed method incorporates two phases. Phase I detects the DDoS attack using a novel machine learning technique called SVM-HHO, which provides information about the vehicle. Phase II mitigates the impact of a DDoS attack and allocates bandwidth using a reliable resources management technique based on the hybrid whale dragonfly optimization algorithm (H-WDFOA). This proposed model could be an effective technique predicting and utilizing reliable information that provides effective results in smart vehicles. The novel machine learning-based technique was implemented through MATLAB and NS2 platforms. Network quality measurements included congestion, transit, collision, and QoS awareness cost. Based on the constraints, a different cost framework was designed. In addition, data preprocessing of the QoS factor and total routing costs were considered. Rider integrated cuckoo search (RI-CS) is a novel optimization algorithm that combines the concepts of the rider optimization algorithm (ROA) and cuckoo search (CS) to determine the optimal route with the lowest routing cost. The enhanced hybrid ant colony optimization routing protocol (EHACORP) is a networking technology that increases efficiency by utilizing the shortest route. The shortest path of the proposed protocol had the lowest communication overhead and the fewest number of hops between sending and receiving vehicles. The EHACORP involved two stages. To find the distance between cars in phase 1, EHACORP employed a method for calculating distance. Using starting point ant colony optimization, the ants were guided in phase 2 to develop the shortest route with the least number of connections to send information. The relatively short approach increases protocol efficiency in every way. The pairing of DCM and SBACO at H-WDFOA-VANET accelerated packet processing, reduced ant search time, eliminated blind broadcasting, and prevented stagnation issues. The delivery ratio and throughput of the H-WDFOA-packet VANET benefitted from its use of the shortest channel without stagnation, its rapid packet processing, and its rapid convergence speed. In conclusion, the proposed hybrid whale dragonfly optimization approach (H-WDFOA-VANET) was compared with industry standard models, such as rider integrated cuckoo search (RI-CS) and enhanced hybrid ant colony optimization routing protocol (EHACORP). With the proposed method, throughput could be increased. The proposed system had energy consumption values of 2.00000 mJ, latency values of 15.61668 s, and a drop at node 60 of 0.15759. Additionally, a higher throughput was achieved with the new method. With the suggested method, it is possible to meet the energy consumption targets, delay value, and drop value at node 60. The proposed method reduces the drop value at node 80 to 0.15504, delay time to 15.64318 s, and energy consumption to 2.00000 mJ. These outcomes demonstrate the effectiveness of our proposed method. Thus, the proposed system is more efficient than existing systems.

Recurrent and Deep Learning Neural Network Models for DDoS Attack Detection

Distributed denial of service (DDoS) attack is a subclass of denial of service attack that performs severe attack in a cloud computing environment. It makes a malicious attempt to disturb the usual services of any network or server by using botnets. Hence, an efficient intrusion detection system (IDS) is essential to detect this attack. Some limitations in the existing IDS models for DDoS attack detection are delayed convergence, local stagnation issues, and local and global optimal trapping issues. These limitations are met by the recurrent neural network (RNN) and deep learning- (DL-) based proposed models that can utilize the previous states of the hidden neuron. The proposed research has used a long short-term memory (LSTM) recurrent neural network and autoencoder- and decoder-based deep learning strategy with gradient descent learning rule. The network parameters like weight vectors and bias coefficient are tuned optimally by employing the proposed a hybrid Harris Hawks optimization (HHO) and particle swarm optimization (PSO) algorithm. The proposed hybrid optimization algorithm selects the essential attributes, and the results obtained confirmed that the proposed LSTM and deep learning model outperformed all other models developed in the literature.

Sound classification using evolving ensemble models and Particle Swarm Optimization

