Member sizing optimization of large scale steel space trusses using a symbiotic organisms search algorithm

A systematic approach of optimization is needed to achieve an optimal design of large and complex truss structures. In the last three decades, many researchers have developed and applied various metaheuristic optimization methods to the design of truss structures. This paper investigates a new metaheuristic algorithm called symbiotic organisms search (SOS) for member sizing optimization of relatively large steel trusses. The case studies include a 120-bar dome truss, a 160-bar pyramid truss, and a 942-bar tower truss. The structural analyses are carried out using the standard finite element method. The profile of the members is circular hollow structural sections selected from a set of the AISC standard profiles. The design results using the SOS are then compared to those obtained using other metaheuristic methods, namely the particle swarm optimization, differential evolution, and teaching-learning-based optimization. The comparison shows the superior performance of the SOS in terms of both the optimal solution and consistency. Thus, the SOS is a good alternative for optimizing the design of steel truss structures in real engineering practice

Biological System Behaviours and Natural-inspired Methods and Their Applications to Supply Chain Management

People have learnt from biological system behaviours and structures to design and develop a number of different kinds of optimisation algorithms that have been widely used in both theoretical study and practical applications in engineering and business management. An efficient supply chain is very important for companies to survive in global competitive market. An effective SCM (supply chain management) is the key for implement an efficient supply chain. Though there have been considerable amount of study of SCM, there have been very limited publications of applying the findings from the biological system study into SCM. In this paper, through systematic literature review, various SCM issues and requirements are discussed and some typical biological system behaviours and natural-inspired algorithms are evaluated for the purpose of SCM. Then the principle and possibility are presented on how to learn the biological systems' behaviours and natural-inspired algorithms for SCM and a framework is proposed as a guide line for users to apply the knowledge learnt from the biological systems for SCM. In the framework, a number of the procedures have been presented for using XML to represent both SCM requirement and bio-inspiration data. To demonstrate the proposed framework, a case study has been presented for users to find the bio-inspirations for some particular SCM problems in automotive industry

A Coevolutionary Particle Swarm Algorithm for Bi-Level Variational Inequalities: Applications to Competition in Highway Transportation Networks

A climate of increasing deregulation in traditional highway transportation, where the private sector has an expanded role in the provision of traditional transportation services, provides a background for practical policy issues to be investigated. One of the key issues of interest, and the focus of this chapter, would be the equilibrium decision variables offered by participants in this market. By assuming that the private sector participants play a Nash game, the above problem can be described as a Bi-Level Variational Inequality (BLVI). Our problem differs from the classical Cournot-Nash game because each and every player’s actions is constrained by another variational inequality describing the equilibrium route choice of users on the network. In this chapter, we discuss this BLVI and suggest a heuristic coevolutionary particle swarm algorithm for its resolution. Our proposed algorithm is subsequently tested on example problems drawn from the literature. The numerical experiments suggest that the proposed algorithm is a viable solution method for this problem

Size, Topology, and Shape Optimization of Truss Structures using Symbiotic Organisms Search

Truss structures are common in the building industry. One way to contain construction costs is to implement structural optimization. Optimization has to consider cross-sectional size, area, topology, and node coordinates as design variables. However, each truss structure has numerous complex constraints and variables that make optimizing this structure complex and difficult. The metaheuristic method is efficient and effective in solving large and complex problems. This paper tested three metaheuristic algorithms: particle swarm optimization (PSO), differential evolution (DE), and symbiotic organisms search (SOS). Each algorithm was used to optimize a 10-bar planar truss structure and a 15-bar planar truss structure. SOS was found to have the best optimization results, convergence behavior, and consistency

An efficient chameleon swarm algorithm for economic load dispatch problem

Economic Load Dispatch (ELD) is a complicated and demanding problem for power engineers. ELD relates to the minimization of the economic cost of production, thereby allocating the produced power by each unit in the most possible economic manner. In recent years, emphasis has been laid on minimization of emissions, in addition to cost, resulting in the Combined Economic and Emission Dispatch (CEED) problem. The solutions of the ELD and CEED problems are mostly dominated by metaheuristics. The performance of the Chameleon Swarm Algorithm (CSA) for solving the ELD problem was tested in this work. CSA mimics the hunting and food searching mechanism of chameleons. This algorithm takes into account the dynamics of food hunting of the chameleon on trees, deserts, and near swamps. The performance of the aforementioned algorithm was compared with a number of advanced algorithms in solving the ELD and CEED problems, such as Sine Cosine Algorithm (SCA), Grey Wolf Optimization (GWO), and Earth Worm Algorithm (EWA). The simulated results established the efficacy of the proposed CSA algorithm. The power mismatch factor is the main item in ELD problems. The best value of this factor must tend to nearly zero. The CSA algorithm achieves the best power mismatch values of 3.16×10−13, 4.16×10−12 and 1.28×10−12 for demand loads of 700, 1000, and 1200 MW, respectively, of the ELD problem. The CSA algorithm achieves the best power mismatch values of 6.41×10−13 , 8.92×10−13 and 1.68×10−12 for demand loads of 700, 1000, and 1200 MW, respectively, of the CEED problem. Thus, the CSA algorithm was found to be superior to the algorithms compared in this work

