Multiobjective adaptive symbiotic organisms search for truss optimization problems

This paper presents a multiobjective adaptive symbiotic organisms search (MOASOS) and its two-archive technique for solving truss optimization problems. The SOS algorithm considers the symbiotic relationship among various species, such as mutualism, commensalism, and parasitism, to live in nature. The heuristic characteristics of the mutualism phase permits the search to jump into not visited sections (named an exploration) and allows a local search of visited sections (named an exploitation) of the search region. As search progresses, a good balance between an exploration and exploitation has a greater impact on the solutions. Thus, adaptive control is now incorporated to propose MOASOS. In addition, two-archive approach is applied in MOASOS to maintain population diversity which is a major issue in multiobjective meta-heuristics. For the design problems, minimization of the truss� mass and maximization of nodal displacement are objectives whereas elemental stress and discrete cross-sectional areas are assumed to be behaviour and side constraints respectively. The usefulness of these methods to solve complex problems is validated by five truss problems (i.e. 10-bar truss, 25-bar truss, 60-bar truss, 72-bar truss, and 942-bar truss) with discrete design variables. The results of the proposed algorithms have demonstrated that adaptive control is able to provide a better and competitive solutions when compared against the previous studies

Symbiotic Organisms Search Algorithm: theory, recent advances and applications

The symbiotic organisms search algorithm is a very promising recent metaheuristic algorithm. It has received a plethora of attention from all areas of numerical optimization research, as well as engineering design practices. it has since undergone several modifications, either in the form of hybridization or as some other improved variants of the original algorithm. However, despite all the remarkable achievements and rapidly expanding body of literature regarding the symbiotic organisms search algorithm within its short appearance in the field of swarm intelligence optimization techniques, there has been no collective and comprehensive study on the success of the various implementations of this algorithm. As a way forward, this paper provides an overview of the research conducted on symbiotic organisms search algorithms from inception to the time of writing, in the form of details of various application scenarios with variants and hybrid implementations, and suggestions for future research directions

Using Biological Knowledge for Layout Optimization of Construction Site Temporary Facilities: A Case Study

In recent years, a number of studies have successfully transformed various models for biological collective behavior into intelligent optimization algorithms. These bio-inspired optimization techniques have been developed to provide better solutions than traditional methods to a variety of engineering problems. This paper attempts to apply and compare recent bio-inspired algorithms for determining the best layout of construction temporary facilities. To validate the performance of the proposed techniques, an actual building construction project was used as a test problem. Based on the obtained results, the performance of each bio-inspired algorithm is highlighted and discussed. This paper presents beneficial insights to decision-makers in the construction industry that are involved in handling optimization problems

Enhanced Artificial Coronary Circulation System Algorithm for Truss Optimization with Multiple Natural Frequency Constraints

In this paper, an enhanced artificial coronary circulation system (EACCS) algorithm is applied to structural optimization with continuous design variables and frequency constraints. The standard algorithm, artificial coronary circulation system (ACCS), is inspired biologically as a non-gradient algorithm and mimics the growth of coronary tree of heart circulation system. Designs generated by the EACCS algorithm are compared with other popular evolutionary optimization methods, the objective function being the total weight of the structures.Truss optimization with frequency constraints has attracted substantial attention to improve the dynamic performance of structures. This kind of problems is believed to represent nonlinear and non-convex search spaces with several local optima. These problems are also suitable for examining the capabilities of the new algorithms. Here, ACCS is enhanced (EACCS) and employed for size and shape optimization of truss structures and six truss design problems are utilized for evaluating and validating of the EACCS. This algorithm uses a fitness-based weighted mean in the bifurcation phase and runner phase of the optimization process. The numerical results demonstrate successful performance, efficiency and robustness of the new method and its competitive performance to some other well-known meta-heuristics in structural optimization

Spontaneous Fruit Fly Optimisation for truss weight minimisation:Performance evaluation based on the no free lunch theorem

Over the past decade, several researchers have presented various optimisation algorithms for use in truss design. The no free lunch theorem implies that no optimisation algorithm fits all problems; therefore, the interest is not only in the accuracy and convergence rate of the algorithm but also the tuning effort and population size required for achieving the optimal result. The latter is particularly crucial for computationally intensive or high-dimensional problems. Contrast-based Fruit-fly Optimisation Algorithm (c-FOA) proposed by Kanarachos et al. in 2017 is based on the efficiency of fruit flies in food foraging by olfaction and visual contrast. The proposed Spontaneous Fruit Fly Optimisation (s-FOA) enhances c-FOA and addresses the difficulty in solving nonlinear optimisation algorithms by presenting standard parameters and lean population size for use on all optimisation problems. Six benchmark problems were studied to assess the performance of s-FOA. A comparison of the results obtained from documented literature and other investigated techniques demonstrates the competence and robustness of the algorithm in truss optimisation.Comment: Presented at the International conference for sustainable materials, energy and technologies, 201

Combining machine learning models via adaptive ensemble weighting for prediction of shear capacity of reinforced‑concrete deep beams

