Kabeka Optimization Algorithm (KOA)

sphere function is used

The Kabeka Optimization Algorithm (KOA) is a relatively new optimization algorithm inspired by the movement and behavior of the Kabeka bird, which is native to regions of Africa. It is based on the biological behaviors of this bird, and it was designed to tackle continuous optimization problems. The algorithm mimics the way the Kabeka bird forages for food by balancing exploration and exploitation strategies.Key Features of the Kabeka Optimization Algorithm:

1. Population-based search: Similar to other nature-inspired algorithms, KOA uses a population of solutions that evolve over time to find the optimal solution.
2. Exploration and Exploitation: The algorithm incorporates both exploration (searching the solution space broadly) and exploitation (refining solutions in the vicinity of the best solution) to avoid local optima and ensure global search capability.
3. Movement Mechanism: The algorithm models the movement of the Kabeka bird, which can fly to new locations or exploit the local area for resources based on certain conditions.

General Steps of the Kabeka Optimization Algorithm:

1. Initialization:

• A population of solutions (agents) is initialized randomly within the search space.
• Each agent represents a potential solution to the optimization problem.

1. Fitness Evaluation:

• The fitness of each agent is evaluated based on the objective function.

1. Movement Strategies:

• Exploration: Some agents will explore the search space by moving randomly to new positions.
• Exploitation: Other agents will move towards the best solution found so far (exploitation).

1. Update Mechanism:

• Each agent updates its position based on a combination of exploration and exploitation strategies, balancing the two to ensure efficient search behavior.

1. Convergence: The algorithm continues iterating through these steps, gradually improving the population’s solutions, until a stopping criterion (such as a maximum number of iterations or a convergence threshold) is reached.

Basic Pseudo-code for KOA:

Here’s an outline of how the algorithm might be structured in pseudo-code:

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Initialize population P with random positions in the search space

Evaluate fitness of each agent in P using the objective function

Repeat until stopping condition is met:

For each agent i in P:

Evaluate the fitness of agent i

If agent i is better than its previous position:

Update the best position of agent i

If exploration condition is met:

Move agent i to a random position in the search space

Else if exploitation condition is met:

Move agent i towards the best known position

Update the best solution found so far

End

Return the best solution found