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The paper proposes an enhanced version of the comprehensive learning particle swarm optimization (CLPSO) method for the simultaneous optimal size and shape design of steel truss structures under applied forces. The CLPSO approach incorporates the two novel enhancing techniques, namely perturbation-based exploitation and adaptive learning probabilities, in addition to its distinctive diversity of particles preventing the premature local optimum solutions. In essence, the perturbation enables the robust exploitation of the updating velocity of particles, whilst the learning probabilities are dynamically adjusted by the ranking information of personal best particles. A combination of these techniques results in the fast convergence and likelihood of the global optimum solution. Applications of the enhanced CLPSO method are illustrated through a number of successfully solved truss design examples. The robustness and accuracy of the proposed scheme are evidenced by the comparisons with available benchmarks processed by some other metaheuristic algorithms in obtaining the optimal size and shape distributions of steel trusses complying with limit state specifications.

Comprehensive Learning; Particle Swarm Optimization; Perturbation-Based Exploitation; Adaptive Learning Probabilities; Simultaneous Size and Shape Optimization