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The main aim of the present paper is to propose efficient multi-objective optimization algorithms (MOOAs) to tackle truss structure optimization problems. The proposed meta-heuristic algorithms are based on the firefly algorithm (FA) and bat algorithm (BA), which have been recently developed for single-objective optimization. In order to produce a well distributed Pareto front, some improvements are implemented on the basic algorithms. The proposed MOOAs are examined for three truss optimization problems, and the results are compared to those of some other well-known methods. The numerical results demonstrate that the proposed MOOAs possess better computational performance compared to the other algorithms.

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Grillages are widely used in various structures. In this research, the Colliding Bodies Optimization (CBO) and Enhanced Colliding Bodies Optimization (ECBO) algorithms are used to obtain the optimum design of irregular grillage systems. The purpose of this research is to minimize the weight of the structure while satisfying the design constraints. The design variables are considered to be the cross-sectional properties of the beams and the design constraints are employed from LRFD-AISC. In addition, optimum design of grillages is performed for two cases: (i) without considering the warping effect, and (ii) with considering the warping effect. Also, several examples are presented to show the effect of different spacing and various boundary conditions. Finally, the results show that warping effect, beam spacing and boundary conditions have significant effects on the optimum design of grillages.

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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.