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Aderinlewo, O and Alade, G A (2014) Reliability-based analysis and design of steel frames with different supports. Journal of Engineering, Design and Technology, 12(01), 39-52.

Cardoso, A F S P and Torkkeli, M (2014) Innovation in footwear companies – does it pay off?. Journal of Engineering, Design and Technology, 12(01), 128-54.

Fernando, N G, Amaratunga, D and Haigh, R (2014) The career advancement of the professional women in the UK construction industry: The career success factors. Journal of Engineering, Design and Technology, 12(01), 53-70.

Kiyang, S T and Zyl, R V (2014) Empirical investigation of wireless sensor network performance in noisy environments. Journal of Engineering, Design and Technology, 12(01), 29-38.

Othman, A A E (2014) A study of the competency requirements of property development graduates to meet the qualifications of the South African construction industry. Journal of Engineering, Design and Technology, 12(01), 2-28.

Rajiv, P, Logesh, R, Vinodh, S and Rajanayagam, D (2014) Financial feasibility and value engineering principles integrated quality function deployment for a manufacturing organization: A case study. Journal of Engineering, Design and Technology, 12(01), 71-88.

Sun, Y, Djouani, K, Wyk, B J v, Wang, Z and Siarry, P (2014) Hypothesis testing-based adaptive PSO. Journal of Engineering, Design and Technology, 12(01), 89-101.

  • Type: Journal Article
  • Keywords: Artificial intelligence; Optimization algorithms; Optimization and system/network identification
  • ISBN/ISSN: 1726-0531
  • URL: https://doi.org/10.1108/JEDT-10-2011-0078
  • Abstract:
    Purpose – In this paper, a new method to improve the performance of particle swarm optimization is proposed. Design/methodology/approach – This paper introduces hypothesis testing to determine whether the particles trap into the local minimum or not, then special re-initialization was proposed, finally, some famous benchmarks and constrained engineering optimization problems were used to test the efficiency of the proposed method. In the revised manuscript, the content was revised and more information was added. Findings – The proposed method can be easily applied to PSO or its varieties. Simulation results show that the proposed method effectively enhances the searching quality. Originality/value – This paper proposes an adaptive particle swarm optimization method (APSO). A technique is applied to improve the global optimization performance based on the hypothesis testing. The proposed method uses hypothesis testing to determine whether the particles are trapped into local minimum or not. This research shows that the proposed method can effectively enhance the searching quality and stability of PSO.

Vinodh, S and Jayakrishna, K (2014) Development of integrated ECQFD, LCA and sustainable analysis model: A case study in an automotive component manufacturing organization. Journal of Engineering, Design and Technology, 12(01), 102-27.