Discrete chaotic gravitational search algorithm for unit commitment problem

This paper presents a discrete chaotic gravitational search algorithm (DCGSA) to solve the unit commitment (UC) problem. Gravitational search algorithm (GSA) has been applied to a wide scope of global optimization problems. However, GSA still suffers from the inherent disadvantages of trapping in local minima and the slow convergence rates. The UC problem is a discrete optimization problem and the original GSA and chaos which belong in the realm of continuous space cannot be applied directly. Thus in this paper a data discretization method is implemented after the population initialization to make the improved algorithm available for coping with discrete variables. Two chaotic systems, including logistic map and piece wise linear chaotic map, are used to generate chaotic sequences and to perform local search. The simulation was carried out on small-scale UC problem with six-unit system and ten-unit system. Simulation results show lower fuel cost than other methods such as quadratic model, selective pruning method and iterative linear algorithm, confirming the potential and effectiveness of the proposed DCGSA for the UC problem.