Optimum Design of truss structures considering nonlinear analysis and dynamic loading using metaheuristic algorithms

This paper discusses the effect of global stability on the optimal size and shape of truss structures taking into account of a nonlinear critical load, truss weight and serviceability at the same time. The nonlinear critical load is computed by arc-length method. In order to increase the accuracy of the estimation of critical load (ignoring material nonlinearity), an eigenvalue analysis is implemented into the arc-length method. Furthermore, a pure pareto-ranking based multi-objective optimization model is employed for the design optimization of the truss structure with multiple objectives. The computational performance of the optimization model is increased by implementing an island model into its evolutionary search mechanism. The proposed design optimization approach is applied for both size and shape optimization of real world trusses including 101, 224 and 444 bars and successful in generating feasible designations in a large and complex design space. It is observed that the computational performance of pareto-ranking based island model is better than the pure pareto-ranking based model. Therefore, pareto-ranking based island model is recommended to optimize the design of truss structure possessing geometric nonlinearity.

Size optimization of space trusses using Big Bang–Big Crunch algorithm

In recent decades, due to the limitations of resources and equipment in the construction of engineering projects, the optimal use of existing facilities for reducing the overall costs of the projects has been of great importance. Due to the significant advances in optimization techniques, the application of these methods in engineering sciences, especially civil and structural engineering, has received more attention than before. In the last years, various optimization methods and algorithms have been used for optimization purposes, including metaheuristic algorithms. In this paper, the optimum design of truss structures is considered utilizing Atomic Orbital Search (AOS) as one of the recently developed metaheuristic algorithms. The applicability of this technique in the weight optimization of complex truss structures is in perspective. Four of the benchmark problems in the truss optimization field, such as 10-bar, 25-bar, 52-bar, and 160-bar truss structures, are used as design examples. A simple penalty approach is implemented in AOS as a constraint handling scheme, while a comprehensive statistical analysis is also conducted through multiple optimization runs. The results of the AOS are compared to the results of other methods from the literature, while the AOS demonstrates a good level of weight reduction capability in dealing with these complex design problems.KeywordsTruss structureAtomic orbital searchMetaheuristic algorithmOptimization first section

Wind resistant size optimization of geometrically nonlinear lattice structures using a modified optimality criterion method

The research and development of metaheuristic methods are critical issues in computer science. In the past decade, metaheuristic algorithms have been used in many engineering applications such as optimization of engineering problems, telecommunications, information security, and image processing. Many metaheuristic algorithms such as particle swarm optimization (PSO), ant colony optimization (ACO), and genetic algorithm (GA) are recently becoming very popular.There are many studies conducted in the literature on the comparison of PSO with other metaheuristic algorithms. In this chapter, various studies carried out between the years of 2010 and 2020 about the comparison of PSO with the other metaheuristic algorithms will be examined. The metaheuristic algorithms to be considered are simulated annealing (SA), genetic algorithm (GA), differential evolution (DE), ant colony optimization (ACO), artificial bee colony (ABC) algorithm, particle swarm optimization (PSO), tabu search (TS), harmony search (HS), firefly algorithm (FF), cuckoo search (CS), bat-inspired algorithm (BA), water wave optimization (WWO), clonal selection algorithm (CLONALG), chemical reaction optimization (CRO), sine cosine algorithm (SCA), glowworm swarm optimization (GSO), and grey wolf optimizer (GWO). This study aims to evaluate and analyze the covered papers according to several criteria such as (a) rates of studies according to publishing years, (b) the metaheuristic algorithms that are compared to PSO, (c) performance evaluation of compared algorithms, (d) the metaheuristic algorithms with their inspirational approaches and their initial proposed studies and years, (e) the field of subjects where the algorithms are applied in the reviewed studies, and (f) used databases in the examined studies.This study is a comprehensive literature review of the comparison of PSO with the most popular metaheuristic algorithms. The intention of this review is to be useful for researchers who want to conduct a survey on this area of the subject as this chapter will cover the essential and helpful analysis of the related research.

The fast development of electric vehicles (EVs) has resulted in several topics of research in this area, such as the development of a charging pricing strategy, charging control, location of the charging station, and the structure within the charging station. This paper proposes the optimal design of the structure of an EV fast-charging station (EVFCS) connected with a renewable energy source and battery energy storage systems (BESS) by using metaheuristic algorithms. The optimal design of this structure aims to find the number and power of chargers. Moreover, the renewable energy source and BESS can reduce the impact on the grid, so these energy sources are considered as ones of the optimally-designed structure of EVFCS in this work. Thus, it is necessary to determine the optimal sizing of the renewable energy source, BESS, and the grid power connected to EVFCS. This optimal structure can improve the profitability of the station. To solve the optimization problem, three metaheuristic algorithms, including particle swarm optimization (PSO), Salp swarm algorithm (SSA), and arithmetic optimization algorithm (AOA), are adopted. These algorithms aim to find the optimal structure which maximizes the profit of the EVFCS determined by its net present value (NPV), and the results obtained from these algorithms were compared. The results demonstrate that all considered algorithms could find the feasible solutions of the optimal design of the EVFCS structure where PSO provided the best NPV, followed by AOA and SSA.

