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A Memetic Approach to Multi-Disciplinary Design and Numerical Optimization Problems using Intensify Slime Mould Optimizer

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Abstract

A new memetic metaheuristic optimizer, hybridizing the classical Slime Mould Optimization Algorithm (SMA) with Harris hawk’s Optimizer (HHO), is developed. The proposed hSMA-HHO is based on SMA- a swarm-inspired population metaheuristics algorithm with a notable approach in global optimization and HHO- a nature-inspired algorithm based on the supportive behavior and hunting style of Harris hawks. Although both algorithms perform well individually, they still require improvement for better efficacious results. The exploitation and exploration behavior are improved. Also, the problem of trap** in local optima is removed as observed by investigating the proposed algorithm on an extensive set of standard benchmarks comprising multiple functions with various dimensions. The results achieved are analyzed and compared with other recent metaheuristic algorithms. Furthermore, convergence graphs and statistical analysis prove the supremacy of the proposed hSMA-HHO algorithm over other up-to-date metaheuristics algorithms. The proposed algorithm is also checked to solve the optimal design of 11 well-recognized constrained engineering problems. Analysis and comparison of results reveal that the proposed hSMA-HHO algorithm is an encouraging and viable optimization approach for elucidating different engineering design problems.

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Authors and Affiliations

  1. School of Electronics and Electrical Engineering, Lovely Professional University, Punjab, India

    Shivani Sehgal & Vikram Kumar Kamboj

  2. Department of Electrical Engineering, Maharishi Markandeshwar (Deemed to be University) Mullana, Haryana, India

    Aman Ganesh

  3. Schulich School of Engineering, University of Calgary, Alberta, Canada

    Vikram Kumar Kamboj & O. P. Malik

  4. Bissett School of Busines, Mount Royal University, Calgary, Alberta, Canada

    Vikram Kumar Kamboj

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Sehgal, S., Ganesh, A., Kamboj, V.K. et al. A Memetic Approach to Multi-Disciplinary Design and Numerical Optimization Problems using Intensify Slime Mould Optimizer. Appl Intell 54, 2031–2083 (2024). https://doi.org/10.1007/s10489-023-05073-7

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