An ant colony algorithm applied to lay-up optimization of laminated composite plates

Abstract

Ant colony optimization (ACO) is a class of heuristic algorithms proposed to solve optimization problems. The idea was inspired by the behavior of real ants, related to their ability to find the shortest path between the nest and the food source. ACO has been applied successfully to different kinds of problems. So, this manuscript describes the development and application of an ACO algorithm to find the optimal stacking sequence of laminated composite plates. The developed ACO algorithm was evaluated on four examples for symmetric and balanced lay-up. The results of the first three cases, in which the classical lamination theory was used to obtain the structural response of rectangular plates, were compared to those obtained from the literature using genetic algorithms (GA) and other ACO algorithm. The fourth example investigates the maximization of fundamental frequencies of rectangular plates with central holes, where the structural response was obtained by finite element analysis, showing that this optimization technique may be successfully applied to a broad class of stacking sequence problems for laminated composites.

Weight optimization of hybrid composite laminate using learning-oriented artificial algae algorithm

“…Hybrid composite laminate involves parameters that should be optimized to produce composite laminate that can satisfy critical buckling load factor constraint. Metaheuristic algorithms have been applied in optimization of parameters of composite material [18,22,23,27,[31][32][33].…”

“…Most of the studies focused on composite material under different loading conditions with constraints such as fixed fiber orientation angle (for example 90°, ± 45° and 0°) [14,[24][25][26], fixed ply thickness, for example, 64 layers [24,26], in-plane loading [8,12,14], buckling loading [24,[26][27][28][29], with a fixed composite parameters.…”

Adaptive particle swarm optimization with population diversity control and its application in tandem blade optimization

“…Particle swarm optimization (PSO) is one of the most popular and effective swarm intelligence algorithms, which was raised by Kennedy based on the natural phenomenon of birds looking for food. [1][2][3] Of course, bio-inspired optimization methods such as Genetic Algorithms, 4 Artificial Bee Colony, 5 and Ant Colony Optimization 6 have been successfully applied for optimization design of real-life problems. Benefitted from its simple concept, fast convergence, and strong capacity of global optimization, PSO has become one of the most promising optimization algorithms since its development and has many applications in diverse sciences such as electric power system optimization, 7 neural network training, 8 optimization of geotechnical problems, 9 structure optimization design, 10 material optimization, 11 and so on.…”

Optimization of concrete I-beams using a new hybrid glowworm swarm algorithm

“…Hybrid PSO optimization has also been widely applied (Shieh et al, 2011;Valdez et al, 2011;Fan and Zahara, 2007;Ahandania et al, 2012;Li et al, 2009;Wang et al, 2013) demonstrating faster convergence rates. Likewise, ACO was improved by incorporating a hybridization strategy (Chen et al, 2012;Koide et al, 2013). This paper describes a new hybrid algorithm applied to two objective functions, namely the embedded CO 2 emissions and the economic cost.…”