Cardinality-constrained Portfolio optimization using an improved quick Artificial Bee Colony Algorithm

Portfolio optimization problem is related to investment trade-off between risk and return. The Investors have to make decision to how much invest money in each company stock while in the stock market there are so many stock to invest. And combination result yield return differently. This problem is one type of NP hard problems which recently trend using evolutionary computation (EC) and swarm intelligence based optimization techniques. Artificial Bee Colony (ABC) algorithm is the one which can be modeled to solve Portfolio optimization as well. In this paper propose an improved quick Artificial Bee Colony (iqABC) by modified employed bees phase to choose neighborhood solution by using Gbest value. Moreover, the performance of iqABC is compared with the state of art algorithms' performance in portfolio optimization problem.