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In engineering, flood routing is an important technique necessary for the solution of a floodcontrol problem and for the satisfactory operation of a flood-prediction service. A simple conceptual model like the Muskingum model is very effective for the flood routing process. One challenge in application of the Muskingum model is that its parameters cannot be measured physically. In this article we proposed imperialist competitive algorithm (ICA) for optimal parameter estimation of the linear Muskingum model. This algorithm uses imperialism and imperialistic competition process as a source of inspiration. Optimization to identify Muskingum model parameters can be considered as a suitable field to investigate the efficiency of this algorithm.

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The Big Bang-Big Crunch (BB–BC) method is a relatively new meta-heuristic algorithm which inspired by one of the theories of the evolution of universe. In the BB–BC optimization algorithm, firstly random points are produced in the Big Bang phase then these points are shrunk to a single representative point via a center of mass or minimal cost approach in the Big Crunch phase. In this paper, the BB–BC algorithm is presented for optimal cost design of water distribution systems and employed to optimize different types of hydraulic networks with discrete variables. The results demonstrate the efficiency of the proposed method compared to other algorithms.

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This paper presents application of an improved Harmony Search (HS) technique and Charged System Search algorithm (CSS) to estimate transport energy demand in Iran, based on socio-economic indicators. The models are developed in two forms (exponential and linear) and applied to forecast transport energy demand in Iran. These models are developed to estimate the future energy demands based on population, gross domestic product (GDP), and the data of numbers of vehicles (VEH). Transport energy consumption in Iran is considered from 1968 to 2009 as the case of this study. The available data is partly used for finding the optimal, or near optimal values of the weighting parameters (1968-2003) and partly for testing the models (2004-2009). Finally transport energy demand in Iran is forecasted up to the year 2020.

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A charged system search algorithm (CSS) is applied to the optimal cost design of water distribution networks. This algorithm is inspired by the Coulomb and Gauss’s laws of electrostatics in physics. The CSS utilizes a number of charged particles which influence each other based on their fitness values and their separation distances considering the governing law of Coulomb. The well-known benchmark instances, Hanoi network, double Hanoi network, and New York City tunnel problem, are utilized as the case studies to evaluate the optimization performance of CSS. Comparison of the results of the CSS with some other meta-heuristic algorithms indicates the performance of the new algorithm.

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The increase in the number of construction projects and the involvement of a large amount of resources show that one of the most important actions of any construction project is to select the right contractor for the project. Delays in most construction projects and increased costs compared to initial estimates are often due to inadequacies by contractors, indicating that the contractor has not been properly selected. The complexities of the construction industry and the existing uncertainties have led experts to point out that choosing a contractor is a sensitive and difficult task. The purpose of this paper is to design a fuzzy inference system (FIS) to select the best contractor in conditions of uncertainty. The fuzzy inference system is a powerful tool for handling the uncertainties and subjectivities arising in the evaluation process of contractors. The proposed FIS has a two-step computational process in which 28 criteria are determined to evaluate the contractors. The proposed FIS is applied to evaluate and select the best contractor among 5 contractors considered by the general department of roads and urban development in Shahrekord. The studied criteria for evaluating contractors are categorized in six groups, including good history and credibility, equipment, management and specialized staff, economic-financial, skills-ability, and technical criteria. The results show that technical criteria are determined as the most important criteria for evaluating contractors. Furthermore, the results of applying the proposed FIS reveal that contractor C is the best contractor with the final score of 31.40.