José P.G. Carvalho

Bio: José P.G. Carvalho is an academic researcher from Universidade Federal de Juiz de Fora. The author has contributed to research in topics: Optimization problem & Particle swarm optimization. The author has an hindex of 2, co-authored 8 publications receiving 27 citations.

Truss optimization with multiple frequency constraints and automatic member grouping

Abstract: This paper deals with sizing and shape structural optimization problems with respect to the minimization of the masses of truss structures considering multiple natural frequencies as the constraints of the problems. The sizing and shape design variables are discrete and continuous, respectively. It can be attractive to use a reduced number of distinct cross-sectional areas minimizing costs of fabrication, transportation, storing, checking, welding, and so on. Also, it is expected a labor-saving when the structure is welded, checked and so on. On the other hand, one can observe that the task of discovering the optimum member grouping is not trivial and leads to an exhaustive trial-and-error process. Cardinality constraints are adopted in order to obtain an automatic variable linking searching for the best member grouping of the bars of the trusses analyzed in this paper. A CRPSO (Craziness based Particle Swarm Optimization) is the search algorithm adopted in this paper. This algorithm uses a modified velocity expression and an operator called “craziness velocity” in order to avoid premature convergence. An Adaptive Penalty Method is adopted to handle the constraints. Six truss structures are analyzed, presenting very interesting results providing curves of tradeoff between the optimized weights versus the number of distinct cross-sectional areas used in these solutions.

Multi-objective truss structural optimization considering natural frequencies of vibration and global stability

Abstract: Conflicting objectives such as minimizing weight and minimizing the maximum nodal displacement, with constraints on normal stresses in the bars, is a common multi-objective structural optimization problem widely found in the literature. This paper proposes multi-objective structural optimization problems with the combination of new conflicting objectives functions and constraints, such as the natural frequencies of vibration and the load factors concerning the global stability of the structure. The solution for these problems may be of great interest in the field of structural engineering, not yet discussed in the literature. The problems analyzed in this paper deal with both discrete and continuous sizing, shape, and layout design variables. The search algorithm adopted here is a modified version of the Differential Evolution called the Third Evolution Step Differential Evolution (GDE3). Several experiments are analyzed with their Pareto-fronts showing the non-dominated solutions. The solutions are defined after obtaining the Pareto curve, which is one of the most important steps and a task that may not be trivial for the Decision Maker. This paper involves a strategy that establishes criteria defining weights (importance) for each objective function and, through these values, enables comparison scenarios. The numerical experiments include plane and spatial benchmark trusses.

A Differential Evolution to Find the Best Material Groupings in Truss Optimization

Abstract: Recently, the structural optimization has received a strong emphasis that leads in the formulation of the objective function questions regarding the possible combination of various materials That is, the multi- material optimization in which these materials present different characteristics between them For example, those referring to the behavior of the material that can be physically linear or non-linear, linear behaviors with different modulus of elasticity, different costs depending on the volume to be used, different behaviors in tension and compression, and so on The topological structural optimization, particularly, has been receiving efforts in this direction and is extremely adequate to address this type of problem Another issue in this process is to include the possibility of limiting the number of different materials to be used in the optimized final design The objective of this paper is to propose a strategy to obtain solutions for structural optimization problems in sizing, shape and topology, where the use of different materials will be incorporated in the formulation of the problem, besides the possibility of the designer choosing the maximum number of these materials The search algorithm to be used is the Differential Evolution and the control of the maximum number of materials to be used is done through the use of cardinality constraints

An adaptive constraint handling technique for particle swarm in constrained optimization problems

Abstract: Nature inspired meta-heuristics are largely used to solve optimization problems. However, these techniques should be adapted when solving constrained optimization problems, which are common in real world situations. Here an adaptive penalty approach (called Adaptive Penalty Method, APM) is combined with a particle swarm optimization (PSO) technique to solve constrained optimization problems. This approach is analyzed using a benchmark of test-problems and 5 mechanical engineering problems. Moreover, three variants of APM are considered in the computational experiments. Comparison results show that the proposed algorithm obtains a promising performance on the majority of the test problems

Black Hole Mechanics Optimization: a novel meta-heuristic algorithm

Abstract: In this paper, a new meta-heuristic algorithm is proposed. The proposed method contains a mathematical kernel and a physical simulation. The mathematical kernel computes the optimum direction of each variable subjected to the cost function. Then, it conducts generated data to the detected path. Besides, the physical simulation controls the exploration procedure as well as the exploitation. The simulating process is based on the mechanics of black holes. In the structure of the proposed algorithm, there are two types of black holes. The first is a Kerr black hole forming a circular gravity filed to explore all the problem search space. The other one is a Schwarzschild black hole exploiting the data in the vicinity of its singularity. Eventually, the potency and efficiency of the new algorithm are investigated using several mathematical and four benchmark skeletal structures.

Multi-objective truss structural optimization considering natural frequencies of vibration and global stability

Abstract: Conflicting objectives such as minimizing weight and minimizing the maximum nodal displacement, with constraints on normal stresses in the bars, is a common multi-objective structural optimization problem widely found in the literature. This paper proposes multi-objective structural optimization problems with the combination of new conflicting objectives functions and constraints, such as the natural frequencies of vibration and the load factors concerning the global stability of the structure. The solution for these problems may be of great interest in the field of structural engineering, not yet discussed in the literature. The problems analyzed in this paper deal with both discrete and continuous sizing, shape, and layout design variables. The search algorithm adopted here is a modified version of the Differential Evolution called the Third Evolution Step Differential Evolution (GDE3). Several experiments are analyzed with their Pareto-fronts showing the non-dominated solutions. The solutions are defined after obtaining the Pareto curve, which is one of the most important steps and a task that may not be trivial for the Decision Maker. This paper involves a strategy that establishes criteria defining weights (importance) for each objective function and, through these values, enables comparison scenarios. The numerical experiments include plane and spatial benchmark trusses.

Population-based optimization in structural engineering: a review

Abstract: Structural engineering is focused on the safe and efficient design of infrastructure. Projects can range in size and complexity, many requiring massive amounts of materials and expensive construction and operational costs. Therefore, one of the primary objectives for structural engineers is a cost-effective design. Incorporating optimality criteria into the design procedure introduces additional complexities that result in problems that are nonlinear, nonconvex, and have a discontinuous solution space. Population-based optimization algorithms (known as metaheuristics) have been found to be very efficient approaches to these problems. Many researchers have developed and applied state-of-art metaheuristics to automate and optimize the design of real-world civil engineering problems. While there is a large body of published papers in this area, there are few comprehensive reviews that list, summarize, and categorize metaheuristic optimization in structural engineering. This paper provides an extensive survey of a wide range of metaheuristic techniques to structural engineering optimization problems. Also, information is provided on available structural engineering benchmark problems, the formulation of different objective functions, and the handling of various types of constraints. The performance of different optimization techniques is compared for many benchmark problems.