imperialist competitive algorithm

In this paper, the multi-objective optimum design of plate fin heat exchangers is investigated. To this end, the efficiency and cost as two important factors for the design of heat exchangers are regarded as the objective functions. Fin pitch, fin height, fin offset length, cold stream flow length, no-flow length and hot stream flow length are considered as six design parameters. The ε-NTU method is applied to estimate the heat exchanger pressure drop and its effectiveness. A case study related to a gas furnace in Barez tire group located in the northwest of Kerman, Iran is considered for the constant parameters. The Imperialist Competitive Algorithm (ICA) is used to find the optimal design parameters to achieve the maximum thermal efficiency and minimum consumption cost. The method of the weighting coefficients is applied to change the considered multi-objective optimization problem as a single objective one. Furthermore, the effects of variations of the design parameters on the objective functions are independently investigated, and the related graphs are presented.

This article addresses a new approach to 3D path planning of UCAVs. To solve this NP-hard problem, imperialist competitive algorithm (ICA) was extended for path planning problem. This research is related to finding optimal trajectories before UCAV missions. Developed planner provides 3D optimal paths for UCAV flight with real DTM of Tehran environment. In UCAV mission, final computed paths should be smooth that made the path planning problems constrained. This planner can offer flyable 3D paths based on mission requirements. It’s a comprehensive study for efficiency evaluation of EA planners, and then novel approach will be proposed and compared to ICA, GA, ABC and PSO algorithms. Then path planning task of UCAV is performed. Simulations show advantage of proposed methodology.

In this paper, the multi-objective optimum design of shell and tube heat exchangers is investigated. A thermal modelling of an industrial shell and tube heat exchanger is performed using an -NTU method for estimating the shell side heat transfer coefficient and pressure drop. The efficiency and total cost (includes the capital investment for the equipment and operating cost) are two important parameters in the design of heat exchangers. The fixed parameters and the ranges of the design variables are obtained from a shell and tube recovery heat exchanger in Barez tire production factory located in Kerman city, Iran. The Imperialist Competitive Algorithm (ICA) is used to find the optimal design parameters to achieve the maximum thermal efficiency and minimum consumption cost as the objective functions. The tube inside and outside diameters, tube length and the number of tubes are considered as four design variables. Furthermore, the effects of changing the values ​​of the design variable on the objective functions are independently investigated. At the end, the obtained Pareto front and the related design variables and their corresponding objective functions are presented.

Optimization of turning process is a non-linear optimization with constrains and it is difficult for the conventional optimization algorithms to solve this problem. The purpose of present study is to demonstrate the potential of Imperialist Competitive Algorithm (ICA) for optimization of multipass turning process. This algorithm is inspired by competition mechanism among imperialists and colonies, in contrast to evolutionary algorithmsthat perform the exploration and exploitation in the solution space aiming to efficiently find near optimal solutions using a finite sequence of instructions. To validate the proposed approach, the results of ICA were finally compared with Genetic Algorithm (GA).Based on the results; ICA has demonstrated excellent capabilities such as simplicity, accuracy, faster convergence and better global optimum achievement. The outcome shows the success of ICA in optimizing the machining process indicating that data analysis method developed in this work can be effectively applied to optimize machining processes.

The metal sheets play an important role in the mechanical design, particularly in the aerospace structures. The rivet connections are frequently used to connect these sheets. The riveting quality greatly influences the rupture of the rivet and the sheet. The various parameters affect the quality of this operation. In this paper, the optimization of the parameters contributing to the riveting quality in order to minimize the value of the maximum tangential stress in the sheets is addressed. To this end, the tolerance of the hole diameter in the top and the bottom sheets, the friction coefficient, and the tolerance of the rivet diameter and the rivet length were considered as the parameters influencing the riveting quality. A total of 64 models were obtained by the permutations of the parameters two at a time. The outputs were determined using the finite element method. The objective function for the optimization is the maximum tangential stress for which there is no analytical relation. Thus, three methods including the multivariable linear regression (MLR), the artificial neural network model of the radial basis function (RBF) type, and the hybrid model of the artificial neural network and the genetic algorithm (ANN-GA) were employed to model this function. Further, the performance of the three models was compared and the most suitable one was selected to model the objective function. The regression model was used to model the values of the height and the diameter after riveting. The imperialist competitive algorithm is utilized to solve this optimization problem. The obtained value for the maximum tangential stress using the imperialist competitive algorithm is 16368 pounds per square inches. After modification, this value increased to 23440 pounds per square inches using the finite element method. . The 0.07689 inches and 0.18524 inches were obtained for the height and diameter of the rivet after riveting, respectively.

Machining processes such as end milling are the main steps of production which have major effect on the quality and cost of products. Surface roughness is one of the considerable factors that production managers tend to implement in their decisions. In this study, an artificial neural network is proposed to minimize the surface roughness by tuning the conditions of machining process such as cutting speed, feed rate and depth of cut. The proposed network is tested by many test problems of Ghani et al.[1] study and the weights of network are optimized by using three meta-heuristics, genetic algorithm (GA), imperialist competitive algorithm (ICA). The results show the efficiency and accuracy of the proposed network.

This paper presents dynamic modelling and control of a linear prismatic series of elastic actuator. The capability of generating large torques is why this actuator is increasingly used in human-assistive robotic systems. Due to having human in the loop, the actuator requires precise control. A fractional PID controller known for its improved performance is used for the control, due to having additional degrees of freedom than the classical PID. The actuator has one servo driver and five controller gains to be tuned. The gains are optimized using both Particle Swarm Optimization (PSO) and Imperialist Competitive Algorithms (ICA). Comparison of the results from the two optimization methods illustrates that the PSO tuned FOPID controller has a slightly better performance, faster convergence and better settling time. Next, the PSO tuned controller is compared with a Genetic Algorithm (GA) tuned PID controller. It is shown that the PSO tuned FOPID controller continues to offer better performance, especially in terms of its rise time and settling time.