Automatic sound classification attracts increasing research attention owing to its vast applications, such as robot navigation, environmental sensing, musical instrument classification, medical diagnosis, and surveillance. In this research, we propose an ensemble convolutional bidirectional Long Short-Term Memory (CBiLSTM) network with optimal hyper-parameter selection for undertaking sound classification. We first transform each audio signal into a spectrogram representation using the Short-time Fourier transform (STFT). A Particle Swarm Optimization (PSO) variant is subsequently proposed to optimize the learning rate, weight decay, numbers of filters and hidden units in the convolutional and BiLSTM layers, respectively, in order to extract effective spatial–temporal characteristics from the spectrogram inputs. To tackle the issue of stagnation in optimization, the proposed algorithm incorporates local exploitation using secant and Newton–Raphson methods, promising leader generation using regular and irregular super-ellipse formulae, and three-dimensional spherical search coefficients. Moreover, it takes into account multiple fused elite signals in conjunction with numerical analysis based exploitation to balance between diversification and intensification. A variety of CBiLSTM networks with distinctive optimized settings are devised. An ensemble model is then constructed by incorporating a set of three yielded networks based on a majority voting scheme. Evaluated using several audio data sets, our ensemble CBiLSTM networks outperform those with default and optimal settings identified by other search methods, existing deep architectures and state-of-the-art related studies. In addition to sound classification tasks, the proposed PSO algorithm also outperforms a number of classical and advanced search methods for solving diverse unimodal and multimodal benchmark functions with statistical significance.

A self-adaptive weighted differential evolution approach for large-scale feature selection

Recently, many evolutionary computation methods have been developed to solve the feature selection problem. However, the studies focused mainly on small-scale issues, resulting in stagnation issues in local optima and numerical instability when dealing with large-scale feature selection dilemmas. To address these challenges, this paper proposes a novel weighted differential evolution algorithm based on self-adaptive mechanism, named SaWDE, to solve large-scale feature selection. First, a multi-population mechanism is adopted to enhance the diversity of the population. Then, we propose a new self-adaptive mechanism that selects several strategies from a strategy pool to capture the diverse characteristics of the datasets from the historical information. Finally, a weighted model is designed to identify the important features, which enables our model to generate the most suitable feature-selection solution. We demonstrate the effectiveness of our algorithm on twelve large-scale datasets. The performance of SaWDE is superior compared to six non-EC algorithms and six other EC algorithms, on both training and test datasets and on subset size, indicating that our algorithm is a favorable tool to solve the large-scale feature selection problem. Moreover, we have experimented SaWDE with six EC algorithms on twelve higher-dimensional data, which demonstrates that SaWDE is more robust and efficient compared to those state-of-the-art methods. SaWDE source code is available on Github at https://github.com/wangxb96/SaWDE.

A Review on Policy Gaps of Agriculture Research and Development (R&D) in India and Their Effect on Agriculture Stakeholders

Purpose: An impressive stride in the agriculture sector in India after green revolution from a food deficient to food surplus country is attributed to the well-established infrastructure and contribution of various farm families in this sector. In the current scenario, various cons of food surplus and other stagnation issues of the policies need to be contemplated. The various policy reforms should inculcate well utilization of agriculture investment funds, methodologies to allow increments in farmer’s income to prevent their suicidal rates, incentives and requirements for private R&D investment in agriculture while maintaining the sustainable development goal of India. A complete transparent paradigm approach to be followed by the central, state governments and private sectors for fostering agriculture growth is analyzed in this review. The impetus behind the lack of agriculture growth in spite of tremendous productivity measures adopted by farmers, policy makers; public and private investment lies in lacking of an appropriate infrastructure as per current need and demand. It is imperative to foster paradigm protocol in agriculture with articulate government intermediaries to prevent monopoly of a particular authority in due course of time. It develops spurring behavior among stakeholders to form a consortium to revitalize the complete agriculture R&D to obviate the bottle necks hampering agriculture proliferation. Design: The present review highlights the mandatory need in the current scenario to review the agriculture research and development policies and desired amendments as per the needs of the associated stake holders. Paper Type: Review article

Feature Selection for Alzheimer’s Gene Expression Data Using Modified Binary Particle Swarm Optimization

Alzheimer’s Disease (AD) is a neurological disorder that destroys memory and other significant mental functions. One of the most accurate methods to identify the disease-causing genes is to monitor gene expression values in various samples. Selecting significant genes for classification is important in gene expression studies. In this study, the experimental data are taken from the gene expression data of human brain in persons with AD and older control subjects GEO GSE5281 data set. In this work, a new two-step gene selection is applied to filter the noisy and redundant genes, based on the statistical method and heuristic optimization approach. T-statistic (T-test), Signal to Noise Ratio (SNR) and F-test, are used in the first step of the gene selection process. The top ten significant genes selected from the statistical methods are applied to Particle Swarm Optimization (PSO) to obtain the optimal number of features of Alzheimer’s disease. To avoid the stagnation issue in PSO, a modified PSO approach is proposed which finds a new particle position by utilizing the Genetic Algorithm (GA) crossover and mutation operators. The classifiers, Decision tree, Support Vector Machine (SVM), Linear Model, Random Forest and Neural network, are employed in training and testing data to analyse the performance of GA & PSOs. Modified PSO with t-Test in Random forest and Linear model provides 100% accuracy for the test dataset of GSE5281 with optimum number of genes. The significant genes identified through this research are EGR1, CKMT1B, RPL15, PSMB3, GRK4, COX6A1 and PHIP from the GSE5281 dataset.

