Illuminant estimation is the primary step to solve the color constancy problem. There are various statistical-based, learningbased and combinational-based color constancy algorithms already exist. However, the statistical-based algorithms can only perform well on images that satisfy certain assumptions, learningbased methods are complex methods that require proper preprocessing and training data, and combinational-based methods depend on either pre-determined or dynamically varying weights, which are difficult to determine and prone to error. Therefore, this paper presents a new optimization based illuminant estimation method which is free from complex preprocessing and can estimate the illuminant under different environmental conditions. A strong color cast always has an odd standard deviation value in one of the RGB channels. Based on this observation, a cost function called the degree of color cast(DCC) is formulated to determine the quality of illuminant color-calibrated images. Here, a swarm intelligence based particle swarm optimizer(PSO) is used to find the optimum illuminant using the degree of illuminant tinge. The proposed method is evaluated using real-world datasets and the experimental results validate the effectiveness of the proposed method.