Comprehensive Taxonomies of Nature- and Bio-inspired Optimization: Inspiration Versus Algorithmic Behavior, Critical Analysis Recommendations

In recent algorithmic family simulates different biological processes observed in Nature in order to efficiently address complex optimization problems. In the last years the number of bio-inspired optimization approaches in literature has grown considerably, reaching unprecedented levels that dark the future prospects of this field of research. This paper addresses this problem by proposing two comprehensive, principle-based taxonomies that allow researchers to organize existing and future algorithmic developments into well-defined categories, considering two different criteria: the source of inspiration and the behavior of each algorithm. Using these taxonomies we review more than three hundred publications dealing with nature- inspired and bio-inspired algorithms, and proposals falling within each of these categories are examined, leading to a critical summary of design trends and similarities between them, and the identification of the most similar classical algorithm for each reviewed paper. From our analysis we conclude that a poor relationship is often found between the natural inspiration of an algorithm and its behavior. Furthermore, similarities in terms of behavior between different algorithms are greater than what is claimed in their public disclosure: specifically, we show that more than one-third of the reviewed bio-inspired solvers are versions of classical algorithms. Grounded on the conclusions of our critical analysis, we give several recommendations and points of improvement for better methodological practices in this active and growing research field

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

Biomimetic Engineering

Humankind is a privileged animal species for many reasons. A remarkable one is its ability to conceive and manufacture objects. Human industry is indeed leading the various winning strategies (along with language and culture) that has permitted this primate to extraordinarily increase its life expectancy and proliferation rate. (It is indeed so successful, that it now threatens the whole planet.) The design of this industry kicks off in the brain, a computing machine particularly good at storing, recognizing and associating patterns. Even in a time when human beings tend to populate non-natural, man-made environments, the many forms, colorings, textures and behaviors of nature continuously excite our senses and blend in our thoughts, even more deeply during childhood. Then, it would be exaggerated to say that Biomimetics is a brand new strategy. As long as human creation is based on previously acquired knowledge and experiences, it is not surprising that engineering, the arts, and any form of expression, is influenced by nature’s way to some extent. The design of human industry has evolved from very simple tools, to complex engineering devices. Nature has always provided us with a rich catalog of excellent materials and inspiring designs. Now, equipped with new machinery and techniques, we look again at Nature. We aim at mimicking not only its best products, but also its design principles. Organic life, as we know it, is indeed a vast pool of diversity. Living matter inhabits almost every corner of the terrestrial ecosphere. From warm open-air ecosystems to the extreme conditions of hot salt ponds, living cells have found ways to metabolize the sources of energy, and get organized in complex organisms of specialized tissues and organs that adapt themselves to the environment, and can modify the environment to their own needs as well. Life on Earth has evolved such a diverse portfolio of species that the number of designs, mechanisms and strategies that can actually be abstracted is astonishing. As August Krogh put it: "For a large number of problems there will be some animal of choice, on which it can be most conveniently studied". The scientific method starts with a meticulous observation of natural phenomena, and humans are particularly good at that game. In principle, the aim of science is to understand the physical world, but an observer’s mind can behave either as an engineer or as a scientist. The minute examination of the many living forms that surround us has led to the understanding of new organizational principles, some of which can be imported in our production processes. In practice, bio-inspiration can arise at very different levels of observation: be it social organization, the shape of an organism, the structure and functioning of organs, tissular composition, cellular form and behavior, or the detailed structure of molecules. Our direct experience of the wide portfolio of species found in nature, and their particular organs, have clearly favored that the initial models would come from the organism and organ levels. But the development of new techniques (on one hand to observe the micro- and nanostructure of living beings, and on the other to simulate the complex behavior of social communities) have significantly extended the domain of interest