meta heuristic algorithms

Refurbishing an oil rig is one of the best alternatives to renting this capital-intensive equipment since its rental cost amounts to tens of thousands of dollars per day. This research focuses on the scheduling of activities and inventory planning in the supply chain of the Sina 1 drilling rig reconstruction project. Since no prior record of such an endeavor exists in the country, project scheduling is conducted under conditions of activity uncertainty. In addition, uncertainty in the timing of activities improves project owners' understanding of their schedule and creates a broader view of the project and future activities. In this model, contractor and supplier costs are considered simultaneously, and uncertain activity scheduling and order planning are carried out in a way that minimizes the overall cost of the chain.

Given the large number of variables and constraints in the mathematical model of supply chain activity scheduling, the problem under study is classified as NP-hard. For this reason, meta-heuristic methods are used to solve such problems, which provide near-optimal answers in less time compared to exact methods. In this study, the electromagnetic algorithm has been used to solve this problem. This algorithm has been applied to a real project (the foundation section of the Sina 1 drilling rig).

The mathematical model proposed in this research has been coded using the electromagnetic algorithm within the MATLAB software environment. The input parameters include general parameters and control parameters of the electromagnetic meta-heuristic algorithm. General parameters pertain to the specifications of the contractor, suppliers, activities, resources, and consumables. To validate the efficiency and effectiveness of the designed electromagnetic algorithm, three case problems were selected. The first problem consists of five activities; the second problem involves the same five activities but with two suppliers, while the third problem features a larger-scale scenario. First, the exact solution for each problem was obtained using AIMMS software, and then the solutions and computational times of the electromagnetic algorithm were compared with those of AIMMS. Notably, as problem size increases, the solution time in AIMMS grows significantly (exponentially). The proposed electromagnetic algorithm demonstrates acceptable performance in terms of computational time. The meta-heuristic electromagnetic algorithm solves the sample problem in 115 seconds, whereas AIMMS requires approximately 747 seconds to find the exact solution. Additionally, the relative deviation of the electromagnetic meta-heuristic algorithm from AIMMS is approximately one percent. Finally, a sensitivity analysis was conducted to examine the impact of different payment methods on the project's supply chain costs.

This research presents the modeling and solution approach for a two-level project supply chain, encompassing both the contractor and suppliers, with the objective of minimizing project and inventory costs for both entities.By comparing the results obtained from the exact method and the electromagnetic algorithm, it was found that the solution time using the electromagnetic algorithm is significantly less than the solution time in the exact method. In addition, the relative difference in the quality of the results is limited to approximately one percent. These cases clearly indicate the effectiveness and efficiency of the proposed algorithm.The results indicate that the proposed electromagnetic algorithm is a highly effective approach for this problem and converges toward an optimal solution.

High-quality accounting information has always been one of the main columns of financial markets because resources are allocated efficiently through the correct dissemination of information. So is a pre-requisite for well functioning capital markets and economy as a whole and as such should be of importance to investors, companies and accounting standard setters. The importance of accounting information stems from reducing the state of uncertainty and increasing the level of confidence of the decision-maker, especially during critical times such as financial crises. Thus, the purpose of this research is to test the level of accounting information quality during the financial crisis by the method of meta-heuristic algorithms. For this purpose, the data of 86 companies admitted to the Tehran Stock Exchange were analyzed using the particle aggregation optimization algorithm. The findings of the research show that the optimal level of accounting information quality of companies Decrease during the financial crisis.

The aim of the present study is to optimize the behavioral indicators of entrepreneurial performance in technology-based startups. The research method of this study is a mixture (qualitative-quantitative) of the meta-synthesis method in the qualitative part and the decision tree and genetic algorithm in the quantitative part. For this purpose, the meta-synthesis method has been used to examine and identify the dimensions and influential factors, in which regard the research population is 164 valid scientific articles published between 2013 and 2023, after specialized filtering and quality control of the texts, 38 articles were selected for analysis and coding. Next, the meta-heuristic algorithm was used to optimize the identified indicators. For this purpose, in the meta-heuristic algorithm section, the genetic algorithm was used for its optimal modeling because it was the best method for optimizing this research according to experts. The findings showed that in the qualitative section, we obtained 97 open codes and 18 closed codes, which were categorized into four categories: entrepreneurial structural factors, entrepreneurial behavioral factors, entrepreneurial capital factors, and entrepreneurial external factors. In the quantitative section, for the indicators, it shows that the indicators of government programs, entrepreneurship education in elementary schools, entrepreneurship education after school, and finally business and professional infrastructure have the greatest impact on the classification of countries, respectively, and to improve the status of entrepreneurs and the ranking of countries, special focus should be placed on these four areas

