Dynamic Population BAT algorithm for economic load dispatch

Dynamic Population BAT algorithm for economic load dispatch and the test system is IEEE 30 bus system

The provided MATLAB code implements the BAT Algorithm for solving multidimensional optimization problems with a sum constraint. The optimization objective is to minimize a cost function involving a set of variables represented as the search space X.

The BAT Algorithm is inspired by the echolocation behavior of bats, where they use sound pulses to locate prey and communicate with each other. In the BAT algorithm, candidate solutions are represented as bats, and their positions in the search space are updated iteratively to find the optimal solution.

main steps of the Bat Algorithm include:

1. Population Initialization: The algorithm begins by randomly generating an initial population with pop members. Each member represents a potential solution within the search space, with the sum constraint ensured by adjusting the first dimension (X(1)) to match the desired sum XT.
2. Bat Movement: Bats represent candidate solutions, and their movement is controlled by loudness A and pulse rate r, which act as adaptive control parameters. Bats can move towards the global best solution, the local best solution, or explore new regions randomly.
3. Bound Handling: The bat movement is subjected to simple bounds/limits. If a bat's position exceeds the search space boundaries, it is corrected to remain within the valid range.
4. Local and Global Update: The algorithm updates the local best solutions and the global best solution based on the fitness of individual bats.
5. Adaptive Control: The loudness A and pulse rate r are adapted during the optimization process to balance exploration and exploitation.
6. Dynamic Population Size Adjustment: The algorithm dynamically adjusts the population size based on the performance of the global best solution, increasing or decreasing it by a fixed step size after reaching half of the maximum iterations.
7. Result Visualization: The algorithm records the best cost and best fitness over iterations and plots them to visualize the optimization progress.

The Bat Algorithm efficiently explores the search space and can handle constraints, making it suitable for solving complex multidimensional optimization problems with a sum constraint.