A Comparative Study of Recent Multi-objective Metaheuristics for Solving Constrained Truss Optimisation Problems

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Abstract

Multi-objective truss optimisation is a research topic that has been less investigated in the literature compared to the single-objective cases. This paper investigates the comparative performance of fourteen new and established multi-objective metaheuristics when solving truss optimisation problems. The optimisers include multi-objective ant lion optimiser, multi-objective dragonfly algorithm, multi-objective grasshopper optimisation algorithm, multi-objective grey wolf optimiser, multi-objective multi-verse optimisation, multi-objective water cycle algorithm, multi-objective Salp swarm algorithm, success history-based adaptive multi-objective differential evolution, success history–based adaptive multi-objective differential evolution with whale optimisation, non-dominated sorting genetic algorithm II, hybridisation of real-code population-based incremental learning and differential evolution, differential evolution for multi-objective optimisation, multi-objective evolutionary algorithm based on decomposition, and unrestricted population size evolutionary multi-objective optimisation algorithm. The design problem is assigned to minimise structural mass and compliance subject to stress constraints. Eight classical trusses found in the literature are used for setting up the design test problems. Various optimisers are then implemented to tackle the problems. A comprehensive comparative study is given to critically analyse the performance of all algorithms in this problem area. The results provide new insights to the pros and cons of evolutionary multi-objective optimisation algorithms when addressing multiple, often conflicting objective in truss optimisation. The results and findings of this work assist with not only solving truss optimisation problem better but also designing customised algorithms for such problems.

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Availability of data and materials

The test data, test problems’ details, and optimisation parameters settings can be found in https://github.com/Natee-Panagant/Comparative-study-of-truss-sizing-optimisation-problems-in-MATLAB/.

Code availability

All the implemented MATLAB codes are available in https://github.com/Natee-Panagant/Comparative-study-of-truss-sizing-optimisation-problems-in-MATLAB/.

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Funding

Sujin, Natee, and Nantiwat have financial support from the Thailand Research Fund (RTA6180010).

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Affiliations

  1. Sustainable Infrastructure Research and Development Centre, Department of Mechanical Engineering, Faculty of Engineering, Khon Kaen University, Khon Kaen, 40002, Thailand

    Natee Panagant, Nantiwat Pholdee & Sujin Bureerat

  2. Department of Automotive Engineering, Bursa Uludağ University, Görükle, Bursa, Turkey

    Ali Riza Yildiz

  3. Centre for Artificial Intelligence Research and Optimisation, Torrens University Australia, 90 Bowen Terrace, Brisbane, QLD, 4006, Australia

    Seyedali Mirjalili

  4. YFL (Yonsei Frontier Lab), Yonsei University, Seoul, Korea

    Seyedali Mirjalili

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  1. Natee Panagant
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  2. Nantiwat Pholdee
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  3. Sujin Bureerat
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  4. Ali Riza Yildiz
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  5. Seyedali Mirjalili
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Correspondence to Sujin Bureerat.

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Panagant, N., Pholdee, N., Bureerat, S. et al. A Comparative Study of Recent Multi-objective Metaheuristics for Solving Constrained Truss Optimisation Problems. Arch Computat Methods Eng (2021). https://doi.org/10.1007/s11831-021-09531-8

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