Enhanced imperialist competitive algorithm for optimal structural design

Solving complex engineering problems using meta-heuristics requires powerful operators to maintain sufficient diversification as well as proper intensification during the search. Standard Imperialist Competitive Algorithm, ICA, delays search intensification by propagating it via a number of articifial empires that compete each other until one concurs with the others. An Enhanced Imperialist Competitive Algorithm (EICA) is developed here by adding an evolutionary operator to the standard ICA followed by greedy replacement in order to improve its effectiveness. The new operator introduces a walking step directed from the less significant t with a fitter individual in each pair of the search agents together with a random scaling and pick-up scheme. EICA performance is then compared with ICA as well as genetic algorithm, particle swarm optimization, differential evolution, colliding bodies optimization, teaching-learning-based optimization, symbiotic organisms search in a set of fifteen test functions. Second, a variety of continuous and discrete engineering benchmarks and structural sizing problems are solved to evaluate EICA in constrained optimization. In this regard, a diversity index and other convergence metrics are traced. The results exhibit a considerable improvement on the algorithm using the proposed features of EICA and its competitive performance, compared to other treated methods.