Economic Load Dispatch Problem with Valve Point Effect Through Gravitational Search Algorithm

The Economic Load Dispatch Problem (ELDP) plays an important role in the operation of power system, and several models by using different techniques have been used to solve these problems. Several traditional approaches, like lambda iteration and gradient method are utilized to find out the optimal solution of non linear problem. The purpose of this work is to find out the advantages of application of the evolutionary computing technique to the economic load dispatch problem including valve point loading effect. In solving optimization problems with a high-dimensional search space, the classical optimization algorithms do not provide a suitable solution because the search space increases exponentially with the problem size, therefore solving these problems using exact techniques (such as exhaustive search) is not practical. There is no specific algorithm to achieve the best solution for all optimization problems. Some algorithms give a better solution for some particular problems than others. Drawbacks of classical algorithms such as gradient based, interior point, linear programming etc., can be declared as insecure convergence properties, long execution time, and algorithmic complexity. Besides, the solution can be trapped in local minima. Hence searching for new heuristic optimization algorithms is an open problem. In the present work, a new meta-heuristic and population based search algorithm based on Newton’s law of gravity and law of motion proposed by Rashedi et al. in 2009 known as GRAVITATIONAL SEARCH ALGORITHM (GSA) is used for solving Economic Load Dispatch Problem (ELDP) including valve point loading effect. The GSA algorithm uses the theory of Newtonian physics and its searcher agents are the collection of masses. The gravitational force moves all agents to the optimal solution so the position of the agent gives the solution.