A distinctive optimization technique known as Ant Colony Optimization (ACO) has gained huge popularity in these recent years due to its flexibility and the ability to avoid reaching local optima. This optimization approach has become a candidate approach for many optimization problems. Unfortunately, this attractive algorithm suffers several downsides including stagnation and slow convergence toward optimal solution. Thus, a new algorithm, termed as Differential Evolution Ant Colony Optimization (DEACO) has been modelled to compensate the drawbacks. The algorithm was utilized to solve economic load dispatch problem in order to verify its performance. Economic Load Dispatch (ELD) problem concerns the planning of generators outputs that can meet load demand at minimum operating cost. Moreover, in this research, several ant parameters, including number of ants and nodes were manipulated to investigate the behaviour of DEACO algorithm. Comparative studies between DEACO and conventional ACO suggested that the new algorithm has successfully overcome the weaknesses of classical ACO.
|Number of pages||11|
|Journal||Journal of Theoretical and Applied Information Technology|
|Publication status||Published - 01 Jan 2013|
All Science Journal Classification (ASJC) codes
- Theoretical Computer Science
- Computer Science(all)