Considering the importance of stock price synchronicity as an indicator of the information content of the accounting system and a measure for efficiency of the market, This research aims to provide a price synchronicity model for stocks using financial and non-financial criteria by Meta-heuristic algorithm.
The data collection method employed was survey-based and library studies. The study population of this research consists of 167 companies listed on the Tehran Stock Exchange from the 2011 to the end of 2021, which were selected randomly and data were analyzed by meta-heuristic algorithms of Invasive Weed Optimization and Particle Swarm Optimization using MATLAB 2016 and Mini Tab software.
Findings indicate that the IWO algorithm is more accurate than PSO algorithm in estimating the coefficients of financial and non-financial factors affecting price synchronicity. Some financial and non-financial factors have a positive impact on price synchronicity while others have a negative impact. The ratio of non-executive managers and the size of the board of directors have a positive effect on price synchronicity. Information asymmetry has a negative impact on price synchronicity. The liquidity rank of stocks has a negative impact on price synchronicity. Operating profit margin has a positive effect on price synchronicity. The fixed asset turnover ratio has a positive effect on price synchronicity. Political communications have a negative impact on price synchronicity. Environmental factors have a negative impact on price synchronicity. Oil prices have a negative impact on price synchronicity, while being associated with commercial groups has a positive effect on price synchronicity.

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.

The purpose of this research was to design a distribution channel selection system in the supply chain of the oil industry using the combination of adaptive neural-fuzzy network and meta-heuristic algorithms (Case study: National Oil Products Distribution Company of the dual regions of West Azerbaijan). This research was based on the purpose of the research, developmental-applicative and based on the descriptive-modeling research method. The statistical population of this research was 185 axis sales, Sekodar and fuel supply stations in the two regions of North and South of West Azarbaijan province. In this research, questionnaires and documents were used to collect data. In order to analyze the data, confirmatory factor analysis, adaptive neural-fuzzy network in basic mode and adaptive neural-fuzzy network combined with genetic meta-heuristic algorithms and particle swarm have been used. In this research, first, the combined distribution channel selection system was designed, and then using the system designed based on the least error, the traditional distribution channel and the branding plan of the fuel stations were evaluated based on the input scores. The results show that the best system for selecting the distribution channel was the adaptive neural-fuzzy network combined with the particle swarm algorithm. By comparing the performance of the branding plan and the traditional method, it was determined that the branding plan has a better performance and is a suitable distribution channel for the National Oil Products Distribution Company of the dual regions of West Azerbaijan.

The main goal of investors in the stock market is to get the highest return at the desired time, therefore introducing the most suitable method for conducting transactions is of special importance for investors. Successful trading in financial markets should be done close to key reversal points. In recent years, various systems have been developed to identify these return points. Technical analysis tries to identify the time to enter and exit trades.In this article, we are trying to select the one with a higher success rate by using the technical rules according to the previous researches, and by using soft calculations, the decision parameters in the technical rules are improved using the firefly algorithm.The results of this model are compared with the results of using the standard parameters of the indicators and the results of the purchase and maintenance strategy. In order to validate the introduced trading system, we compared it with the results of the optimized intelligent system based on optics and genetic algorithm. The results of the research show that by optimizing the parameters of technical analysis indicators, the investment efficiency can be increased compared to the usual methods in the stock market and previous researches.