Imperialist competitive algorithm (ICA) is a new socio-politically motivated global search strategy. The ICA is applied to hybrid composite laminates to obtain minimum weight and cost. The approach which is chosen for conducting the multi-objective optimization was the weighted sum method (WSM). The hybrid composite Laminates are made of glass/epoxy and carbon/epoxy to combine the lightness and economical attributes of the first with high-stiffness property of the second in order to make trade-off between the cost and weight as the objective functions and natural flexural frequency as a constraint. The results were evaluated for different weighting factors (a) including optimum stacking sequences, and number of plies made of either glass or carbon fibers using the ICA, and were compared with those using the genetic algorithm (GA) and ant colony system (ACS). The comparisons confirmed the advantages of hybridization and revealed that the ICA outperformed the GA and ACS in terms of function’s value and constraint accuracy.

Because the underlying physiology of pathological tremor in a Parkinson''s patient is not well understood, the existing physical and drug therapies have not been successful in tremor treatment. Different mathematical modeling of such vibration has been introduced to investigate the problem and reduce the existing vibration. Most of the models have represented the induced vibration as a sinusoidal wave for mathematical simplification. In this study, a more realistic model based on random vibration was used to attack the problem of tremor suppression. A simple approach for suppressing the tremor associated with Parkinson''s disease was presented. This paper was concerned with a multi-objective approach for optimum design of linear vibration absorber subject to random vibrations. Analytical expressions, for the case of non-stationary white-noise accelerations, were also derived. The present approach was different from conventional optimum design criteria since it was based on minimizing displacement as well as accelerating variance of the main structure responses without considering performances required against discrepancy in response. In this study, in order to control the tremor induced on biomechanical arm model excited by non-stationary based acceleration random process, multi-objective optimization (MOO) design of a vibration absorber was developed and performed using modern imperialist competitive optimization algorithm for multi-objective optimization. The results demonstrated importance of this method and showed that multi-objective design methodology provided significant improvement in performance stability and giving better control of the design solution choice.

This paper deals with volume fraction optimization of Functionally Graded (FG) beams resting on elastic foundation for maximizing the first natural frequency. The two-constituent functionally graded beam consists of ceramic and metal. These constituents are graded through the thickness of beam according to a generalized power-law distribution. One of the advantages of using generalized power- law distribution is the ability of controlling the materials volume fraction of FG structures for considered applications. The primary optimization variables are the four parameters in the power-law distribution. Since the search space is large, the optimization processes becomes so complicated and too much time consuming. Thus a novel meta–heuristic called Imperialist Competitive Algorithm (ICA) which is a socio-politically motivated global search strategy is applied to find the optimal solution. A proper and accurate Artificial Neural Network (ANN) is trained by training data sets obtained from generalized differential quadrature (GDQ) method to reproduce the behavior of the structure in free vibration. The ANN improves the speed of optimization process by a considerable amount. The performance of ICA is evaluated in comparison with other nature inspired technique Genetic Algorithm (GA). Comparison shows the success of combination of ANN and ICA for design of material profile of FG beam. Finally the optimized material profile for the optimization problem is presented.

Artificial neural network was considered in previous studies for prediction of engine performance and emissions. ICA methodology was inspired in order to optimize the weights of multilayer perceptron (MLP) of artificial neural network so that closer estimation of output results can be achieved. Current paper aimed at prediction of engine power, soot, NOx, CO2, O2, and temperature with the aid of feed forward ANN optimized by imperialist competitive algorithm. Excess air percent, engine revolution, torque, and fuel mass were taken into account as elements of input layer in initial neural network. According to obtained results, the ANN-ICA hybrid approach was well-disposed in prediction of results. NOx revealed the best prediction performance with the least amount of MSE and the highest correlation coefficient(R) of 0.9902. Experiments were carried out at 13 mode for four cases, each comprised of amount of plastic waste (0, 2.5, 5, 7.5g) dissolved in base fuel as 95% diesel and 5% biodiesel. ANN-ICA method has proved to be selfsufficient, reliable and accurate medium of engine characteristics prediction optimization in terms of both engine efficiency and emission.

In this research work، first، based on the three-dimensional elasticity theory and by means of the Generalized Differential Quadrature Method (GDQM)، free vibration characteristics of functionally graded (FG) rectangular plates resting on Pasternak foundation are focused. The two-constituent functionally graded plate consists of ceramic and metal grading through the thickness. A three-parameter power-law distribution is considered for the ceramic volume fraction. The benefit of using a three-parameter power-law distribution is to illustrate and present useful results arising from symmetric، asymmetric and classic profiles. A detailed parametric study is carried out to highlight the influences of different profiles of fiber volume fraction، three parameters of power-law distribution and two-parameter elastic foundation modulus on the vibration characteristics of the FG plates. The main goal of the structural optimization is to minimize the weight of structures while satisfying all design requirements imposed. Thus، for the second aim of this paper، volume fraction optimization of FG plates with objective of minimizing the density to achieve a specified fundamental frequency is presented. The primary optimization variables are the three parameters of the volume fraction of ceramic. Since the optimization processes is complicated and too much time consuming، a novel meta–heuristic called Imperialist Competitive Algorithm (ICA) which is a socio-politically motivated global search strategy and Artificial Neural Networks (ANNs) are applied to obtain the best material profile through the thickness. The performance of ICA is evaluated in comparison with other nature inspired technique Genetic Algorithm (GA). Comparison shows the success of combination of ANN and ICA for design of material profile of FG plates. Finally the optimized material profile for the considered optimization problem is presented.