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The design of the structural supports has always been practically important in engineering applications. In addition to holding a structure properly, supports can also be utilized to improve the structural performances. In this study, by using modified finite element method (MFEM) and Imperialist Competitive Algorithm (ICA), the maximum of bending moment was minimized. In this paper both elastic and rigid supports are taken into account. As compared to other design optimization methods, ICA is robust, more efficient, and requiring fewer number of function evaluations, while leading to better quality of results. Appling the modified finite element method not only reduces computational cost and increases convergence rate, but also reach the global optimum position of supports. Three classical examples are given to demonstrate the validity and capability of the proposed optimization procedure for finding the global support positions. Results show that support position optimization by using present method, can reduce the maximal moment significantly, and deserves more investigation.

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Microgrids are small-scale, low voltage (LV) power networks which employ renewable distribution energy resources (DERs) with power electronic interfaces (PEIs). Microgrids as single controlled units and active distribution networks require flexible control systems to ensure reliable and secure operation in different modes. These various operations of microgrid cause variations in voltage and frequency especially in island mode. In this paper, a new control method with two optimization algorithms (genetic algorithm (GA) & imperialist competitive algorithm (ICA)) are proposed to eliminate both voltage and frequency disturbances. Also, a new concept of conventional droop control in format of fast droop controller (FDC) is designed to guaranty the microgrid system reliability with cooperation of a modern frequency controller. Simulation results show the truth behavior of proposed approach in comparison with previous methods

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One of the most important issues in industry, particular casting industry is to determine the internal structure of objects such as identifying the interfacial boundary configurations between material, identification of impurities or mechanical properties of the material. The objective of the present inverse problem is to identified simultaneously two regular interfacial boundary configurations and mechanical properties of the components of a multiple (three) connected domains using a discrete number of displacement measurements obtained from an uniaxial tension test. A unique combination of a global optimization method i.e. the Imperialist Competitive Algorithm (ICA) and local optimization methods i.e. Simplex Method (SM) along with the inverse application of the Boundary Elements Method (BEM) are employed in an inverse software package. A fitness function, which is the summation of squared differences between the measured displacements and computed at identical locations on the exterior boundary, is minimized. The obtained results (run-time and error-rate), clearly demonstrate the efficiency of this present algorithm (the Imperialist Competitive Algorithm and Simplex Method) to optimize the objective function and the estimation simultaneously two regular interfacial boundary configurations and mechanical properties.

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Identification of the thermal conductivity of a functionally graded material (FGM) is considered as an inverse heat conduction problem. ‎In this investigation, the measurements of the temperatures on the portion of the 2D body where heat flux is specified as the boundary ‎condition and/or the heat flux on the portion of the boundary where temperature is specified as the boundary condition are used as ‎additional data needed to identify the thermal conductivity of the FGM domain in an inverse procedure. The thermal conductivity is ‎approximated as a quadratic function of only one direction, and therefore three constant coefficients should be estimated simultaneously.‎‏ ‏The solution of the direct heat conduction problem for‏ ‏FGM domain is obtained using the boundary elements method (BEM).‎‏ ‏The ‎imperialist competitive algorithm (ICA) which is an evolutionary and meta-heuristic global optimization is used to identify the constants ‎in the thermal conductivity function of the quadratic FGM. An inverse computer code is developed which employs the boundary ‎temperature and heat flux measurements data obtained by solving the direct boundary elements code with known thermal conductivity. ‎To show the feasibility and effectiveness of the developed inverse code, a number of example problems are solved and results are ‎verified.‎