Hyper-FDB-INFO Algorithm

this is latest novel optimization for complex and discontunous optimization problems, which is tested on sphere function

:Pseudocode: Hyper-FDB-INFO Algorithm

1. Initialization:

• Generate an initial population using the LSHADE algorithm.
• Incorporate chaotic maps (CMs), opposition-based learning (OBL), and population ratios to ensure diversity.

1. Training Stage (using LSHADE):

• For each candidate solution in the population:

1. Apply the INFO/FDB-INFO algorithm for a fixed number of iterations.
2. Evaluate the fitness of the candidate solution based on the performance of INFO/FDB-INFO.

• Select the best candidate solution as the initial population for the test stage.

1. Test Stage (using INFO/FDB-INFO):

• Stage 1: Updating Rule
• Update the population using the weighted mean of vectors and fitness–distance balance (FDB).
• Use the FDB method to guide the exploration and exploitation by selecting candidates with the highest score.
• Stage 2: Vector Combination
• Combine vectors to create new candidate solutions.
• Stage 3: Local Search
• Refine solutions through local search mechanisms.

1. Iterative Optimization:

• Repeat the test stage over the maximum number of iterations to refine the solutions further.

1. Constraints Handling:

• Enforce problem-specific constraints (e.g., generator outputs, FACTS device placements) during the optimization process.

1. Output:

• Return the best solution and its fitness value.

% this is demo code use it and comment its performance

%its is successfully running