Discrete Optimum Design of Planar Steel Curved Roof and Pitched Roof Portal Frames Using Metaheuristic Algorithms

Portal frames are single-story frame buildings including columns and rafters, and their rafters can be either curved or pitched. These are used widely in the construction of industrial buildings, warehouses, gyms, fire stations, agricultural buildings, hangars, etc. The construction cost of these frames considerably depends on their weight. In the present research, the discrete optimum design of two types of portal frames including planar steel Curved Roof Frame (CRF) and Pitched Roof Frame (PRF) with tapered I-section members are presented. The optimal design aims to minimize the weight of these frame structures while satisfying some design constraints based on the requirements of ANSI/AISC 360-16 and ASCE 7-10. Four population-based metaheuristic optimization algorithms are applied to the optimal design of these frames. These algorithms consist of Teaching-Learning-Based Optimization (TLBO), Enhanced Colliding Bodies Optimization (ECBO), Shuffled Shepherd Optimization Algorithm (SSOA), and Water Strider Algorithm (WSA). Two main objectives are followed in this paper. The first one deals with comparing the optimized weight of the CRF and PRF structures with the same dimensions for height and span in two different span lengths (16.0 m and 32.0 m), and the second one is related to comparing the performance of the considered metaheuristics in the optimum design of these portal frames. The obtained results reveal that CRF is more economical than PRF in the fair comparison. Moreover, comparing the results acquired by SSOA with those of other considered metaheuristics reveals that SSOA has better performance for the optimal design of these portal frames.

Enhanced Colliding Bodies Optimization Towards the Solution of Economic Load Dispatch Problem

This paper proposes application of enhanced colliding bodies optimization algorithm (ECBO) for solving the economic load dispatch problem, which aims to get minimum generation cost through economic scheduling of generating unit. The algorithm is a novel efficient optimization algorithm, and its idea is attained from collisions, which are one-dimensional between two or more bodies. Each agent involved in this collision is shaped as a body having defined mass and defined velocity. To analyze the performance of the proposed method, it is tested on four different test systems consisting of 3, 5, 13, and 18 generating units. Both convex and non-convex fuel cost function have been considered. Numerical results have been compared with various well-known algorithms. A significant improvement in the results has been observed. It has been farther noted that an average 5.1% better results were provided by ECBO in terms of generation cost compared to other algorithms. Moreover, the simulation time is also improved. Besides this, Kruskal-Wallis non-parametric test has been performed.

Chaotic enhanced colliding bodies optimization algorithm for structural reliability analysis

It is of extreme importance to assess the failure probability and safety level of structural system in structural design. Nowadays, many researchers presented several approaches for structural reliability analysis, such as the first-order reliability method, Monte Carlo simulation, and the meta-heuristic algorithm. The meta-heuristic algorithm is not only efficient to solve global optimization problems but also shown to be an effective tool for structural reliability analysis. A recent meta-heuristic optimization approach, enhanced colliding bodies optimization, has emerged as a relatively simple implementation with a fast convergence speed. Chaos theory is characterized by its ergodicity, pseudo-randomness, and irregularity. This article thus presents a novel approach introducing chaotic maps into the enhanced colliding bodies optimization algorithm to promote the performance of convergence, named as chaotic enhanced colliding bodies optimization algorithm. The proposed algorithm uses chaotic maps to change the generation pattern of particles and improve convergence characteristics. A procedure based on the effective use of the represented chaotic enhanced colliding bodies optimization is then applied in structural reliability analysis. A variety of numerical and structural problems are tested in this article to demonstrate that the given method actually improves the performance of enhanced colliding bodies optimization in convergence as well as the accuracy for reliability analysis compared with the other methods existing in the literature.

Optimization of Tower Crane Location and Material Quantity Between Supply and Demand Points: A Comparative Study

Location optimization of tower crane as an expensive equipment in the construction projects has an important effect on material transportation costs. Due to the construction site conditions, there are several tower crane location optimization models. Appropriate location of tower cranes for material supply and engineering demands is a combinatorial optimization problem within the tower crane layout problem that is difficult to resolve. Meta-heuristics are popular and useful techniques to resolve complex optimization problems. In this paper, the performance of the Particle Swarm Optimization (PSO) and four newly developed meta-heuristic algorithms Colliding Bodies Optimization (CBO), Enhanced Colliding Bodies Optimization (ECBO), Vibrating Particles System (VPS), and Enhanced Vibrating Particles System (EVPS) are compared in terms of their effectiveness in resolving a practical Tower Crane Layout (TCL) problem. Results show that ECBO performs better than other three methods in both cases.

Modified Dolphin Monitoring Operator for Weight Optimization of Frame Structures

In this article, a modified dolphin monitoring (MDM) operatoris introduced and used to improve the performance of the collidingbodies optimization (CBO) algorithm for optimal designof steel structures (CBO-MDM). The performance of the CBO,enhanced colliding bodies optimization (ECBO) and CBOMDMare compared through three well-established structuralbenchmarks. The optimized designs obtained by thesealgorithms are compared, and the results show that the performanceof CBO-MDM is superior to those of the other twoalgorithms. The MDM is found to be a suitable tool to enhancethe performance of the CBO algorithm.