What does EHO mean in UNCLASSIFIED

Concept

EHO mimics the social behavior of elephants within a herd, where individual elephants (agents) interact and collaborate to achieve a common goal. Each agent possesses a position (solution) in the search space, and the herd's objective is to find the optimal solution.

Algorithm

EHO operates in three distinct phases:

• Initialization: Randomly initialize a herd of agents within the search space.
• Clan Formation: Divide the herd into smaller clans based on their proximity.
• Clan Movement: Each clan leader (best agent) guides its members towards its current position.

Clan Update

Within each clan, agents are periodically updated based on the clan leader's position and a random walk strategy. This allows for exploration and exploitation of the search space.

Leader Update

The clan leader is updated in each iteration based on the fitness of the agents in its clan. The agent with the best fitness becomes the new clan leader.

Herd Movement

Once all clans have updated their agents, the herd as a whole moves towards the best clan leader's position. This facilitates global convergence towards the optimal solution.

Advantages

• Robustness: EHO is less susceptible to falling into local optima compared to other optimization algorithms.
• Balance: It achieves a balance between exploration and exploitation, allowing for thorough search and efficient convergence.
• Adaptability: EHO can be easily adapted to solve various optimization problems with different constraints and objectives.

Disadvantages

• Computational Complexity: EHO can be computationally expensive for large-scale problems due to its iterative nature.
• Parameter Tuning: The performance of EHO is sensitive to the choice of algorithm parameters, requiring careful tuning.