Global Artificial Joint optimization Algorithm(GAJOA)

sphere function is used

General Concept for GAJA:

• Global: The algorithm explores the entire solution space (global search).
• Artificial Joint: The algorithm may be inspired by how different parts of a system (particles, solutions, or components) interact through a joint mechanism (links between solutions).
• Evolution: Like other optimization algorithms, GAJA could involve iterative updates based on interaction rules between different solutions, aiming to converge to an optimal solution.

Possible Characteristics of GAJA:

1. Joint Mechanism: Solutions in the search space interact based on a defined mechanism (such as physical joints, spring-like forces, or connected agents).
2. Movement and Flexibility: The particles or agents could adjust their positions dynamically, seeking optimality through exploration and exploitation.
3. Global Search and Adaptation: GAJA would combine global search techniques (wide exploration) with local search (fine-tuning) to ensure both diversity and convergence toward the optimal solution.

Given this conceptualization, let's assume GAJA works similarly to evolutionary or swarm-based algorithms but introduces "joint" interaction between solutions. Here’s a basic structure and possible MATLAB implementation for a GAJA Optimization Algorithm.