ant colony algorithm

The shortage and restriction of drinking water and even water consumption in many water-scarce countries of the globe, including Iran, has become an essential and vital issue. This is one of the key reasons that people and communities maximize the development of crops. The best management of crop production, taking into account the requirement for water in the agricultural sector, is one of the acceptable options in nations like Iran, which is also active in the field of agriculture. On the basis of this, it is vital to look at various analytical, planning, and decision-making techniques. Additionally, taking into account and calculating virtual water can significantly improve the cultivation pattern and decrease the amount of water currently consumed; as a result, optimizing the cultivation pattern using virtual water calculations can be highly beneficial. Coherent management and accurate decision-making in a variety of areas will result from the usage and implementation of spatial information systems, and taking into account virtual water will enhance navigation patterns and decrease water consumption. In order to achieve the best allocation of the cultivation pattern and as a result, the correct use of water in the agricultural lands of Ben-Rood district in the operations of Varzaneh city, located in the southeast of Isfahan province, was achieved in the current research by using the decision-making system that was created with the capabilities of GIS and artificial intelligence, along with the virtual water calculations of some agricultural plants. Using the collective intelligence algorithm of the ant community (ACO) in conjunction with the spatial information system, it was possible to achieve this goal after examining the optimization methods in decision-making using environmental parameters. This was done by taking into account the virtual water of cultivation and the growth of plants in the irrigation networks of the agricultural lands of the mentioned sector. Finally, the findings of this study demonstrate that the amount of water consumed may be decreased to 37% of the initial amount after optimizing the allocation of land for the cultivation of specific crops in that area, based on the virtual water of the crops.

Construction is one of the most important issues in a society economically, socially, culturally and so on. But the issue of quality in the construction projects requires discussion takes more scientific. The objective of this study is to investigate the optimization of the quality of construction projects and construction costs based on system reliability theory. Also, a real construction project is presented to evaluate the efficiency of the proposed model. In order to achieve this goal, a four-step algorithm is defined and proposed model using two methods M3AS and classic ant colony algorithm is solved. The results of both methods are compared. Parzin five-story residential building located on Zaferanieh Street is studied. The results indicated that M3AS innovative approach through local search is more capable than the classical ant colony algorithm for optimization. Most previous studies have not considered the importance of the quality of project. In this study, quality in the form of reliability at the time of completion of the work is quantified. The proposed model has been so overcome lack of expression and effectively using reliability theory. This model can automatically produce reliability and optimal cost of system with regard to structure function of reliability and non-linear function of cost-reliability. In this study, multi-objective optimization model based on reliability theory extends to decision-makers to determine and choose between cost and quality for construction projects. Since a major part of the annual budget allocated to construction projects, these projects are the most important component of the country's development that must be managed properly in terms of cost and quality and be completed according to the schedule. iT is clear that the status of the construction industry in terms of quality and cost should be much improved.

Vehicle routing problem has an important place in applied research. Finding the right path to solve Vehicles Routing Problem is one of the most significant measures taken in recent decades. In this routing method, each delivery must be made within time windows, and not to mention the fact that each customer must be visited only once. At the present paper, Ant Colony Algorithm Optimization for Vehicle Routing Problem with Time Windows and Capacity Constraint (ACOVRPTWCC) has been utilized. First, Ant Algorithm for Vehicle routing Problem with Time Windows and vehicle Capacity Constraint classic has been implemented. In this way, of 9 problem implemented through 100 customers in 6 problems, solutions are optimized. Then, algorithm is carried out on a new type of vehicle routing problem with time window. The most decrease of cost related to R206 problem by 32.72 percent minimization of cost. For any problem, on the average, 20 second time is needed.

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. Ultimately, to demonstrate the capabilities of this algorithm, it was compared with the genetic algorithm. 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 genetic theory, whereas, given the movement of 30 ants, the time required to arrive to the scene by the ambulances for ant colony algorithm and the genetic algorithm was calculated 19' 45'' and 24' 15'', respectively.

