Mohammad Khajehzadeh ¹, Mahdiyeh Eslami ²

Affiliations
1 Civil Engineering Department, Anar Branch, Islamic Azad University, Anar, IR
2 Electrical Engineering Department, Anar Branch, Islamic Azad University, Anar, IR

Abstract

This paper presents an effective optimization method for nonlinear constrained optimization of retaining structures. A newly developed heuristic global optimization algorithm called gravitational search algorithm (GSA) is introduced and applied for the optimization of retaining structures. The optimization procedure controls all geotechnical and structural design constraints while reducing the overall cost of the retaining wall. To verify the efficiency of the proposed method, two design examples of retaining structures are illustrated. To further validate the effectiveness and robustness of the GSA, these examples are solved using two other heuristic algorithms namely particle swarm optimization and genetic algorithm. The comparison between the results of the new method and selected other algorithms indicate that, the proposed method could provide solutions of high quality, accuracy and efficiency and outperforms other methods for optimum design of retaining structure.

Keywords

Retaining Structure, Optimization, Minimum Cost, Gravitational Search Algorithm, Heuristic Method

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Hadi Zayandehroodi ¹, Hamzehbabaei ¹, Mahdiyeh Eslami ¹

Affiliations
1 Department of Electrical Power Engineering, Kerman Branch, Islamic Azad University, Kerman, IR

Abstract

In this paper by introduction of a general model of network, the behavior of networks has been analyzed respect to power consumption of transmitter and receiver nodes, and the length of CSMA slots. Then by prediction of model in future the input variables of network can be adjusted subject to this optimized energy consumption of network. The solution of the optimization problem of this paper, is quadratic programming. The objective function of quadratic programming is obtained by generalized model predictive control. Lower threshold values for reliability and delay probability less than a maximum, was assumed equal to 95%. Using quadratic programming, a vector of network inputs are obtained. This vector includes network variables such as power consumption of transmitter and receiver nodes and CSMA slot length. The vector also includes input variables of network for future. At each moment of time due to the dynamic changes of network reliability and energy consumption, network input vectors are updated By comparing the simulation results of the proposed method and when the model is not predicted, the proper performance of quadratic programming in network behavior optimization can be understood. By quadratic programming, the energy consumption of network is optimized and the reliability as well as the probability of the delay overreaching the allowed limitation does not exceed the threshold domain.

Keywords

Cooperative Communication Wireless Networks, Energy Consumption Optimization, Generalized Model Predictive Control, Latency, Quadratic Programming, Reliability

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