ant colony algorithm

The most important goal of every investor in the stock market is to increase returns and reduce investment risk. Therefore, the purpose of this research is to analyze the effectiveness of meta-heuristic algorithms in stock portfolio optimization. Considering that in this research, the past performance of Tehran Stock Exchange companies is examined in past studies from 1390-1399, therefore, in terms of the research design, this research was post-event using Delphi and meta-analysis techniques. The statistical community of this research Academic researchers in the field of finance and active in the Tehran Stock Exchange, and the sampling method in this research was targeted with a volume of 30 people. The data collection tool was a researcher-made questionnaire. The method of collecting information was structured interview of researchers and review of the results of various studies in the field of determining the optimal stock portfolio in Tehran Stock Exchange. In order to analyze the data, Spss software version 23 and Laserl version 5.7 were used. The results showed that among meta-heuristic algorithms of genetic algorithm, ant colony and bee colony are the most suitable tools with the aim of not stopping at local optimal points and not premature convergence. Finally, after evaluating the appropriate algorithms, a comparison of the average risk and returns of the stock portfolio in genetic algorithms, ant colony and bee colony was done in the study unit, they showed that in terms of the criteria of reducing the risk of genetic and bee algorithms and in terms of increasing the return of the optimal portfolio Stock bee algorithm has worked more efficiently.

The purpose of this research is to present the evolutionary model of short selling using collective intelligence and an ant colony algorithm. In terms of method, this research is in the category of quantitative research, and the purpose of the study is practical. The statistical population includes all active companies admitted to the Tehran Stock Exchange. This research has been investigated between 2011 and 2019 for active companies admitted to the Tehran Stock Exchange. The method of data collection is library-type and uses foreign and domestic articles and financial data of companies admitted to the stock exchange, which has been compiled by referring to financial statements and explanatory notes using the new Rahvard Novin software. In the following, we have presented the short-selling model by using EViews 9 software and MATLAB, and then using MATLAB software and the ant colony algorithm, we have presented the evolutionary model of short selling. In the end, by comparing the step-by-step regression model and the borrowed sales model (ant colony algorithm), he presented a model that is more efficient than other models. The result of the research indicates that the short-selling model with the help of the ant colony algorithm has a higher efficiency.

This study aims to use two ant colony algorithm and particle swarm algorithm to predict earning management and determine which algorithm has more explanatory power.To achieve the research goal, 163 companies have been selected by systematic elimination method in the period 2013-2019. The data are panel and thirteen variables have been considered to examine the models. Finally, eight variables have been identified as effective and tests have been performed using Python software. The results show that earnings management can be predicted with more than 97% accuracy by both algorithms, but the ability to predict the particle swarm model in accrual earnings management is higher, however ant colony algorithm has more power in predicting real earnings management.

Due to the importance of manufacturing and production systems issues in modern businesses, in recent years, many magazines and researchers have focused their research on this field. Manufacturing and production is an emerging pattern in which production resources are hardware (handling materials, equipment, tools and machines, computers), software (computer-aided design, computer-aided production) and production capabilities (ability design, production, maintenance, repair, management, simulation, optimization) is virtualized and available to users in the entire manufacturing and production cycle and allows the joint use of production systems and resources produced at the global level. Manufacturing and production based on new technologies is a solution that enables users to receive their requests in different layers with optimal timing. Therefore, the main goal of this research is to provide a mathematical model to optimize the production output function in dynamic manufacturing and production systems. For this purpose, the researcher first conducted a comprehensive and complete study of the research literature, and after collecting the information, he selected and developed the initial model, and in the second phase of the research, he started by obtaining statistical information and data from the relevant statistical community, to build the model. Its original and initial test was done. Since the problem of optimizing the production output function in dynamic manufacturing and production systems is included in the NP-hard category, it means that for such problems, a quick and feasible solution has not been found in a reasonable time from the genetic algorithm and ant colony in MATLAB software was used and through the tools and methods of solving and analyzing them, the questions of the current research were given a suitable and optimal answer.