The Description of “Temperature Self-Limiting” in Jordanian Solar Generators with Natural Cooling

This study helps to make the sustainability of the experiential system reg of solar generator device better, particularly the unexpected inactiveness of drainage liquid throughout the solar system which is generating power. Stagnation situations can be disastrous for solar system units. Various methods to mitigating the consequences of the stagnation state have been established and tested. Some suggested approaches are not appropriate for all device designs and implementations. The tested reg generator systems in experiments can continue to work although the collector of the piping system is cut off. furthermore, the absorber layer is a challenge because it absorbs general solar incandesce regardless of cell temperature, causing the piping system to become inactive. This research depicts the experiential data that was tracked and mentioned in dealing with stagnation. The hydrodynamic flowing in the experiential solar generator was simulated using rigid flow. The measuring and processing of the data allowed the identification of excessive heat and stagnation issues in real-world operating environments. Daily, the test logging data of the prototype reg device was monitored to guide the incipience of inactivity and excessive heat. Most items have been utilized in the study; solenoid check valve and the Reflux Pipe in the Check Valves have been utilized as primary control items within the experimental reg unit, while normal cooling was utilized as the subaltern control element. Under stagnation phases, an air path is installed at the rear of the absorber to cool it normally. In general, there are agreement between the experimental and simulation results.

Productivity and Preferences of New Potato Varieties and their Relationships in Five Districts of Bhutan

Potato (Solanum tuberosum L.) is one of the widely cultivated horticultural commodities in Bhutan. Over the years yields of potato at national level have stagnated due to degeneration of seeds and lack of appropriate varieties in the country. To address the yield stagnation issue, two potato varieties (Yusi Maap and Nasphel Kewa Kaap (NKK)) were introduced in recent years. To understand crop productivity and farmer’s preferences of the new varieties this study was conducted in Bumthang, Chukha, Gasa, Haa, and Wangduephodrang districts from 2017 to 2018 using the field demonstration trials and participatory varietal selection method. Desiree, the popular variety was used as a control. On an average across years and dzongkhags, mean yields of the NKK and Yusi Maap were 10.4 and 10.5 tons/acre, respectively. Hence, yields of Yusi Maap and NKK were approximately 30% higher in comparison to Desire, which was 7.30 tons/acre on an average across the years and dzongkhags. Being one of the oldest varieties, Desiree consistently showed lower productivity compared to other two varieties due to seed degeneration and quality deterioration. Although the productivity difference between NKK and Yusi Maap is minimal (0.10 tons/acre) the preference votes of Yusi Maap was 1.3 times that of NKK indicating the higher level of preferences for Yusi Maap. There was positive significant relationship (P=0.02) between the potato productivity and the farmer’s preference suggesting that higher productivity attracted more votes and lower productivity attracted lesser votes. Further higher productivity is also seen as a means to earn cash income for the household to achieve food security in terms of household’s consumptions through the sale of potatoes. The findings provide a sound scientific basis to guide program implementers and policy-makers in terms of potato research and development at the national level.

Российская индустрия гостеприимства в пандемию – кризис или прорыв?