This study presents a novel artificial intelligence (AI) technique based on two support vector machine (SVM) models and symbiotic organisms search (SOS) algorithm, called “optimized support vector machines with adaptive ensemble weight- ing” (OSVM-AEW), to predict the shear capacity of reinforced-concrete (RC) deep beams. This ensemble learning-based system combines two supervised learning models—the support vector machine (SVM) and least-squares support vector machine (LS-SVM)—with the SOS optimization algorithm as the optimizer. In OSVM-AEW, SOS is integrated to simulta- neously select the optimal parameters of SVM and LS-SVM, and control the coordination process of the learning outputs. Experimental results show that OSVM-AEW achieves the greatest evaluation criteria for coefficient of correlation (0.9620), coefficient of determination (0.9254), mean absolute error (0.3854 MPa), mean absolute percentage error (7.68%), and root- mean-squared error (0.5265 MPa). This paper demonstrates the successful application of OSVM-AEW as an efficient tool for helping structural engineers in the RC deep beams design process

Development of frame finite element model for truss structures with semi-rigid connections

The problem of connecting truss structures is one of the major concerns in structural analysis and design. The behavior of truss structures is usually analyzed using a common finite element model, which considers each member as a two-force member. Each truss member connection is treated as a rotational pinned joint, but in the reality, the members of truss structures are usually connected with bolts or by welding. Alternatively, a designer may analyze such a structure using a frame finite element model where joint connections are considered fixed or rigid connections, which provide a connection that is stiffer than the inherent behavior. In this research, instead of using truss or frame finite element models, a substructure technique is employed to develop a more realistic finite element model. Each element is separated into three parts, a main element and two joint elements. The substructure technique is integrated into the frame finite element model to reduce design variables in global equations, to increase deformability of the joint elements, and make the proposed model more realistic. Young’s modulus values of the joints are reduced as a percentage of the modulus of the main elements. Comparison of the results obtained from the proposed model to the truss and frame finite element models are reported

Chaotically Enhanced Meta-Heuristic Algorithms for Optimal Design of Truss Structures with Frequency Constraints

The natural frequencies of any structure contain useful information about the dynamic behavior of that structure, and by controlling these frequencies, the destructive effects of dynamic loads, including the resonance phenomenon, can be minimized. Truss optimization by applying dynamic constraints has been widely welcomed by researchers in recent decades and has been presented as a challenging topic. The main reason for this choice is quick access to dynamic information by examining natural frequencies. Also, frequency constraint relations are highly nonlinear and non-convex and have implicit variables, so using mathematical and derivative methods will be very difficult and time consuming. In this regard, the use of meta-heuristic algorithms in truss weight optimization with frequency constraints has good results, but with the introduction of form variables, these algorithms trap at local optima. In this research, by applying chaos map in meta-heuristic algorithms, suitable conditions have been provided to escape from local optima and access to global optimums. These algorithms include Chaotic Cyclical Parthenogenesis Algorithms (CCPA), Chaotic Biogeography-Based Optimization (CBBO), Chaotic Teaching-Learning-Based Optimization (CTLBO) and Chaotic Particle Swarm Optimization (CPSO), respectively. Also, by using different scenarios, a good balance has been achieved between the exploration and exploitation of the algorithms

Weight optimization of steel lattice transmission towers based on Differential Evolution and machine learning classification technique

Transmission towers are tall structures used to support overhead power lines. They play an important role in the electrical grids. There are several types of transmission towers in which lattice towers are the most common type. Designing steel lattice transmission towers is a challenging task for structural engineers due to a large number of members. Therefore, discovering effective ways to design lattice towers has attracted the interest of researchers. This paper presents a method that integrates Differential Evolution (DE), a powerful optimization algorithm, and a machine learning classification model to minimize the weight of steel lattice towers. A classification model based on the Adaptive Boosting algorithm is developed in order to eliminate unpromising candidates during the optimization process. A feature handling technique is also introduced to improve the model quality. An illustrated example of a 160-bar tower is conducted to demonstrate the efficiency of the proposed method. The results show that the application of the Adaptive Boosting model saves about 38% of the structural analyses. As a result, the proposed method is 1.5 times faster than the original DE algorithm. In comparison with other algorithms, the proposed method obtains the same optimal weight with the least number of structural analyses

An Adaptive Fuzzy Symbiotic Organisms Search Algorithm and Its Applications

This paper discusses the development of a Symbiotic Organisms Search Algorithm (SOS) variant, called Adaptive Fuzzy SOS (FSOS). Like SOS, FSOS exploits three types of symbiosis operators namely mutualism, commensalism, and parasitism in order to undertake the search process. Unlike SOS, FSOS is able to adaptively select a single or any combination of mutualism, commensalism, and parasitism update operator(s) as the search progresses based on the current search status controlled by their individual probabilities via the fuzzy decision-making. To validate its performance, we have evaluated FSOS to solve 23 benchmark functions and take a t-way test generation as our case study. Experimental results demonstrate that FSOS exhibits competitive performance against its predecessor (SOS) and other competing metaheuristic algorithms