Organizations are working to reduce warehousing and transportation costs, expanding supply chain management. Transportation and inventory are the two main elements in the supply chain. The issue of routing-inventory is the consistent distribution of the product from one or more distribution points among the consumer group, which minimizes the average cost of distribution along the planning horizon without creating a shortage for the organization is the primary goal of this study. This study addresses this issue in perishable products with a fixed lifespan. A very critical area of vehicle routing is the inventory routing issue. The most important objectives of this research were to review the studies conducted in the field of routing-inventory, inventory management, routing of perishable goods, implementing the cost minimization goal in the proposed mathematical model, and developing appropriate approaches to solve the proposed model under the assumptions of this research.

Given the nature of the problem that comes from NP-hard types, It is impossible to arrive at a definitive answer for its large dimensions, so this study proposes a method based on the ant colony algorithm algorithm, which solves the inventory routing problem under new assumptions. In this study, this issue was addressed in perishable products with a fixed lifespan.

It was shown that the proposed algorithm is capable of solving problems in different dimensions, especially where it seems impossible to achieve global optimality. Experiments on different benchmarks all confirmed the optimality of the results over competing algorithms and guided us to achieve the research objective of minimizing costs.

Supply chain is a complex network that includes products, services, and information flows between suppliers and customers, all of which create added value in the economy. The lesson of not operating this chain will be an obstacle to creating added value in the macroeconomic perspective. In addition to the above, the marketing, engineering and design units of a manufacturer are also part of the supply chain. In general, the supply chain is a chain that includes all activities related to the flow of goods and the conversion of materials, from the stage of preparation of the raw material to the stage of delivery of the final product to the consumer. The sole purpose of the supply chain is to minimize the total cost of the given network, To achieve this goal there is a need for a suitable business model in the industry. In this research, we propose the method of setting the optimal order quantity by optimizing particle swarm for supply chain management. In studies and after the implementation of the algorithm, we conclude that this algorithm has a high execution time in long distances and in high repetition cannot reduce the total cost, but in short distances is very efficient, the execution time is low. The total cost is low.

In today's highly competitive world, closed-loop supply chains (CLSC) have become a major challenge for product recycling. The recycling system has a special place in the supply chain due to the coverage of laws and the reduction of environmental pollution, increasing economic power by creating new jobs and the ability to recover the value of returned products. The purpose of this study is to design fuzzy- goal model for optimizing the cost and distance of vehicles in the four echelon closed-loop supply chain. The closed-loop logistics model could not be easily solved with the base gradient methods due to its NP-Hard problem group, so the ant algorithm was used for optimization. Finally, after implementing the fuzzy goal model of the research, it was found that the goal of minimizing the cost in the model has a higher membership amount than the distance goal, and managers should pay special attention to this goal.

