Optimal Structural Design by Ant Colony Optimization

Abstract

Rcpriab anikblc d i d y from the publisher P h O l o c Q P y i~ pamittsd by lianv crib 0 2001 OPA ( -Publishen Auodation) N.V. Publirhdbylianvmdn thc Gordon and Brrach science P u M imprint.Ant colony optimization (ACO) is a relatively new heuristic comhinatorial optimization algorithm in which the search proass is a stochastic procedure that incorporates positive feedback of awumulated information. The positive feedback (i.e., autocatalysis) facility is a feature of ACO which gives an emergent search procedure such that the (common) problem of algorithm termination at local optima may be avoided and search for a global optimum is possible. The ACO algorithm is motivated by analogy with natural phenomena, in particular. the ability of a colony of ants to 'optimize' their collective endeavours. In this paper the biological background for ACO is explained and its computational implementation is presented in a structural design context. The particular implementation of ACO makes use of a tabu search (TS) local improvement phase to give a computationally enhanced algoritbm (ACOTS).In this paper ACOTS is applied to the optimal structural design, in terms of weight minimization, of a 25-bar space truss. The design variables are the cross-sectional areas of the bars, which take discrete values. Numerical investigation of the 25-bar space truss gave the best (i.e., lowest to-date) minimum weight value. This example provides evidence that ACOTS is a useful and technically viable Optimization technique for discrete-variable optimal structural design.

Conceptual design optimization of rectilinear building frames: A knapsack problem approach

“…The ACO method can be categorized as a stochastic method, which, although generally more computationally expensive than standard deterministic methods, is more effective in dealing with problems with a large number of local minima (Bland 2001;Afshar, Sharifi, and Jalali 2009). The ACO method has been inspired by the behaviour of ant colonies when they try to reach food.…”

Improving End-User Programming with Situational Mashups in Web 2.0 Environment

“…There were various optimization solutions have been proposed, such as mathematics programming, genetic algorithm [22], ant colony optimization [23], cultural algorithm [24], and particle swarm optimization [25]. In the study area of search-based software engineering (SBSE) research area [26], there are many contributions for combinational optimization based on the metaheuristic mechanisms.…”

Search group algorithm: A new metaheuristic method for the optimization of truss structures

“…In fact, a large number of these algorithms have been widely applied for solving a variety of truss optimization problems. For example, classical metaheuristic algorithms such as Simulated Annealing [5,6], Genetic Algorithms [7,8], Particle Swarm Optimization [6,[9][10][11], Harmony Search [12][13][14] and Ant Colony Optimization [15], were employed. For a comprehensive review of the optimization of truss structures using metaheuristics, the reader is referred to Saka [16,17], Lamberti and Pappalettere [18] and the references therein.…”