Nowadays, various imitations of natural processes are used to solve challenging optimization problems faster and more accurately. Spin glass based optimization, specifically, has shown strong local search capability and parallel processing. However, generally, spin glasses have a low rate of convergence, since they use Monte Carlo simulation techniques such as simulated annealing (SA). Here, we investigate a new hybrid local search method based on spin glass for using adaptive distributed system capability, extremal optimization (EO) for using evolutionary locally search algorithm and SA for escaping from local optimum states. As shown in this paper, this strategy can lead to faster rate of convergence and improved performance than conventional SA and EO algorithm. The resulting are then used to solve the portfolio selection problem that is a non-deterministic polynomial complete (NPC) problem. This is confirmed by test results of five of the world\\\'s major stock markets, reliability test and phase transition diagram.

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