A heuristic particle swarm optimizer for optimization of pin connected structures
Computers and Structures
Particle swarm approach for structural design optimization
Computers and Structures
Evolutionary algorithms, homomorphous mappings, and constrained parameter optimization
Evolutionary Computation
An efficient simulated annealing algorithm for design optimization of truss structures
Computers and Structures
Size optimization of space trusses using Big Bang-Big Crunch algorithm
Computers and Structures
Self-adaptive harmony search algorithm for optimization
Expert Systems with Applications: An International Journal
Comparison of non-deterministic search techniques in the optimum design of real size steel frames
Computers and Structures
Sizing truss structures using teaching-learning-based optimization
Computers and Structures
Direct simulation of the tensioning process of cable-stayed bridges
Computers and Structures
Fully Stressed Design Evolution Strategy for Shape and Size Optimization of Truss Structures
Computers and Structures
Survey A survey on applications of the harmony search algorithm
Engineering Applications of Artificial Intelligence
Chaotic swarming of particles: A new method for size optimization of truss structures
Advances in Engineering Software
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Harmony search (HS) algorithm was conceptualized using an analogy with music improvisation process where music players improvise the pitches of their instruments to obtain better harmony. Although the efficiency of HS algorithm has been proved in different engineering optimization applications, it is known that HS algorithm is quite sensitive to the tuning parameters. Several variants of HS algorithm have been developed to decrease the parameter-dependency character of HS algorithm. In this study, two improved harmony search algorithms called efficient harmony search algorithm (EHS) and self adaptive harmony search algorithm (SAHS) are proposed for sizing optimization of truss structures. Four classical truss structure weight minimization problems are presented to demonstrate the robustness of the proposed algorithms. The results of the present algorithms are compared with those of HS algorithm and other meta-heuristic algorithms recently developed in literature.