Neighborhood Search-Driven Accelerated Biogeography-Based Optimization for Optimal Load Dispatch

Lack of exploration capability of biogeography-based optimization (BBO) leads to slow convergence. To address this limitation, this paper presents a memetic algorithm (MA), namely, aBBOmDE, which is a new version of BBO to solve both complex and noncomplex economic load dispatch (ELD) problems of thermal plant. In aBBOmDE, the performance of BBO is accelerated by using a modified mutation and clear duplicate operators. Then, modified DE (mDE) is embedded as a neighborhood search operator to improve their fitness after a predefined threshold. mDE is used with mutation operator DE/best/1/bin to explore the search near the best solution. The length of local search is set to achieve a balance between the search capability and the excess computational cost. In aBBOmDE, migration mechanism is kept same as that of BBO to maintain its exploitation ability. Modified operators are utilized to enhance the exploration ability while a neighborhood search operator, further, enhances the search capability of the algorithm. This combination significantly improves the convergence characteristics of the original algorithm. The effectiveness of the proposed algorithm has been verified on five different test systems with varying degree of complexity. The results have been compared with other existing techniques. The results indicate that the proposed approach can efficiently solve practical ELD problems.