Tanks in water distribution networks are used to store water for emergency conditions, fire flow demand and demand oscillations controll. Construction of tanks spends a lot of money and therefore using whole volume of tanks is essential while operation. Otherwise, if tank volume will be more or less than what is required during operation, tank reliability is reduced. Accordingly, in this paper, a new relationship for tank reliability according to water level variation in tanks is defined. Therefore, maximum water level in tanks is defined as the decision variable. The definition of tank reliability is as follows. At first, the values of maximum level for each tank is computed such a way that optimal use is provided from balancing volume of tanks. In fact, for these maximum level values, maximum reliability is acheived for each tank. Now if during optimization process, a value lower than these computed maximum level is selected for decision variables, tank reliability is reduced. To compute the value of tank reliability, the values of tank water level for the selected decision variables is devided by the values of tank water level for maximum tank reliability. Also, because water level variation can effect on pressure and water age in demand nodes, this effect is investigated by considering hydraulic and quality reliability. In fact, variation of water level in tanks changes node demand pressures and in result actual node demands. Also, variation of water level or on the other hand variation of storage volume affects on water age in demand nodes. Besides, in order to investigate the simultaneous effect of water level variation on hydraulic and quality reliability, a relationship is also defined for integrated reliability. Definition of integrated reliability is to investigate whether there is optimum maximum tank level values that both hydraulic and quality reliability is improved simultaneusly while tank construction costs is minimum. Optimal management of tanks in water distribution networks to provide required water of consumers with desired quality is of high importance. To acheive this, optimization is defined as a powerful tool. In this paper, by focusing on operation phase, multiobjective optimization of water distribution performance is performed in which tank costs is considered as the first objective and tank reliability, node hydraulic reliability, node water age reliability and integrated reliability is considered as the second objective. Ant colony algorithm is codified in Microsoft Visual C for optimization due to its simplicity and high performance. The validity of the edited algorithm is tested on mathematical functions and proved to be applicable on water distribution networks. The created trade-off curve from multiobjective optimization helps the decision makers to select the top choice based on the importance of their own criterion whether it is hydraulic or quality.

Transportation Discrete Network Design Problem (TDNDP) is the problem of selecting a feasible subset of proposed projects، i. e. highways، so as to minimize the total travel time of the network users. This problem falls into the NP-Hard complexity class of problems for which no efficient algorithm exists for exact solution in practical cases. As a result، to find a rather good solution for the problem in a reasonable amount of time، many studies addressed TDNDP through heuristic and meta-heuristic approaches. However، application of parallel computing is still another way to further speedup TDNDP solution approaches. This paper is going to explore the application of parallel computation in a meta-heuristic algorithm in TDNDP. A parallel Ant Colony Algorithm (ACA)، based on the study of Poorzahedy and Abulghasemi، is proposed with the master-worker parallelization paradigm. The Chicago Sketch transportation network is considered as a case study with 16 bi-directional proposed projects. The results of parallelization over a cluster of 8 processing cores support that parallel algorithms can achieve high quality solutions in 4000 seconds، while this happens for the single-core algorithm in 10000 seconds. The parallel ACA finds the exact solution of the problem in two instances out of three runs and in the other instance it converges to a solution with 0. 07 percent error from the exact solution. The parallel performance of ACA is also compared with that of the branch and bound algorithm. The comparison indicates that the parallel branch and bound algorithm requires more that 32000 seconds running time to find the exact solution of the problem، while the parallel ACA reveals a much faster performance. Transportation Discrete Network Design Problem (TDNDP) is the problem of selecting a feasible subset of proposed projects، i. e. highways، so as to minimize the total travel time of the network users. This problem falls into the NP-Hard complexity class of problems for which no efficient algorithm exists for exact solution in practical cases. As a result، to find a rather good solution for the problem in a reasonable amount of time، many studies addressed TDNDP through heuristic and meta-heuristic approaches. However، application of parallel computing is still another way to further speedup TDNDP solution approaches. This paper is going to explore the application of parallel computation in a meta-heuristic algorithm in TDNDP. A parallel Ant Colony Algorithm (ACA)، based on the study of Poorzahedy and Abulghasemi، is proposed with the master-worker parallelization paradigm. The Chicago Sketch transportation network is considered as a case study with 16 bi-directional proposed projects. The results of parallelization over a cluster of 8 processing cores support that parallel algorithms can achieve high quality solutions in 4000 seconds، while this happens for the single-core algorithm in 10000 seconds. The parallel ACA finds the exact solution of the problem in two instances out of three runs and in the other instance it converges to a solution with 0. 07 percent error from the exact solution.