Drought is one of the most significant natural hazards which affects various aspects of human life with its gradual, slow and creeping occurrence. As a disastrous climate phenomenon, it directly impacts on human communities through making changes in access to water resources, and imposes enormous economic, social and environmental costs on various communities. One of the strategies governments always use to compensate the damages that droughts impose on various economic, social and environmental sectors is to include special financial loans in the form of grants, special development credits, special structural projects, and so on. Therefore, in this regard, a lot of financial aids have been allocated by the government for it. But, sometimes, it is observed that the allocation and distribution of drought credits among different cities of the province have been more influenced by political, ethnic, and religious interests rather than by the severity of the drought and its detrimental effects on various economic, agricultural, and social sectors. Therefore, in this study, it has been tried to present a method based on an optimization model in order to take a step in optimizing the management of drought budget based on realities in Sistan and Baluchestan province in south-east of Iran. In this study, the indicators proposed by the Cabinet of Ministers of the Islamic Republic of Iran for reducing the effects of drought were applied to manage the optimal distribution of drought budgets among the cities of Sistan and Baluchestan province in southeast of  Iran. These indicators were population, having urban and rural drinking water, vulnerability of water resources, surface area under cultivation, number of users of agricultural sector, annual consumption of agricultural water, and rainfall. Using the figures given in the 2011 statistical yearbook, numerical values ​​for each of these indices were obtained. But given that these indices are not of equal importance in each city and some may be more important in one city and less important in another, the Analytical Hierarchy Process (AHP) technique using paired comparison method was applied to determine the relative importance of these indices in Sistan and Baluchestan province. Using this technique, a relative weight was determined for each of these indicators and for each city. In addition to determining the weight of these indices, it was needed to convert droughts in the province to numerical values. In this study, to analyze droughts in Sistan and Baluchestan province, Standardized Precipitation Index (SPI), which is one of the proposed indicators of World Meteorological Organization (WMO) has been used for a period of 30 years (1984-2014) and for 6 synoptic stations. The results of drought analysis in Sistan and Baluchestan province in an annual scale showed that the frequency distribution of different drought levels in Sistan and Baluchestan province has not been uniform, with the most droughts occurring in this 30 year period in northern part of the province (Zabol and Zahedan) including 14 and 13 repeated cases; respectively, with the lowest in central part of Baluchistan (Khash and Saravan) including 11 repeated cases. But, what should be noted after the frequency of droughts is the persistence and durability of droughts. As a result, weak but long-lasting droughts can be much more damaging than severe but short-lived droughts. Sistan and Baluchestan province has been under drought with varying degrees of severity from the water year of 1998-1999 to the water year of 2005-2006. But in the meantime, the north of Sistan and Baluchestan province (Zabol station) has a condition which is very different from other parts of the province. The north of Sistan and Baluchistan province has been experiencing severe droughts for 14 years from the year 1999-2000 to 2012-2013 with the exception of the year 2004-2005. It has been the long-term drought persistence in the province that has caused many environmental, economic and social problems, such as the drying up of Lake Hamoun and the water reduction in the Hirmand River in the north of the province, and severe reduction of groundwater resources in central Baluchistan. Therefore, given that Sistan and Baluchestan province has experienced 7 years of continuous drought between the two water years of 1998-1999 to 2009-2010, these seven years were chosen as examples, and the ant colony algorithm was implemented on them. Based on the selected criteria and the proposed method, it seems that the combination of Analytic hierarchy process (AHP) technique and Ant colony algorithm can formulate an intelligent system to determine the priority of the cities in Sistan and Baluchestan province during droughts for allocating the optimal budget and reducing the harmful effects of droughts. Based on the prioritization maps of optimal drought budget allocation among the cities in Sistan and Baluchestan province, an almost good agreement can be observed between the results and the realities existing in the province.

Investment decision making is one of the key issues in financial management. Investor might know about different asset types when facing with various options and the ways in which investors can incorporate them in devising a strategy is significant. Selecting the appropriate tools and techniques that can make optimum portfolio is one of the main objectives of the investment world. In this study it is tried to optimize the decision making in stock selection or optimization of portfolio by means of artificial colony of honeybee algorithm. And to determine the effectiveness of the algorithm, Sharp criteria algorithm, the trainer criteria and its downside risk were calculated and compared with the portfolio made up of genetic and ant colony algorithms .The sample consisted of active firms listed in the Tehran Stock Exchange from 2005 to 2015. The sample was selected by the systematic removal method. The findings show that Sharp criteria algorithm formed by the artificial bee colony algorithm functions better than the genetic and ant colony algorithms in terms of portfolio formation .However, the trainer's criteria and downside risk of the stock portfolio formed through the artificial bee colony algorithm shows the optimum function, this difference is not statistically significant.

