Evolutionary computation and structural design: A survey of the state-of-the-art

A framework for data-driven analysis of materials under uncertainty: Countering the curse of dimensionality

A heuristic particle swarm optimizer for optimization of pin connected structures

The main goal of the structural optimization is to minimize the weight of structures while satisfying all design requirements imposed by design codes. In this paper, the Artificial Bee Colony algorithm with an adaptive penalty function approach (ABC-AP) is proposed to minimize the weight of truss structures. The ABC algorithm is swarm intelligence based optimization technique inspired by the intelligent foraging behavior of honeybees. Five truss examples with fixed-geometry and up to 200 elements were studied to verify that the ABC algorithm is an effective optimization algorithm in the creation of an optimal design for truss structures. The results of the ABC-AP compared with results of other optimization algorithms from the literature show that this algorithm is a powerful search and optimization technique for structural design.

/pdf/artificial-bee-colony-algorithm-for-optimization-of-truss-1pp0qjan9m.pdf

Artificial Bee Colony algorithm for optimization of truss structures

Multimodal optimization (MMO) aiming to locate multiple optimal (or near-optimal) solutions in a single simulation run has practical relevance to problem solving across many fields. Population-based meta-heuristics have been shown particularly effective in solving MMO problems, if equipped with specifically-designed diversity-preserving mechanisms, commonly known as niching methods. This paper provides an updated survey on niching methods. This paper first revisits the fundamental concepts about niching and its most representative schemes, then reviews the most recent development of niching methods, including novel and hybrid methods, performance measures, and benchmarks for their assessment. Furthermore, this paper surveys previous attempts at leveraging the capabilities of niching to facilitate various optimization tasks (e.g., multiobjective and dynamic optimization) and machine learning tasks (e.g., clustering, feature selection, and learning ensembles). A list of successful applications of niching methods to real-world problems is presented to demonstrate the capabilities of niching methods in providing solutions that are difficult for other optimization methods to offer. The significant practical value of niching methods is clearly exemplified through these applications. Finally, this paper poses challenges and research questions on niching that are yet to be appropriately addressed. Providing answers to these questions is crucial before we can bring more fruitful benefits of niching to real-world problem solving.

/pdf/seeking-multiple-solutions-an-updated-survey-on-niching-2oepq8tnhl.pdf

Seeking Multiple Solutions: An Updated Survey on Niching Methods and Their Applications

http://repository.ias.ac.in/9455/1/372.pdf

Design of truss-structures for minimum weight using genetic algorithms

Optimization of truss-structures for finding optimal cross-sectional size, topology, and configuration of 2-D and 3-D trusses to achieve minimum weight is carried out using real-coded genetic algorithms (GAs). All the above three optimization techniques have been made possible by using a novel representation scheme. Although the proposed GA uses a fixed-length vector of design variables representing member areas and change in nodal coordinates, a simple member exclusion principle is introduced to obtain differing topologies. Moreover, practical considerations, such as inclusion of important nodes in the optimized structure is taken care of by using a concept of basic and non-basic nodes. Stress, deflection, and kinematic stability considerations are also handled using constraints. In a number of 2-D and 3-D trusses, the proposed technique finds intuitively optimal or near-optimal trusses, which are also found to have smaller weight than those that are reported in the literature.

/pdf/optimal-truss-structure-design-using-real-coded-genetic-nlujqdu9vs.pdf

Surendra Gulati

Papers

Design of truss-structures for minimum weight using genetic algorithms

Optimal truss-structure design using real-coded genetic algorithms