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<p>This research presents an advancement of the Elk Herd Optimization targeting specific real-world multi-objective optimization problems,&#xa0;this algorithm is stated as the multi-objective Elk Herd Optimization (MOEHO). MOEHO exploits reproductive behaviour among elk herds for balancing exploration and exploitation within the optimization procedure toward diversification and convergence. The algorithm performed better over the set of small-to-medium scale structural design problems thus is widely applicable in engineering design.&#xa0;Further,&#xa0;when compared with eight benchmark truss structures against five well-established algorithms the MOEHO has outperformed them in the perspective of performance parameters like Spacing (SP), Hypervolume (HV) and Inverted Generational Distance (IGD). More concrete statistical analysis through Friedman rank test also ascertains the robustness and efficiency of the algorithm, especially at high complexities in optimization. The research attracts attention to the ability of such an algorithm which maintains a balance between the exploration and exploitation.&#xa0;Computational efficiency of MOEHO and qualitatively diversifying solutions along Pareto front, makes it especially applicable in complex engineering applications. Further research into extension of MOEHO with applicability on more dimensional problems,&#xa0;applied even in energy systems optimization.</p>

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Multi objective elk herd optimization for efficient structural design

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年4月6日

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  • Pinank Patel,
  • Divya Adalja,
  • Nikunj Mashru,
  • Pradeep Jangir,
  • Arpita,
  • Reena Jangid,
  • Gulothungan G,
  • Mohammad Khishe

摘要

This research presents an advancement of the Elk Herd Optimization targeting specific real-world multi-objective optimization problems, this algorithm is stated as the multi-objective Elk Herd Optimization (MOEHO). MOEHO exploits reproductive behaviour among elk herds for balancing exploration and exploitation within the optimization procedure toward diversification and convergence. The algorithm performed better over the set of small-to-medium scale structural design problems thus is widely applicable in engineering design. Further, when compared with eight benchmark truss structures against five well-established algorithms the MOEHO has outperformed them in the perspective of performance parameters like Spacing (SP), Hypervolume (HV) and Inverted Generational Distance (IGD). More concrete statistical analysis through Friedman rank test also ascertains the robustness and efficiency of the algorithm, especially at high complexities in optimization. The research attracts attention to the ability of such an algorithm which maintains a balance between the exploration and exploitation. Computational efficiency of MOEHO and qualitatively diversifying solutions along Pareto front, makes it especially applicable in complex engineering applications. Further research into extension of MOEHO with applicability on more dimensional problems, applied even in energy systems optimization.