Multi objective elk herd optimization for efficient structural design.

Area of Science:

• Engineering Optimization
• Computational Intelligence
• Metaheuristic Algorithms

Background:

• Real-world engineering design often involves complex multi-objective optimization problems.
• Existing optimization algorithms may struggle with balancing exploration and exploitation, leading to suboptimal solutions.
• The Elk Herd Optimization algorithm provides a foundation for developing advanced optimization techniques.

Purpose of the Study:

• To introduce and evaluate the Multi-Objective Elk Herd Optimization (MOEHO) algorithm.
• To assess MOEHO's effectiveness in solving small-to-medium scale structural design problems.
• To compare MOEHO's performance against established algorithms using key metrics like Spacing, Hypervolume, and Inverted Generational Distance.

Main Methods:

• MOEHO leverages elk herd reproductive behavior to balance exploration and exploitation.
• The algorithm was tested on benchmark truss structures.
• Statistical analysis, including the Friedman rank test, was employed to validate robustness and efficiency.

Main Results:

• MOEHO demonstrated superior performance over five well-established algorithms on benchmark truss structures.
• The algorithm achieved better results in terms of Spacing (SP), Hypervolume (HV), and Inverted Generational Distance (IGD).
• MOEHO showed robustness and efficiency, particularly in high-complexity optimization scenarios.

Conclusions:

• MOEHO is a computationally efficient and robust algorithm for multi-objective optimization.
• Its ability to balance exploration and exploitation makes it suitable for complex engineering applications.
• Future research can extend MOEHO to higher-dimensional problems and applications in energy systems optimization.