ABSTRACTMeta heuristic algorithms have been introduced as a powerful method to solve the nonlinear optimization problems. These algorithms have been employed in many complex engineering problems due to their high capability in finding the solutions and reaching the optimal results within a short period of time. Optimization of distributed generation units in distribution systems, which have profoundly impacted on the system losses and voltage profile, is one of these nonlinear problems. In this study, a novel objective function was proposed for optimization procedure by meta-heuristic algorithms. The related objective function consists of the total cost of distributed generation units, cost of the purchased natural gas, cost of distribution system power losses, and penalty for greenhouse gas emissions. The electrical, cooling, and heating loads were considered in this study. In the distribution system, the waste and fuel cell were used to supply the required heating and cooling loads. The meta-heuristic algorithms including Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Imperialist Competitive Algorithm (ICA) were employed to find the optimal location and size of distributed generation units in a distribution system. A detailed performance analysis was done on 13 bus radial distribution system. The performances of three algorithms were compared with each other and results showed that the PSO was the fastest; and had the best solution and optimum results. Furthermore, the PSO reached the optimum solution in a fewer number of iterations than the GA and ICA algorithms.

The genetic algorithm(GA) is efficient method for topological optimization problem of truss structures because it is possible to obtain global optimum on non-convex design space. The bottleneck of this method is that a large number of function evaluations and structural analyses are necessarily. Performance of the GA depends on first arrangement of string positions and the crossover operator strongly when the simple GA apply to the topological optimization problems. If inadequate correspondence between design variables and the string positions is used, good solution is hardly obtained in spite of numerous calculations. The objective of this study is development a new genetic algorithm specialized to the topological optimization problem of the truss structure improving the calculation efficiency and reliability to obtain optimum solution. To overcome disadvantage of the simple GA, biased crossover operation with string position rearrangement is proposed. Highly fitted gene is crated and inherited by using this procedure. Minimum weight design of the truss structure consists of nine nodes are carried out by using simple G A and proposed GA, and calculation performance is compared. From the result, it is confirmed that the proposed GA is more effective and reliable in comparison with the simple GA.

There are two general methods of robust design optimization (RDO) and reliability-based robust design optimization (RBRDO) for the optimal design of structures with non-deterministic variables. In the problem-solving process of both RDO and RBRDO, assessing the probabilistic constraints of the problem and the standard deviation of the structural responses is time-consuming and costly. In this study, by presenting a multi-step approach, for a probable optimal solution, the deterministic constraints are first investigated. Then the limit state functions related to probabilistic constraints are computed based on the mean value of random variables (in the RBRDO problem) and in the step of uncertainties effects simulation on the response of the structure, probabilistic constraints are evaluated (in the RBRDO problem). Designs that not meeting the deterministic constraints or in the unsafe area for the mean values of the random variables are not included in calculating the first and second terms of the objective function and reliability assessment. This leads to reduce the volume of calculations. The Monte Carlo simulation method is used to assess the probabilistic constraints, and the EVPS metaheuristic algorithm is used for the optimization process. Three benchmark trusses of 10, 25, and 72 bars were studied to assess the efficiency of the proposed approach. The first objective function in these problems was the mean weight of trusses and the second objective function was the standard deviation of the displacement of a specific node in a certain direction.

Comparison of nine meta-heuristic algorithms for optimal design of truss structures with frequency constraints

Procedural Content Generation (PCG) has emerged as a powerful approach for automating game content creation, offering significant benefits in terms of cost reduction and time efficiency compared to traditional game design and development processes (Zhang, Zhang, & Huang, 2022). While Genetic Algorithms (GAs) have been widely used in PCG, alternative metaheuristic algorithms such as Particle Swarm Optimization (PSO) and Artificial Bee Colony (ABC) have demonstrated their effectiveness in delivering high-quality solutions and efficient optimization capabilities across different problem domains (Amato, 2017). However, their application in PCG remains limited. I aim to evaluate the performance of PSO and ABC in map layout generation, challenging the conventional use of GAs. By comparing three metaheuristic algorithms (GA, ABC, and PSO) I seek to assess the effectiveness of these approaches in generating game levels and identify any obvious differences in their performance characteristics. Comprehensive experiments are conducted, applying GA, ABC, and PSO to a map layout generation. Metrics like convergence speed and content quality are used to evaluate the generated game content. My findings reveal that both ABC and PSO demonstrate advantages over traditional GA implementations when generating game levels, indicating their potential for enhancing PCG. In this presentation, I will share the results of comparing three metaheuristic algorithms (GA, PSO, and ABC) in map layout generation for game levels, emphasizing the potential benefits of leveraging diverse algorithmic approaches to create more captivating and immersive game worlds. Also, I will conclude with a call for further research in this area to expose new possibilities in content generation. By considering varied metaheuristic approaches, game developers can improve content generation techniques and create more captivating and interactive player experiences.