Reduced transfer time results in increased levels of service to citizens and their satisfaction would increase consequently. To this end، using new and improved routing algorithms and localizing them in urban areas، considering their scope and extent، can be efficient in urban management and relief sectors. The purpose of this study is to look at the operation of the routing algorithms، including Genetic and Ant colony and due to the size of the city conduct a research to optimize the routing by using Game theory algorithm in the software ArcGis. Therefore، programs written for routing with Genetic algorithm، Ant colony and Game theory were called for in the software ArcGis and then used for routing. The results showed lack of spatial information database shared between rescue squads makes their services impossible in the least amount of time and if spatial databases are made and used، due to the small size of the city of Semnan، the time to achieve an optimal route by ArcGis using Game theory algorithm would be shorter compared with other algorithms. Therefore، the time for the Genetic theory، Game theory and Ant colony are 0. 27، 0. 23 and 0. 19 ms. respectively. Time to reach the crash site، using the routing by ArcGis program with Genetic algorithm، Ant colony and Game theory are 3 minutes and 5 seconds، 3 minutes and 15 seconds، and 2 minutes and 42 seconds، respectively.

Bus transportation system is of the most important public transportation. Improvement of this system has a considerable effect on the efficiency of the urban transportation system as well as increasing passengers’ satisfaction and attraction. Consequently, this will result in energy saving and reduction in volume of traffic and environmental pollution. All the issues about bus network design including the optimal routing and the fleet size in each path are taken into account as a crucial issue in public transportation planning. These issues are so complicated that no exact solution can be found in real cases. This study attempts to introduce a method to find optimized path of the bus network in terms of passengers’ satisfaction through applying ant colony algorithm as a metaheuristic algorithm. The passengers’ satisfaction is assessed by an overall objective function which includes some desirable parameters such as the fulfilled (coverage of) demands, the path frequency, the network accessibility, the number of transfers and the traveled distance. In this paper, the Sioux-falls network is simulated in proposed model and the results are compared with the shortest path algorithm. The improvement in represented paths via proposed algorithm is remarkably evident compared with those from shortest path algorithm.

Uncertain demand is one of the most important sources of uncertainties in transportation networks. It is almost based on various sources such as difficulty in forecasting of socio economic variables and the model’s deficiency. This demand is one of the main operative elements of transportation network design problems. The aim of this paper is to evaluate the effect of demand variability in discrete and continuous transportation network design problem. Thus, the uncertainty of demand has been assumed to have a maximum and a minimum rate, and the level of uncertainty in demand is assumed to vary between these extreme values as in a box. In this paper two Meta heuristic methods; Ant Colony Optimization (ACO) and Genetic Algorithm (GA), has been used to solve the aforementioned problem. It has been shown in this paper that relative to designing a network with maximum demand, the proposed method has found demands with 4 to 10 percent longer travel time. This could considerable effect on results of the network design problem.

Transportation network design is one of the most important issues in transportation engineering, which is complicated by uncertainty. Uncertainty in demand is one of the most common types of uncertainty in traffic networks, which comes from various sources such as difficulties in forecasting of trip productions and attractions. In this paper, a method based on robust optimization for discrete network design in scenario-based variable demand conditions is used, in which the level of imposed uncertainty of that problem will depend on designer choice. For this reason, the objective functions of weighted mean and variance total travel time in several demand scenarios are implemented; and for solving that problem, genetic and ant colony algorithms are used. To examine the proposed model, two case studies are applied to an ordinary network. In the first one, the demand pattern in hypothetical scenarios has a high disparity, and the variance of total travel time is more important in the final design than the mean. In the second case study, the demand pattern has a low disparity, and the weight of the mean and variance is more significant in the design solution. Based on two conditions, the results of design in the condition of uncertainty are different with independent design of each scenario, which emphasizes the use of optimization methods in conditions of uncertainty to solve similar problems. Also, in comparison of genetic and ant colony algorithms in various weights of the objective function, the ant colony algorithm found better solutions with less calculation effort.