An improved ant colony optimization for constrained engineering design problems

• Published in: Engineering computations Vol. 27; no. 1; pp. 155 - 182
• Main Authors: Kaveh, A., Talatahari, S.
• Format: Journal Article
• Language: English
• Published: Bradford Emerald Group Publishing Limited 05.01.2010
• Subjects: Algorithms; Archives & records; Civil engineering; Communication; Computer engineering; Design engineering; Design optimization; Heuristic; Mathematical models; Operations research; Optimization algorithms; Optimum design; Pheromones; Programming and algorithm theory; Statistical analysis; Studies; Optimum design; Civil engineering; Programming and algorithm theory
• ISSN: 0264-4401; 1758-7077
• DOI: 10.1108/02644401011008577

Purpose - The computational drawbacks of existing numerical methods have forced researchers to rely on heuristic algorithms. Heuristic methods are powerful in obtaining the solution of optimization problems. Although they are approximate methods (i.e. their solution are good, but not provably optimal), they do not require the derivatives of the objective function and constraints. Also, they use probabilistic transition rules instead of deterministic rules. The purpose of this paper is to present an improved ant colony optimization (IACO) for constrained engineering design problems.Design methodology approach - IACO has the capacity to handle continuous and discrete problems by using sub-optimization mechanism (SOM). SOM is based on the principles of finite element method working as a search-space updating technique. Also, SOM can reduce the size of pheromone matrices, decision vectors and the number of evaluations. Though IACO decreases pheromone updating operations as well as optimization time, the probability of finding an optimum solution is not reduced.Findings - Utilizing SOM in the ACO algorithm causes a decrease in the size of the pheromone vectors, size of the decision vector, size of the search space, the number of function evaluations, and finally the required optimization time. SOM performs as a search-space-updating rule, and it can exchange discrete-continuous search domain to each other.Originality value - The suitability of using ACO for constrained engineering design problems is presented, and applied to optimal design of different engineering problems.