In a day-ahead electricity market, competitive bidding strategy plays a vital role for power suppliers to maximize their profit. In this type of market, each power supplier submits a set of hourly production prices and offers capacity for the next period. The market operator, after receiving this data along with forecasted hourly load from the demand side, allocates production output to each unit. Power suppliers face the problem in trading their offers in the market, due to the uncertain behavior of competitive power suppliers and power demand. Therefore, the power supplier requires a suitable bidding strategy for handling uncertainty in the market to maximize their profits. Moreover, the considerations of ramp rates are necessary for the precise representation of practical power system. Thus, in the present work, a modified gravitational search algorithm based on oppositional learning concept is used to solve strategic bidding problem for power suppliers considering six generators with ramp rates, 24-h load data and rivals behavior. The total hourly profits of generators with and without considering ramp rates have been compared.