Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms
Parallel processing neural networks and genetic algorithms
Advances in Engineering Software
An exponential big bang-big crunch algorithm for discrete design optimization of steel frames
Computers and Structures
Upper bound strategy for metaheuristic based design optimization of steel frames
Advances in Engineering Software
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The present study addresses a parallel solution algorithm for optimum design of large steel space frame structures, in particular high-rise steel buildings. The algorithm implements a novel discrete evolution strategy optimization method to effectively size these systems for minimum weight according to the provisions of ASD-AISC specification and various practical aspects of design process. The multitasking environment in the algorithm rests on a master-slave model based parallelization of the optimization procedure, which provides an ideal platform for attaining optimal solutions in a timely manner without losing accuracy in computations. Three design examples from the category of high-rise steel buildings are studied extensively to demonstrate cost-efficiency of the algorithm in conjunction with a cluster of computers with 32 processors. The variation in performance of the parallel computing system with respect to the number of processors employed is also scrutinized in each design example.