imperialist competitive algorithm

The goal of any structural design process is to produce a safe design that meets all the design codes requirements, while trying to minimize the cost of the design. In Civil Engineering Structures, majorly cost is the objective of optimization of the structure. In most cases, the material cost is included in objective function. In some cases, construction cost is also included in objective function. The objective of the optimization is to minimize the cost of the Structures while satisfying strength and serviceability constraints. In order to investigate the efficiency and accuracy of the modern optimization methods in comparison with classical methods, an elevated INTZE tank is primarily designed based on the recommendations of Indian code and then optimized using Genetic Algorithm (GA), Firefly Algorithm (FA), Imperialist Competitive Algorithm (ICA) and Particle Swarm Optimization (PSO). objective function is the material cost of the tank which is the function of the design variables. The total material cost of the tank can be expressed as the sum of the cost of concrete, reinforcement and cost of formwork. Design constraints of the problem also include the stresses of the structure members and the limitations of the volume and geometric dimensions of the water tank. All constraints were satisfied and the stresses and stability constraints were in acceptable ranges.

management of these resources. The use of modern methods, including ANN and evolutionary algorithms in estimating water quality, due to its high speed, convergence and efficiency, saves and reduces costs and the best management. The main purpose of this study is to evaluate the results of the chemical analysis of groundwater samples from 14 wells in Jolfa plain and also estimate the ground water quality parameters using imperialist competitive algorithm (ICA) and ANN. Therefore, ground water quality parameters include TDS, EC and SAR estimate using imperialist competitive algorithm (ICA) and ANN and groundwater resources quality in terms of drinking, agriculture and industry were examined by Wilcox, Schuler and Piper and standards. Correlation coefficient of (R2) 90%, indicates the acceptable accuracy of ANN compared with ICA algorithm in estimating groundwater quality parameters. By using different diagrams the results show that the hardness of samples are too much and not suitable for drinking. It should also be noted that a very high hardness and corrosion of sample, water not be used in industry. The salinity of 7 samples is very high and according classification is located in C4S2 class and not suitable for agricultural consumption.

One of the most important groups of heuristic algorithms is meta-heuristic algorithms which has been received much attention in recent years. Since these approaches are very efficient for escaping local optimum points, they are one of the best algorithms for solving operation research problems. This paper proposes a hybrid imperialist competitive algorithm called (IICA) in which a tabu list and several local search algorithms including the 1–0 exchange moves, the 1–1 exchange move and the 2-Opt move are used for improving performance of ICA. Furthermore, some of traveling salesman problems as one of the most important versions of the vehicle routing problem are considered for comparing this algorithm with other mataheuristic algorithms. Computational results on 13 standard benchmark problem instances show that the proposed algorithm is comparable in terms of solution quality to the other meta-heuristic algorithms

Evaluating the groundwater level in arid and semi-arid regions of the country requires accurate prediction and efficiency of its fluctuations. The use of modern methods, including evolutionary algorithms, artificial neural networks and fuzzy methods, is very useful for prediction the groundwater level and generating artificial water surface data due to its high efficiency. In this research, by using Election and Imperialist Competitive Algorithms, artificial neural network, monthly data for 9 years as well as groundwater level of 10 wells, predicted the 7-year the groundwater level of Reshtkhar plain in khorasan-Razavi. In order to train the models, the statistic data was provided on 10 observation wells with a 9-year (2002-2014), which 70% of the data was introduced as training data to the model and 30% of the data was used as a test for calibration of the model. The results of the Election Algorithm predicted Reshtkhar groundwater level for the year 1400, between 14 to 16.5 meters in diffirent areas of the plain. Based on the calculations and the results obtained from the statistical parameters, the Election algorithm was RMSE, R2 and NSE, 0.029, 0.90 and 0.73 respectively, compared with the two methods of artificial neural network and Imperialist Competitive Algorithm has a significant ability to predicte the groundwater level.

In recent decades, the optimal use of dam reservoirs among water resource management researchers has been of great interest. So, due to the high performance and capabilities of evolutionary algorithms, in this study, using gray wolf optimizer algorithm (GWO) to predict Urmia Shaharchay dam reservoir and present a short-term forecast program for next years. The gray wolf algorithm imitates the hierarchy of leadership and the mechanism of hunting gray wolves in nature. In this algorithm, four types of gray wolves consists of alpha, beta, delta and omega have been used to simulate the hierarchy of leadership. In this study, considering the annual planning and monthly intervals, GWO algorithm was firstly evaluated for prediction storage of Urmia shaharchay reservoir during 2006-202014 years and the results compared with ICA algorithm. The results showed that GWO algorithm, with a high accuracy of 90%, provides better results in finding optimal response, convergence rate and lower computational cost compared to ICA algorithm. The results of this study indicated that GWO algorithm, an appropriate algorithm to solve the optimal operation of dam reservoir system problem.

