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The main object of this research is to optimize an end-filled castellated beam. In order to support high shear forces close to the connections, sometimes it becomes necessary to fill certain holes in web opening beam. This is done by inserting steel plates and welding from both sides. Optimization of these beams is carried out using three meta-heuristic methods involves CSS, CBO, and CBO-PSO algorithms. To compare the performance of these algorithms, the minimum cost of the beam is taken as the design objective function. Also, in this study, two common types of laterally supported castellated beams are considered as design problems: beams with hexagonal openings and beams with circular openings. A number of design examples are considered to solve in this case. Comparison of the optimal solution of these methods demonstrates that the hexagonal beams have less cost than cellular beams. It is observed that optimization results obtained by the CBO-PSO for more design examples have less cost in comparison to the results of the other methods.

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Over the past decades, several techniques have been employed to improve the applicability of the metaheuristic optimization methods. One of the solutions for improving the capability of metaheuristic methods is the hybrid of algorithms. This study proposes a new optimization algorithm called HPBA which is based on the hybrid of two optimization algorithms; Big Bang-Big Crunch (BB-BC) inspired by the theory of the universe evolution and Artificial Physics Optimization (APO) which is a physical base optimization method. Finally, the performance of the proposed optimization method is compared with the originated methods. Moreover, the performance of the proposed algorithm is evaluated for truss optimization as an applied constrained optimization problem.

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In decentralized construction projects, costs are mostly related to investment, material, holding, logistics, and other minor costs for implementation. For this reason, simultaneous planning of these items and appropriate scheduling of activities can significantly reduce the total costs of the project undertaken. This paper investigates the decentralized multiple construction projects scheduling problem with the aim of minimizing 1) the completion time of the construction projects and 2) the costs of project implementation. Initially, a bi-objective integer programming model is proposed which can solve small-size problems using the method. Then, a Priority Heuristic Algorithm (PHA), Non-dominate Sorting Artificial Bee Colony (NSABC) and Non-dominate Sorting Genetic Algorithm II (NSGA-II) are developed to handle large-size problems using a modified version of Parallel Schedule Generation Scheme (PSGS). The computational investigations significantly reveal the performance of the proposed heuristic methods over exact ones. Finally, the proposed methods are ranked using TOPSIS approach and metric definition. The results show that NSGA-II-100 (NSGA-II with 100 iterations), NSABC-100 (NSABC with 100 iterations) and PHA are ranked as the best known solution methods, respectively.