proteas optimization algorithm

sphere function is implemented

The idea of a "Proteas Optimization Algorithm" could be inspired by the characteristics of the South African national cricket team, the Proteas, and the resilient qualities of the protea flower, which is known for its adaptability and toughness in diverse environmental conditions. This optimization algorithm could draw inspiration from concepts of resilience, strategic planning, and adaptability in changing conditions, similar to how a cricket team adjusts its strategy or how the protea flower survives in harsh environments.Concept of the Proteas Optimization Algorithm

1. Adaptability: The algorithm would mimic the ability of the Proteas to adapt to different cricket formats (Test, ODI, T20) and strategies, or the protea flower’s resilience. It would adjust its search strategy dynamically in response to environmental changes in the optimization landscape.
2. Strategic Phases: Similar to how a cricket match has different phases (powerplays, middle overs, death overs), the algorithm could break down the optimization process into strategic phases, each with distinct behaviors and objectives.
3. Teamwork and Coordination: The algorithm could simulate collaboration among different "players" (candidate solutions) to achieve the best outcome, emphasizing coordination and mutual support.

Design Principles of the Proteas Optimization Algorithm

1. Initialization: Generate a diverse initial population of solutions, representing the Proteas team players, each with a specific role and strategy.
2. Phased Exploration and Exploitation:

• Powerplay Phase (Aggressive Search): Focus on exploring the search space aggressively with a higher risk-taking strategy, aiming to cover a wide range of potential solutions.
• Middle Overs (Balanced Search): Transition to a more balanced approach, focusing on both exploration and exploitation to refine promising areas while still maintaining some diversity.
• Death Overs (Intensive Exploitation): Narrow down the search to the most promising areas, making precise and fine-tuned adjustments to optimize the best solutions.

1. Resilience Mechanism: Implement a recovery strategy to handle setbacks (e.g., getting stuck in local optima). This could involve restarting or diversifying the population when progress stagnates.
2. Communication and Adaptation: Solutions can share information about their performance, similar to how team members communicate, enabling the algorithm to adapt strategies based on what is working well.

Mathematical Formulation

1. Population Representation: Each candidate solution in the population is akin to a cricket player with specific attributes, such as position, strategy, and fitness.
2. Fitness Evaluation: The performance of each solution is evaluated based on an objective function, similar to assessing a player’s performance in terms of runs scored, wickets taken, or overall impact on the game.
3. Search Strategies:

• Aggressive Search: Use larger steps or more radical changes in solution parameters to explore the search space.
• Conservative Search: Make smaller, more calculated adjustments to solutions to fine-tune and exploit promising areas.