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Optimisation is the process of trying to find out the best possible solution to any problem satisfying constraints. Soft computing is the class of methods which have been inspired by the biological computational methods and nature's problem-solving strategies. Currently, these methods include neural networks, evolutionary computational models such as genetic algorithms, random cost and linguistic models such as fuzzy logic. Ant colony optimisation (ACO) is one such method applied for large engineering combinatorial optimisation problems. A design procedure utilising an ACO technique is developed for discrete optimisation of reticulated steel space trusses. The ACO algorithm is motivated by the analogy with natural phenomena, in particular the ability of a colony of ants to 'optimise' their collective endeavours. In this paper, the computational implementation of ACO is presented in a structural design context. The objective function considered is the total weight/cost of the structure subjected to material and performance constraints in the form of stress and deflection limits. In the case of reticulated space trusses, the design variables are the cross-sectional areas of members belonging to various groups. The objective function and constraints are obtained by using structural analysis package FEAST (Anonymous, 1995. FEAST user manual. Trivandrum, India: SEG, SDS Group, ISRO, VSSC) in case of structures subjected to static loading and SAP90 (Anonymous, 1990. SAP90, ETABS, SAFE - computer software for structural and earthquake engineering. Berkeley, CA: Computers and Structures) for earthquake loading for reticulated steel space trusses. The numerical examples presented demonstrate the computational advantage of the ACO for large-scale optimisation problems. [ABSTRACT FROM AUTHOR]