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Code Analysis

Source files analyzed

  • Diferential Evolution.m

High-level purpose

The MATLAB script runs a Differential Evolution optimization process over a chosen objective function and displays the population on a 3D surface plot.

Main components

1. Session setup

The script starts by clearing variables, closing figures, and clearing the command window.

2. Objective function selection

Several objective functions are present, with two commented options and one active option:

  • commented Griewank-like expression,
  • commented Rastrigin-like expression,
  • active sphere function: f(x, y) = x^2 + y^2.

3. Search bounds and algorithm parameters

The script defines:

  • lower bounds xl,
  • upper bounds xu,
  • number of generations G,
  • population size N,
  • dimensionality D,
  • mutation factor F,
  • crossover rate CR.

4. Population initialization

An initial population matrix x is filled with random values inside the provided bounds.

5. Evolution loop

For each generation, the script performs the following steps:

  • choose random individuals,
  • create a mutant vector,
  • build a trial vector through crossover,
  • compare the trial vector with the current individual,
  • replace the current individual if the trial vector is better.

6. Fitness evaluation and plotting

At the end of each generation, the script allocates a fitness array and then plots:

  • the objective function surface,
  • the current population points as 3D markers.

Inputs

The script does not take external input files or command-line arguments. Its inputs are hard-coded inside the file:

  • objective function choice,
  • bounds,
  • algorithm parameters,
  • plotting domain.

Outputs

The script is designed to produce:

  • iterative visual output through a MATLAB figure window,
  • an animated or repeatedly updated 3D surface plot,
  • no saved data files or exported reports.

Execution flow

  1. Clear the environment.
  2. Select the objective function.
  3. Define bounds and Differential Evolution parameters.
  4. Create the initial population.
  5. Iterate over generations.
  6. Attempt mutation and crossover for each individual.
  7. Evaluate candidate fitness.
  8. Plot the surface and current population.

Important observed limitations

The following observations describe the current code as written.

Area Observation
Random index selection The script uses r1, r2, and r3, but the index-generation logic does not guarantee distinct valid indices.
Conditional flow The continue logic around the random indices is inverted relative to the standard Differential Evolution workflow.
Vector dimensions The trial vector u is created as a row vector and later assigned to a population column, creating a dimension mismatch.
Fitness comparison The replacement test does not compare the current individual x(:, i) against the corresponding trial vector in a consistent individual-by-individual form.
Fitness loop indexing The fitness array is filled using i inside a loop indexed by k, producing an indexing inconsistency.
Optimization direction The active objective function is a minimization problem, but the script later uses max on the fitness array.
Dimensional consistency The script sets D = 3, but the active objective function and plotting logic only use two coordinates.
Domain consistency Search bounds are defined in radians, while the visualization surface is drawn over a different numeric range.
Unused variables counter and ind are present but not meaningfully used in the current script.

Incomplete or missing parts

  • No root README.md was present in the original repository contents.
  • No test files are present.
  • No separate function files are present.
  • No report, assignment brief, or deliverable document is present.
  • No execution log or expected numerical result is present.

Overall assessment

Useful academic example of a population-based optimization script, but not a validated implementation. The source code remains untouched, and the analysis is limited to repository understanding rather than code correction.