Discrete Size and Shape Optimization of Truss Structures Based on Job Search Inspired Strategy and Genetic Operations

A meta-heuristic algorithm for discrete size and shape optimization of trusses via a job search inspired strategy together with genetic operators of mutation, selection, and crossover is proposed. The alternation of movements with respect to objective function and load bearing capacity of constructive decisions is provided. Being introduced is an intermediate search goal connected in terms of posed limitations with heightened suitability levels of individuals meeting the current requirements for the initial objective function. As soon as these conditions allow achieving a structure type which meets task limitations, requirements for the function value are redefined. This technique does not demand penalty functions that provide strict control of limitations in any algorithm usage, greater stability of the results received, and finding better solutions. The efficiency of this approach in terms of solution accuracy is demonstrated through five benchmark design examples, in comparison with other methods of discrete truss structure optimization

Size/Layout Optimization of Truss Structures Using Vibrating Particles System Meta-heuristic Algorithm and its Improved Version

Vibrating Particles System (VPS) optimization is a newly made meta-heuristic algorithm to optimize problems by inspiration of the free vibration of viscous-damped systems with single degree of freedom. The agents are modeled as particles which systematically proceed toward their equilibrium conditions that are reached by the existing population and historically best position. To enhance the performance of the VPS algorithm, Enhanced Vibrating Particles System (EVPS) applies a new process for updating agent’s positions. This paper tries to improve the EVPS algorithm with the aim of reduction in the regulatory parameters’ effect on the algorithm's performance by reducing the regulatory parameters. To evaluate the performance of the proposed method, it is applied to four optimization problems of truss structures including mixed of discrete and continuous design search spaces with displacement, stress and buckling constraints. As a result, the proposed algorithm is a suitable method and more research can be done on it

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

Optimal Design of Pitched Roof Rigid Frames with Non-Prismatic Members Using Quantum Evolutionary Algorithm

The weight and shape of the gable and multi-span frames (mono and two-span pitched roof) with tapered members, as a familiar group of the pitched roof frames, are highly dependent on the properties of the member cross-section. In this work a quantum inspired evolutionary algorithms, so-called Quantum evolutionary algorithm (QEA) [1], are utilized for optimal design of one gable frame and a multi-span frame in five alternatives with tapered members. In order to optimize the frames, the design is performed using the AISC specifications for stress, displacement and stability constraints. The design constraints and weight of the gable and multi-span frames are computed from the cross-section of members. These optimum weights are obtained using aforementioned optimization algorithm considering the cross-section of members and design constraints as optimization variables and constraints, respectively. A comparative study of the QEA and some recently developed methods from literature is also performed to illustrate the performance of the utilized optimization algorithm and its featuring. Furthermore, optimal design of a multi-span frame is compared with the solution of other methods including the same conditions and constraints. This study indicates the power of QEA in exploring and exploitation due the search space with using Q-gate and binary code for individual representation and updating. Binary code helps the QEA to find optimal solution even with minimum number of Q-bit individuals. High speed of this method is because of such a feature

Chaotic coyote algorithm applied to truss optimization problems

The optimization of truss structures is a complex computing problem with many local minima, while metaheuristics are naturally suited to deal with multimodal problems without the need of gradient information. The Coyote Optimization Algorithm (COA) is a population-based nature-inspired metaheuristic of the swarm intelligence field for global optimization that considers the social relations of the coyote proposed to single-objective optimization. Unlike most widespread algorithms, its population is subdivided in packs and the internal social influences are designed. The COA requires a few control hyperparameters including the number of packs, the population size, and the number maximum of generations. In this paper, a modified COA (MCOA) approach based on chaotic sequences generated by Tinkerbell map to scatter and association probabilities tuning and an adaptive procedure of updating parameters related to social condition is proposed. It is then validated by four benchmark problems of structures optimization including planar 52-bar truss, spatial 72-bar truss, 120-bar dome truss and planar 200 bar-truss with discrete design variables and focus in minimization of the structure weight under the required constraints. Simulation results collected in the mentioned problems demonstrate that the proposed MCOA presented competitive solutions when compared with other state-of-the-art metaheuristic algorithms in terms of results quality

Natural Forest Regeneration Algorithm for Optimum Design of Truss Structures with Continuous and Discrete Variables

In  this  paper  the  recently  developed  nature  inspired  metaheuristic algorithm is utilized for optimum design of truss structures with continuous and discrete variables.  This algorithm is inspired  by  the  natural  process  happening  in  the  forests  with the  rapidly  change  of  environment  and  their  natural  regeneration.   Based  on  this  process  a  simple  powerful  optimization technique is introduced so-called Natural Forest Regeneration (NFR).  Some  well-studied  benchmark  structural  problems  are investigated with both continuous and discrete sizing variables and the results of the NRF are compared to those of some previously developed algorithms

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

CBO and CSS Algorithms for Resource Allocation and Time-Cost Trade-Off

Resource allocation project scheduling problem (RCPSP) has been one of the challenging subjects among researchers in the last decades. Though several methods have been adopted to solve this problem, however, new metahuristics are available to solve this problem for finding better solution with less computational time. In this paper two new metahuristic algorithms are applied for solving this problem known as charged system search (CSS) and colliding body optimization (CBO). The results show that both of these algorithms find reasonable solutions, however CBO could find the result in a less computational time having a better quality. Two case studies are conducted to evaluate the performance and applicability of the proposed algorithms

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