In arid and semi-arid regions, like Iran, water is one of the main factors limiting economic development. In the present study, a new high-performance method was used for optimal water allocation in the agricultural sector during the 2002 to 2016 years. Election Algorithm is an iterative population based algorithm, which works with a set of solutions known as population. The results of this method were compared with the results of Imperialist Competitive Algorithm (ICA). The objective function was determined for each product in the agricultural sector as well as product performance, each product benefits and cultivated area of the demand function, then maximization of the objective function and optimal water resources allocation were performed using EA and ICA algorithms. The results of the application of the EA and ICA algorithms to the optimal water allocation problem, showed that in this section, higher benefits could be obtained through economic policies as well as changing the cultivation pattern. Generally, in the case of Moghan plain can be expressed by applying a coefficient of 0.9, 135 Billion Rials, that is, about 40% of the optimal water resources allocation benefits improving between the agriculture sectors compared to the current situation.

In the current century, the pattern of competition in production and services has shifted from competition among independent firms to competition among supply chains. Meanwhile, the issue of strategic design of the supply chain is one of the key issues in the field of supply chain management. In these issues, the aim is generally to provide a number of manufacturing facilities and distribution centers at the candidate sites (in different geometrical areas) in order to cover the demand for various products in the customer’s area. In the proposed model, demand for each product in customer areas is achieved through a number of distribution centers, while these distribution centers provide products from manufacturing factories, and these factories must purchase the requirement components to design and produce each product from suppliers. Here, a single-objective mathematical model of mix integer linear programming is proposed that simultaneously considers the facility location problem in addition to the design of the multi-level supply chain. In this model, there are various objectives, such as minimizing the costs of the procurement and production of components, the preparation and production of products, storage and dispatch the products to distribution centers, sending to customer areas and the cost of building factories and distribution centers. To evaluate the proposed model, the mathematical model is first coded with GAMS optimization software, and then solved for large-scale problems with particle swarm optimization algorithm and imperialist competitive algorithm. Statistical analysis of the results indicates the quality of the model and the proposed solving procedure.

Surface waters play a significant role in supplying water for different demands such as agriculture, domestic and industrial. Due to the variation of river discharges and the non-compatibility of this resource with the water demands, water supply reliability reduces and some damages are inflicted. Therefore, for appropriate use of water in the reservoir, the reservoir operation should be considered. Reservoir operation is performed using different optimization methods. Heuristic algorithms showed a suitable performance during the last two decades. In this study optimized water releases are determined in the Pishin reservoir using Imperialist Competitive Algorithm (ICA) which is based on the social-political evolution. Modeling is done for a 12-month period that is the mean of a 41-years period of hydrological parameters. Objective function is defined as the square of summation between water releases and water demand in each sub-period. Results are compared with the Particle Swarm Optimization (PSO) and Standard Operation Policy (SOP) methods. The mean of the objective function with PSO and ICA is obtained as 3.9804 and 3.9808 respectively, which shows a close estimation of the two methods. The results also show that the content of unmet demand by these two algorithms is 31.1 percent less than SOP method.

Unmanned aerial systems (UAS) are one of the latest technologies utilized in the hazard management and remote sensing. Nowadays, tendency in the development of UAS is toward autonomous navigation or hybrid tasks. In this context, development of comprehensive, efficient methodologies for path planning, control and navigation of UAS can be regarded as one of the fundamental steps for the development of autonomous systems. Up to now, different planning algorithms have been proposed in the specialized literature in order to enrich the framework of autonomous navigation of unmanned aerial systems. However, few efforts have been devoted to design new chaotic path planners for determining the optimal trajectories of these aerial systems in urban areas. An effective path planning technique can attain mission aims with respect to various restrictions of the UAS and less computational time.Chaos theory is one of the most studied theories with different applications in engineering and technology. Most of the natural processes demonstrate chaotic behavior such as black hole and clouds. Past researchers showed that if an evolutionary algorithm be hybridized with chaos, its performance will have improved, considerably. However, most of the evolutionary algorithms are inspired from nature, but all of their steps are random based motions. But nature is not either completely random based or chaotic. Hence, the combination of these theories should be more realistic. With this regard, evolution and chaos are related to each other narrowly in most of the complex natural systems. It is evidenced that some of the chaotic signals can alleviate the premature convergence problem of the evolutionary algorithms in tackling optimization problems.In this article, first, UAS path planning is modeled as a 3D constrained optimization problem. In this modeling, the aim is the optimization of path, fuel and safety with respect to different restrictions. After scheming and suggesting of general planning framework, UAS path planning problem is investigated by comparative study with regard to the studied scenario. For this aim, evolutionary planner is implemented in order to minimize the flight height, path length and energy consumption considering different restrictions such as safe altitude, turning angle, climbing slope, gliding slope, no fly zones and mission map limits. Then, a comprehensive model is employed to describe route-planning task, and then, based on the hybridization of chaos theory with evolutionary computing, four new evolutionary optimizers are developed. Hence, this paper developed four chaotic optimizers including particle swarm optimization, differential evolution, imperialist competitive algorithm and artificial bee colony technique based on 14 chaotic signals.In the rest of this paper, analyses, and extensive performance evaluation of the designed trajectory-planning approaches are performed according to the success rate results, precision and quality of the results, CPU running times, and convergence speed. The results show that the proposed framework can be utilized in represented scenario as an effective path planner. Proposed strategies are capable to compute the optimal paths more efficiently in comparison with the standard algorithms. From the results it is known that the chaotic differential evolution with logistic map can outperform the other compared algorithms.