Time and cost are significant factors in every project. By reducing the resources allocated to the project, project costs are reduced, while the reduction of available resources means the inability to simultaneously implement activities or activities in the shortest possible time, which in turn increases the duration of the project. This is although, in all projects, the completion of the projects in the earliest time is considered one of the important parameters of the project. Considering the highly practical application of the examined problem, project scheduling by investing multi-skill resources with the possibility of changing the skill level in fuzzy conditions can be considered a positive step towards creating project scheduling problems.                         
Design/methodology/approach: In this paper, the proposed mathematical model of meta-heuristic genetic algorithms to solve the proposed model is discussed and explained in detail. Several skills are needed to perform each activity. The goal is to optimally determine resource availability and find the best schedule by minimizing investment in resources.              
Considering the activities' need for different skills as well as the expertise of the project members in different skills, it seems obvious that each activity can be done with several different situations in terms of human resources allocation, which might be only for one activity, reaching more than 10 modes. As a result, compared to MRCPSP, this issue has a much higher complexity. The Resource Investment Problem (RIP) is a variant of RCPSP where renewable resource constraints are considered decision variables. In many projects, managers, in addition to making decisions about the time of implementation of activities, should determine the number of resources allocated to activities in each period of the implementation of activities according to the status of the project, which means ignoring the constant pattern of resource consumption for activities during their implementation.                          
Practical implications: By comparing the algorithms with the indicators of maximum extension, distance from the ideal solution, distance, and several Pareto solutions, it was found that the multi-objective genetic algorithm performs far better than the multi-objective Cuckoo algorithm regarding the criteria, distance from the ideal solution, and the largest expansion. However, in terms of the number of Pareto solutions, the algorithm is not superior to the other algorithms. Therefore, it can be concluded that the multi-objective genetic algorithm has relatively a better performance than the multi-objective Cuckoo algorithm.           
Social implications: In this research, each activity can be performed with several different situations in terms of human resource allocation, which may reach more than 10 situations just for one activity. As a result, compared to MRCPSP, this issue has a higher level of complexity. Literature review indicates that being multi-skilled increases the productivity, quality, and consistency of work and gives managers more flexibility in work allocation.              
Originality/value: One of the most important branches of project scheduling knowledge is the problem of project scheduling with limited resources. This new concept has led to the development of one of the most general modes of scheduling problems under the title of multi-mode project scheduling with limited resources, which solves many real problems and can be modeled for application.

Supply chain network design is one of the most important issues in supply chain management. Therefore, the purpose of this study is to design a closed loop supply chain network in conditions of uncertainty and solve it using meta-heuristic algorithms.

This research is of applied kind. In this research, field and library study methods were used to collect information. Considering the selection of the tire industry as the under study industry for designing the proposed supply chain network, in this study, using field research, the required data from Kian Tire, Iran Tire, Dena Tire, Barez Tire, Kavir Tire, Yazd Tire, Artaville Tire and Pars Tire were collected and used to solve the model.

The modeling of this chain is done based on environmental considerations, minimizing the total cost of the chain and maximizing the response to customer demand. Finally, this model is solved using NRGA and NSGAII meta-heuristic algorithms and the obtained results are compared with each other. Also for credit evaluation suggested model using limitation epsilon method has been implemented in the GAMS software too.

In this research, the design of closed-loop supply chain network in conditions of uncertainty and its solution using meta-heuristic algorithms is considered and based on the research results, executive proposals were presented.

Accurately predicting of earning management to detect and identify manipulation of financial statements has always been one of the most fundamental Challenges ahead of financial reports users. Applying the Beneish model can be one of the most appropriate models for modeling in the field of earning management prediction. Beneish model 1999 has been developed for this purpose by emphasis on the variables of corporate governance system including audit committee structure, legal inspector and independent auditor, board of director's structure and corporate ownership structure requirements. The data of 81 companies listed on TSE during 2012-2018 has been analyzed by the hybrid method of multi-layer perceptron MLP neural network and metahuristic algorithms of biogeography based optimization BBO, Imperialist Competitive Algorithm ICA and water cycle algorithm WCA. The accuracy of the model by hybirid mothodes of MLP-BBO, MLP-ICA and MLP-WCA has been increased from 59.08 ، 59.96 and 59.79 percentages to 92.06 ، 89.24 and 79.72 percentage respectively. The results indicate the accuracy improvment of the proposed model in detecting earning-manipulator companies and also the higher efficiency of the hybrid method of MLP-WCA compared to the other methods in optimization of multilayer perceptron neural network.

