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New population-based simple algorithms for solving global optimisation problems

Author

Listed:
  • A. Baskar
  • M.A. Sai Balaji
  • Jitendra Kumar Katiyar
  • Bharti Nagpal
  • J. Rajesh Babu

Abstract

Heuristic algorithms have effectively been used for solving global optimisation problems in a continuous space. It can be applied to both constrained and unconstrained problems. Presently, several population-based algorithms were proposed by researchers and are available in the literature but those are not enough to solve the issues. Therefore, this study has proposed five new population-based simple algorithms that do not require any tuning parameter. A different strategy was used for updating the solution set. Unlike other algorithms, the solution set is constructed using three or four expressions to ensure an effective search and move towards the optimal/near-optimal solution. Each expression is used to build the population partially and the best one is selected for the next iteration. Further, it is compared with the recent popular arithmetic optimisation algorithm (AOA) using different benchmark functions and test suites of CEC2019. The dimensions are varied from 2 to 1,000. The results demonstrate the better performance of new algorithms over AOA. However, five real-world problems with constraints are also analysed to further validate their efficacy.

Suggested Citation

  • A. Baskar & M.A. Sai Balaji & Jitendra Kumar Katiyar & Bharti Nagpal & J. Rajesh Babu, 2024. "New population-based simple algorithms for solving global optimisation problems," International Journal of Mathematics in Operational Research, Inderscience Enterprises Ltd, vol. 27(2), pages 199-222.
    • Handle: RePEc:ids:ijmore:v:27:y:2024:i:2:p:199-222
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