This paper studies the two-stage assembly flow shop problem (TAFSP) considering aging effects of the machines and preventive maintenance activities. At the first stage, m-1 parallel machines process parts of each jobs, and at the second stage, related parts of the jobs are assembled by one assembly machine. As the machines work on the jobs, their tools get aged. Aging effects on the machines causes that they will not be able to complete the jobs in the same time could as they were new or when they are operating jobs immediately after their preventive maintenance activity. Processing times of the job are related to the positions, in which it is located after the last preventive maintenance. The job that is operated in a position immediately after the preventive maintenance activity on a machine has its standard processing time. However, the processing time of the jobs operated in the further positions increase based on the number of the positions. The machines return to the initial condition after each preventive maintenance activity. The objective is to schedule the jobs on the machines and determine when the preventive maintenance activities get done on them in order to minimize the total weighted tardiness and maintenance costs. An integer mathematical model is presented for the problem and its validation is shown by solving an example in small scale. Since two-stage assembly flow shop problem is NP-hard, in order to solve the problem in medium and large scale two meta-heuristic algorithms, hybrid genetic algorithm (HGA) and hybrid particle swarm optimization (HPSO) are proposed. These algorithms are the hybrid version of genetic algorithm and particle swarm optimization representatively with simulated annealing. The algorithms are tuned by using Taguchi method, and are used to solve many numerical examples. Finally, the statistical analysis illustrates that the performance of HPSO is better than HGA.

This paper presents compared performances of three metaheuristic algorithms in determining the cost of hybrid renewable energy system. Using genetic algorithm (GA), particle swarm optimization (PSO), and artificial bee colony (ABC), the best affordable sizes of solar photovoltaic array, battery bank, and a minimum-rated diesel generator that could be hybridized to meet the demand of a community in Southwest Nigeria were determined. Load profile, solar radiation, and temperature data were employed as required inputs, and the parameters of the algorithms were properly set to ensure the best result. Bonferroni– Holm method was deployed to ascertain the statistical significance among the algorithms. It was found that ABC produced the best configuration comprising 427 numbers of solar photovoltaic panels, 19 battery units, and 163.2 kW-rated diesel generator. With this, a total annualized cost of $167 284 and 0.2443 estimated cost of energy were obtained. These were the lowest when compared with PSO and GA. The t-test between PSO and ABC are both 5.83 × 10−10 < 0.01666667, between ABC and GA are 6.09 × 10−6 0.01666667 and 9.13 × 10−1 > 0.05. PSO/ABC and ABC/GA groups are clarified significant, while GA/PSO group is insignificant; post hoc test reveals that ABC produced the best result. Hence, a reliable and sustainable power supply at a reduced cost is guaranteed for the community. Cite this article as: T.O. Ajewole, O. Oladepo, K.A. Hassan, A.A. Olawuyi, O. Opeyemi, Comparative study of the performances of three metaheuristic algorithms in sizing hybrid-source power system. Turk J Electr Power Energy Syst, 2022; 2(2), 134-146.

An efficient combination of multi-objective evolutionary optimization and reliability analysis for reliability-based design optimization of truss structures

In this paper, we present a comprehensive and in-depth investigation on the optimization of Proportional-Integral (PI) controller tuning for achieving stability and desired overshoot in the step response. The main objective of this study is to compare the effectiveness of Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) techniques in finding the optimal parameters for the PI controller. The PI controller is a widely used control algorithm that plays a crucial role in many industrial processes. Its tuning greatly affects the system's performance, particularly in terms of stability and overshoot. Therefore, finding the optimal tuning parameters is of utmost importance. To address this optimization problem, we propose the utilization of two popular metaheuristic algorithms, GA and PSO. These algorithms are known for their ability to efficiently search through large solution spaces and find near-optimal solutions. By applying these algorithms to the PI controller tuning problem, we aim to determine which technique yields better results in terms of stability and overshoot tuning. In our comparative study, we provide a detailed explanation of both GA and PSO algorithms, focusing on their working principles and mathematical formulations. We also describe how these algorithms can be applied to the PI controller tuning problem. Furthermore, we highlight the key differences between GA and PSO, shedding light on their strengths and weaknesses. To assess the performance of GA and PSO, we conduct several experiments using different benchmark functions and step response models. We measure the stability and overshoot metrics for various parameter settings obtained through GA and PSO. By thoroughly analyzing the obtained results, we draw meaningful conclusions regarding the effectiveness of each technique. Our findings demonstrate that both GA and PSO exhibit promising results in optimizing PI controller tuning. These observations provide valuable insights and guidelines for choosing the appropriate algorithm based on specific control requirements. In conclusion, this comparative study is thought to contribute to the field of control systems engineering by offering a comprehensive analysis of GA and PSO techniques in the context of PI controller tuning. By highlighting their strengths and weaknesses, it is aimed to provide researchers and practitioners with valuable information for making informed decisions when optimizing control parameters for stability and overshoot reduction purposes.