Applying of two systems Light Rail Transit and Bus Rapid Transit as skeleton of public transport has been defined in Mashhad city. There are problems in the line routes of the system including about 50 percent coverage with each other along their routes that this problem reduces the system's coverage. This research is done with the aim of re-designing of the mentioned routes with a view to maximizing system access. Research method is both qualitative and quantitative and it is applied research. To reform the public transport skeleton routes a mathematical modeling approach is applied using ant colony algorithm. Then combined access index using Hansen method has been calculated divided to 253 traffic zones and in two different cases, system status quo and the system proposed by research. The population access, the places of employment and attractive land uses were have noticed on calculating the mentioned index. The research data were obtained from database of comprehensive transportation study of Mashhad which was update at 2013 using documentary study. Improved routes of public transit skeleton have been defined as the results of this research that they have no coverage with each other so the system coverage is increased while their overall length is less than the approved routes. Also the access index significantly increased in case of modified system compare with the primary one. Given that the existing public transit skeleton system has been designed with methods based on movement, the results of this research reveals differences between the methods based on movement and access in case of designing of public transport.

The purpose of this study is to investigate the application of Meta Heuristic Algorithms in Financial field. The studied algorithms in this study include ant colony algorithm, firefly algorithm, bee algorithm, harmony search, biogeography algorithm. while examining each of these algorithms in brief, internal and external studies are performed and their applications in the financial field are discussed. The results show that further research and ultimately the use of these algorithms in predicting financial and capital market issues can help to increase the performance of accounting and auditing operations to a researchable extent. Increasing research and providing operational backgrounds in recent years also reflect the interest of researchers and research organizations in developing these new approaches, especially in the field of software development.

One of the main missions of financial reporting is a proper disclosure of information in order to meet the needs of financial statement users. This study is aimed to investigate whether the company's disclosure quality can be discovered by Machine learning-based models. In this study, disclosure level rating of listed firms is predicted by Securities and Exchange Organization using the ant colony algorithm and differential evolution model. The sample consists of 171 firms listed in Tehran Stock Exchange during the period from 2010 to 2014. This study uses MATLAB software to predict a firm's disclosure quality. The algorithms fitting results show that the two algorithms have ability to predict earnings management with high accuracy of 95%. In fact, the results indicate that the ant colony algorithm has more ability (error 3.316 percent) of predicting earnings management than the differential evolution algorithm (4. 139 percent error).

Promptness of relief groups and especially, of inter- cities ambulances has a vital role in their performance during unpredicted disasters. In this regard, optimal routing of these groups seems necessary in order to cover maximum population centers. For this purpose, the use of artificial intelligence and the so-called “new routing algorithms,” and its localization among inter/ intra- cities sections, based on their extent and spread, can be an efficient way for efficient urban management and relief organization. Therefore, the aim of this study was to show the practical application of ant colony algorithm for optimizing routing and minimizing the travelled distance .In this research, the case study was performed on over 29 urban and rural points, originated in Parsabad city, in MATLAB and shown in the GIS environment. The proposed model in this paper can not only be used to analyze the issue, but it also can be used to optimize the routing of distribution of basic goods in cases of natural and human disasters, traffic problem, and so on. Need to note that in the proposed method, the Rolette wheel Selection method is used for random selection of the neighborhoods.The results showed that due to the limited area of the case study, time and quality of achieving to optimal route in ant colony algorithm were calculated 0.19 ms faster than the Experimental Method, whereas, given the movement of 30 ants, the time required to arrive to the scene by the ambulances for ant colony algorithm and the Experimental Method was calculated 19' 45'' and 27' 15'', respectively.

