International Journal of Optimization in Civil Engineering
Volume:11 Issue: 2, Spring 2021

The main purpose of the present work is to investigate the impact of soil-structure interaction on performance-based design optimization of steel moment resisting frame (MRF) structures. To this end, the seismic performance of optimally designed MRFs with rigid supports is compared with that of the optimal designs with a flexible base in the context of performance-based design. Two efficient metaheuristic algorithms, namely center of mass optimization and improved fireworks, are used to implement the optimization task. During the optimization process, nonlinear structural response-history analysis is carried out to evaluate the structural response. Two illustrative design examples of 6- and 12-story steel MRFs are presented, and it is observed that the performance-based design optimization considering soil-structure interaction decreases the structural weight and increases nonlinear structural response in comparison to rigid-based models. Therefore, in order to obtain more realistic optimal designs, soil-structure interaction should be included in the performance-based design optimization process of steel MRFs.

In this work, a new hybrid Symbiotic Organisms Search (SOS) algorithm introduced to design and optimize spatial and planar structures under structural constraints. The SOS algorithm is inspired by the interactive behavior between organisms to propagate in nature. But one of the disadvantages of the SOS algorithm is that due to its vast search space and a large number of organisms, it may trap in a local optimum. To fix this problem Harmony search (HS) algorithm, which has a high exploration and high exploitation, is applied as a complement to the SOS algorithm. The weight of the structureschr('39') elements is the objective function which minimized under displacement and stress constraints using finite element analysis. To prove the high capabilities of the new algorithm several spatial and planar benchmark truss structures, designed and optimized and the results have been compared with those of other researchers. The results show that the new algorithm has performed better in both exploitation and exploration than other meta-heuristic and mathematics methods.

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.

A modified dolphin monitoring (MDM) is used to augment the efficiency of particle swarm optimization (PSO) and enhanced vibrating particle system (EVPS) for the numerical crack identification problems in plate structures. The extended finite element method (XFEM) is employed for modeling the fracture. The forward problem is untangled by some cycle loading phase via dynamic XFEM. Furthermore, the inverse problem is solved and compared via two PSO and EVPS algorithms. All the problems are also dissolved by means of fine and coarse meshing. The results illustrate that the function of XFEM-PSO-MDM and XFEM-EVPS-MDM is superior to XFEM-PSO and XFEM-EVPS methods. The algorithms coupled via MDM offer a higher convergence rate with more reliable results. The MDM is found to be a suitable tool which can promotes the ability of the algorithms in achieving the optimum solutions.

The mass matrix formulation is very important to achieve a high-convergent model in structural dynamics. This study calculates the optimum mass matrix for in-plane free vibrations of the plane problems. In fact, the parameterized mass and stiffness for a rectangular element are formulated by the template approach. By using perturbation theory and sensitivity analysis, the changes of the natural frequencies are obtained as a function of the free parameter variations. Based on the natural frequencies, the objective function is established. Through an optimization process, the optimum values for template-free parameters are determined. Findings are used to calculate the plane problems’ natural frequencies. Some structural analyses and comparative studies with the other schemes are performed. Base on the obtained results, the efficiencies and high-convergence properties of the optimal element are demonstrated by numerical examples.

An ensemble method is introduced to solve optimization problems efficiently. The method is mainly based on using the gradient directions along which, the function is reduced at most. Large step sizes are employed for exploration in the first phase. The use of smaller step sizes in subsequence phases will allow for more accurate exploration. To increase the efficiency of the gradient techniques, some enhancements such as mutation, crossover and fly-back operations are introduced to explore the entire design space. The efficiency and the reliability of the multi-phase gradient approach are examined by solving 29 complicated multimodal functions introduced in CEC 2017 and a structural shape optimization problem under frequency constraints. The results are compared with several well-known population-based algorithms.

This article presents a new meta-heuristic optimization algorithm based on the power of human thinking and decision-making, which will be called Gold Rush Optimization (GRO). The thinking and decision-making ability of humans were used in this paper to develop a approach to create an optimization method. The hypothetical interaction between human operators in search of gold, based on the sound volume received from metal detectors, was used to develop the method. Benchmark functions, engineering design examples, and truss structures (which were optimized using different algorithms previously) were used for validation and verification of the proposed algorithm. MATLAB was used for programming. The CEC 2005 benchmark functions obtained reached the global target minimum, and the numerical engineering and truss examples were improved compared to the previous algorithms. Therefore, the proposed algorithm can be used as an alternative for the previously developed meta-heuristic optimization algorithms, which can be used in all optimization fields.

Jellyfish Search (JS) is a recently developed population-based metaheuristic inspired by the food-finding behavior of jellyfish in the ocean. The purpose of this paper is to propose a quantum-based Jellyfish Search algorithm, named Quantum JS (QJS), for solving structural optimization problems. Compared to the classical JS, three main improvements are made in the proposed QJS: (1) a quantum-based update rule is adopted to encourage the diversification in the search space, (2) a new boundary handling mechanism is used to avoid getting trapped in local optima, and (3) modifications of the time control mechanism are added to strike a better balance between global and local searches. The proposed QJS is applied to solve frequency-constrained large-scale cyclic symmetric dome optimization problems. To the best of our knowledge, this is the first time that JS is applied in frequencyconstrained optimization problems. An efficient eigensolution method for free vibration analysis of rotationally repetitive structures is employed to perform structural analyses required in the optimization process. The efficient eigensolution method leads to a considerable saving in computational time as compared to the existing classical eigensolution method. Numerical results confirm that the proposed QJS considerably outperforms the classical JS and has superior or comparable performance to other state-ofthe-art optimization algorithms. Moreover, it is shown that the present eigensolution method significantly reduces the required computational time of the optimization process compared to the classical eigensolution method.