In recent years, meta-heuristic algorithms and their application in solving complicated, nonlinear and NP-hard problems have dramatically increased, while new algorithms have constantly being introduced. In this research, with the aim of ranking meta-heuristic algorithms, using group decision making techniques (different from other research in this field), 5 algorithms including: GA, PSO, ABC, SFLA and ICA by 15 standard test functions, and considering 2 attribute: "mean of answers" and "run time", have been compared. Then they are ranked by 3 group decision making methods including: "Cook and Seiford", "Condorcet" and "Dodgson". In addition, as in ranking by "Condorcet" and "Dodgson" methods, sometimes some options posit the same rank, therefore, in this study; we presented a proposal to overcome the limitation. Then the algorithms with these proposed methods were ranked. Finally, the overall ranking is done using an allocation model our results show that the overall ranking is as follows, respectively: PSO, ICA, GA, ABC and SFLA.

The supply chain management is an important factor in current competitive market. In recent years, the shortage of resources for answering an increasing food demand has increased researchers’ attention to the food supply chain. Given the importance of fish in the Household Food Basket, the development of aquaculture and recycling of returned goods in reverse logistics would significantly help with preserving water resources, as well as sustainable development. Therefore, government agencies and aquaculture industry beneficiaries are interested in reverse logistics. This study is focused on the optimization of a closed-loop supply chain of fish. To this end, a new bi-objective mathematical model is proposed that both minimizes total costs and maximizes fulfilling customers demand in uncertainty situation. Several well-known multi-objective meta-heuristic algorithms and a proposed hybrid meta-heuristic algorithm are applied to identify Pareto solutions. The solutions are then compared in terms of performance metrics. Also, the epsilon-constraint method and sensitivity analysis are used to validate the algorithms and evaluate the performance of the model. Lastly, the VIKOR method is used to select the superior method. To demonstrate the capability of the proposed model, a closed-loop supply chain of trout in northern Iran is investigated as a case study. The results show that the developed model could be effective in reducing the costs and increasing customer satisfaction.

This paper aims to design and regulate the Adaptive Neural Fuzzy Inference Systems (ANFIS) of supplier development, determine the importance of different categories of supplier development (SD), and suggest appropriate activities based on the results for the development of suppliers in the automotive industry.

To design ANFIS, Grid Partitioning, Subtractive Clustering, and FCM have been used. Then, they have been regulated using Back Propagation (BP), Hybrid, Ant Colony Optimization (ACO), Differential Evolution (DE), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO) methods. A five-step procedure has been used for sensitivity analysis of the supplier development categories. By comparing the results of the regulated systems, the most appropriate ones were selected. Also, based on the supplier development score in different categories separately, the supplier development score was predicted for 53 strategic suppliers in the automotive industry. By sensitivity analysis, SD-related categories were prioritized to guide automotive industry manufacturers to use SD-related activities.

Findings indicated that the FCM compared to the other two methods; meta-heuristic regulation methods compared to BP and Hybrid, and ACO, DE, and GA compared to PSO led to better results. The ACO in all systems, the DE in four systems, and the GA in two categories were identified as the dominant methods, while the PSO was not dominant in any of the categories. This finding implies the priority of meta-heuristic algorithms as ACO> DE> GA> PSO, based on the data of this study. The results of the correlation between the scores of the ANFIS and the average scores of the experts show that the designed ANFIS has high accuracy.Practical implications: The results of this study will direct manufacturers' investments and direct involvement in SD. The findings encourage manufacturers and suppliers of the Iranian automotive industry first to activities related to the development of environmental capabilities and then activities related to the development of three other categories. The managers of Iran's automotive industry are suggested to apply the activities related to the development of suppliers' environmental capabilities. These activities include evaluating the supplier's environmental performance and feedback, sharing environmental information, ethics and social responsibility, obtaining environmental and social certifications, developing programs to improve the quality of life of target communities, green procurement, and environmental awareness, logistics activities Inversion, and joint efforts to improve performance are sustainable.

This study was one of the first in-house studies to compare the results of meta-heuristic algorithms compatible with ANFIS in the field of SD. In addition, in terms of implementation, it offered suitable SD activities to car manufacturers.