Financial distress prediction of companies is one of the important issues that can contribute to the success and survival of companies; because providing warning and timely signals can make companies aware of financial distress and bankruptcy and, therefore, by a correct management, they can prevent waste of resources and the damage caused by bankruptcy.
Ant Colony Algorithm (ACA) is an intelligent method that was recently used to solve problems including classifications and predictions which had desired results. This study aims to investigate the financial distress prediction of companies using ant colony algorithm. The statistical population includes companies listed in Tehran Stock Exchange and the sample consists of 174 healthy and distressed companies. Predictor variables were selected from previous studies according to the ratios that were proposed as key variables in prediction model.
The results of the study indicate that the ACA approach in predicting financial distress of companies had significantly better performance than multiple discriminant analysis (MDA).

In this paper, Steel-making Continuous Casting (SCC) scheduling problem is investigated. The scheduling of SCC processes is of major importance in steel industries. Since it is often a bottleneck in steel production, optimal scheduling of it can minimize production cost and increase profit notably. This problem is a specific case of hybrid flow shop scheduling problem accompanied by technological constraints of steel-making and hence classic optimization methods fail to obtain an optimal solution for a real life problem over a suitable time. In this paper, a meta-heuristic algorithm based on an Ant colony algorithm (ACO) which is accompanied with a heuristic timing algorithm is developed. In the proposed algorithm the resource assignment and sequencing is determined with ACO and the timing of jobs is determined using a heuristic forward-backward algorithm. Considering the real constraints in the problem definition, designing a heuristic timing algorithm, embedding the proposed timing algorithm in ACO framework and a novel design of ACO algorithm in pheromone matrix and heuristic information are the main features of this paper. The proposed algorithm is implemented in Khuzestan steel complex and the efficiency of it is compared with a heuristic algorithm of commercial scheduling software which is used in Mobarakeh Steel Company. Numerical results show that the average percent of improvement is about 65% in a limited scheduling time.

All of the financial institutions for gaining the best profit of their investment are always looking for the best investors, consulters, and borrowers. Besides, different sciences attempt to represent accurate methods for the separation of the customers. For that reason, sciences such as psychology, management sciences, mathematics, financial and etc…seek to achieve this aim. The subject that comes into consideration in this paper is the necessity of using the new methods in data mining in mixture with artificial intelligence techniques in order to deal with the sophisticated issue and answer to this question that do the usage of combined approach predict the customer rating well? If we want this process occurs, another dimension must not be forgotten that is the select measurement criteria and in this regard, the researcher has used judging journalist and non-parametric analysis in order to rank criteria thatfinally, select the number of indicatorsin order to implement the hybrid model will lead the researcher to answer this question: do the journalist’s ideas selection criteria result in a good prediction of the credit status of customers? The three indicators “age”, “previous relationship with the bank”, and “credit”to implement a fuzzy neural hybrid model are chosen. The model has been implemented in three layers and results suggest that 89.67% times the system can accurately estimate the proportion of customers provide ratings.To optimize the fuzzy neural network, the ant colony algorithm was used which results in improved performance of the model was 90.5%.

The location of Fire stations plays an important role in the efficiency of these facilities during fire accidents. Therefore, in order to maximize the coverage of these stations to population centers, proper locating of these fire stations seems necessary. On the other hand, locating of fire stations and allocating population to them is a combinatorial optimization problem. Therefore, the purpose of this paper is to integrate geographic information system and a meta-heuristic algorithm based on ant colony algorithm for optimal site selection of fire stations. For this purpose, a case study was carried out in five regions of Tehran. In this study, suitable areas were firstly identified by analytical hierarchical process method which is a common multi-criteria decision making method. For this purpose, access to the main transportation routes, proximity to compatible land uses, and staying away from incompatible land uses were considered as criteria. Furthermore, the coverage of the existing stations was also considered in the final map. Then the suitable stations were selected from the prone options by the proposed ant colony algorithm. In order to select these stations, parameters include incorporating proper distance among fire stations and maximizing the population that can be served by these fire stations in the standard time, were considered. The computational results reveal that the proposed algorithm can obtain high quality solutions to such problems in a short time. The results of this paper also reveal the efficiency of this method in the optimal locating of fire stations.