Statistical mechanics and disordered systems
Communications of the ACM - Lecture notes in computer science Vol. 174
Learning automata: an introduction
Learning automata: an introduction
Learning automata: theory and applications
Learning automata: theory and applications
Dynamics of complex systems
A brief history of cellular automata
ACM Computing Surveys (CSUR)
An updated survey of GA-based multiobjective optimization techniques
ACM Computing Surveys (CSUR)
Evolutionary local-search with extremal optimization
Neural, Parallel & Scientific Computations
New Optimization Algorithms in Physics
New Optimization Algorithms in Physics
Phase Transitions in Combinatorial Optimization Problems - Basics, Algorithms and Statistical Mechanics
From local search to global conclusions: migrating spin glass-based distributed portfolio selection
IEEE Transactions on Evolutionary Computation
Simulated annealing: Practice versus theory
Mathematical and Computer Modelling: An International Journal
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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. But, spin glasses have a low rate of convergence since they use Monte Carlo simulation techniques such as simulated annealing (SA). Here, we propose two algorithms that combine the long range effect in spin glasses with extremal optimization (EO-SA) and learning automata (LA-SA). Instead of arbitrarily flipping spins at each step, these two strategies aim to choose the next spin and selectively exploiting the optimization landscape. As shown in this paper, this selection strategy can lead to faster rate of convergence and improved performance. The resulting two algorithms are then used to solve portfolio selection problem that is a non-polynomial (NP) complete problem. Comparison of test results indicates that the two algorithms, while being very different in strategy, provide similar performance and reach comparable probability distributions for spin selection. Furthermore, experiments show there is no difference in speed of LA-SA or EO-SA for glasses with fewer spins, but EO-SA responds much better than LA-SA for large glasses. This is confirmed by tests results of five of the world's major stock markets. In the last, the convergence speed is compared to other heuristic methods such as Neural Network (NN), Tabu Search (TS), and Genetic Algorithm (GA) to approve the truthfulness of proposed methods.