This paper proposes a new evolutionary algorithm for continuous non-linear optimization problems. This optimization algorithm is inspired by the procedure of trading the shares on stock market and it is called exchange market algorithm (EMA). Evaluation of how the stocks are traded on the stock market by elites has formed this evolutionary as an optimization algorithm. In the proposed method there are two different modes in EMA. In the first mode, there is no oscillation in the market whereas in the second mode, the market has oscillation. It is noticeable that at the end of each mode, the individuals’ finesses are evaluated. For the first mode, the algorithm's duty is to recruit people toward successful individuals, while in the second case the algorithm seeks optimal points. In this algorithm, the generation and organization of random numbers are performed in the best way due to the existence of two absorbent operators and two searching operators leading to high capability in global optimum point extraction. To evaluate the performance of the proposed algorithm, this algorithm has been implemented on 12 different benchmark functions with 10, 20, 30 and 50 dimension variables. The results obtained by 30 dimension variables are compared with the results obtained by the eight new and efficient algorithms. The results indicate the ability of the proposed algorithm in finding the global optimum point of the functions for each run of the program. 
Exchange market algorithm