This article examines the most pressing problematic issues of stagnation in the hospitality industry in Russia, associated with the new challenges of the coronavirus pandemic, both at the international and national levels. The author analyzes new trends and structural changes in the industry and assesses the prospects for overcoming the difficult crisis. The article presents specific proposals aimed at the restoration and constructive development of the tourism industry both for the authorities and administration to amend the adopted Strategy for the development of tourism in the Russian Federation for the period up to 2035, and for business structures, as well as travel companies. According to the author, the hospitality industry can still face serious shocks in the near future and the industry will not come out of the crisis without experiencing repeated shocks and border closings. Nevertheless, tourism is known for its ability to withstand economic downturns and can play a key role in reviving both the global and Russian economies as they emerge from the crisis. To consolidate the trend and to strengthen the development of domestic tourism, the industry needs a coordinated position and strategies for the behavior of the main market players, such as business companies operating in the hospitality industry, as well as authorities and authorities at the federal and regional levels. This requires the development and implementation of a modern strategy, taking into account the need to overcome the crisis caused by the coronavirus pandemic

PROBLEMATIC ASPECTS OF ACCOUNTING FOR NON-CURRENT AND CURRENT TANGIBLE ASSETS IN BUDGETARY INSTITUTIONS

This article examines the current state of accounting support for current and non-current tangible assets in budgetary institutions. The analysis of the receipt of non-current tangible assets allowed us to focus on the issue of stagnation of the value criterion when assigning the asset to fixed assets or other non-current tangible assets. The application of the value criterion is currently an imperfect method of grouping non-current tangible assets by classification groups, because the definition of an asset in some cases depends on the supplier's taxation system. The definition of this problem allowed to suggest ways to solve it, where the first proposal is to refuse to separate value added tax from the price of the asset, and the second is to determine the asset by groups based on its properties without reference to its value. Each of the proposed proposals has its advantages, but given the inflationary factors and the instability of economic processes, the grouping of non-current tangible assets should be done by analyzing the characteristics, properties of the asset without reference to its value. The second part of this article is devoted to the study of the method of disposal of stocks received as a result of intra-departmental movement within the authorized body. It is established that this type of stocks has its own specifics associated with its receipt in the field of management and use in the activities of budgetary institutions. Taking into account these factors, the article proposes to legislate the disposal of this type of stock only by using the method of identified cost of the relevant unit of stock. This will ensure the reliability of the financial and budgetary reporting not only in a particular budgetary institution, but also in general by the authorized body. The proposed improvements in the accounting of non-current and current tangible assets will improve the financial and budgetary reporting of budgetary institutions, bring budgetary accounting to international and European standards, as well as improve the quality of planning in budgetary institutions.

Multi-robot path planning using an improved self-adaptive particle swarm optimization

Path planning is of great significance in motion planning and cooperative navigation of multiple robots. Nevertheless, because of its high complexity and nondeterministic polynomial time hard nature, efficiently tackling with the issue of multi-robot path planning remains greatly challenging. To this end, enhancing a coevolution mechanism and an improved particle swarm optimization (PSO) algorithm, this article presents a coevolution-based particle swarm optimization method to cope with the multi-robot path planning issue. Attempting to well adjust the global and local search abilities and address the stagnation issue of particle swarm optimization, the proposed particle swarm optimization enhances a widely used standard particle swarm optimization algorithm with the evolutionary game theory, in which a novel self-adaptive strategy is proposed to update the three main control parameters of particles. Since the convergence of particle swarm optimization significantly influences its optimization efficiency, the convergence of the proposed particle swarm optimization is analytically investigated and a parameter selection rule, sufficiently guaranteeing the convergence of this particle swarm optimization, is provided in this article. The performance of the proposed planning method is verified through different scenarios both in single-robot and in multi-robot path planning problems. The numerical simulation results reveal that, compared to its contenders, the proposed method is highly promising with respect to the path optimality. Also, the computation time of the proposed method is comparable with those of its peers.

Opposition-based learning Harris hawks optimization with advanced transition rules: principles and analysis

Harris hawks optimizer (HHO) is a recently developed, efficient meta-heuristic optimization approach, which is inspired by the chasing style and collaborative behavior of Harris hawks in nature. However, for some optimization cases, the algorithm suffers from an immature balance between exploitation and exploration. Therefore, in the present study, four effective strategies are introduced into conventional HHO, such as proposing a non-linear energy parameter for the nergy of prey, differor rapid dives, a greedy selection mechanism, and opposition-based learning. These strategies enhance the search-efficiency of HHO and help to alleviate the issues of stagnation at the sub-optimal solution and premature convergence. A well-known collection of 33 benchmark problems is taken to examine the effectiveness of the proposed m-HHO, and the comparison is performed with conventional HHO and other state-of-the-art algorithms. Accordingly, the proposed m-HHO can serve as an effective and efficient optimization tool for global optimization problems.