Extremal optimization

About: Extremal optimization is a research topic. Over the lifetime, 1168 publications have been published within this topic receiving 104943 citations.

Nonlinear model predictive control with the integration of Support Vector Machine and Extremal optimization

Abstract: In nonlinear model predictive control (NMPC), the system performance is greatly dependent upon the accuracy of the predictive model and the efficiency of the online optimization algorithm. In this paper, a novel NMPC scheme with the integration of Support Vector Machine (SVM) and recently proposed general-purpose heuristic “Extremal Optimization (EO)” is presented. With the superior features of self-organized criticality (SOC), non-equilibrium dynamics, co-evolutions in statistical mechanics and ecosystems respectively, a carefully designed EO based on “horizon based mutation strategy” is used as an online solver to obtain optimal future control inputs of NMPC, in which a multi-step-ahead SVM predictive model is employed. Furthermore, simulation studies on a typical nonlinear system are given to illustrate the effectiveness of the proposed control scheme.

Optimization Models and Heuristic Method Based on Simulated Annealing Strategy for Traveling Salesman Problem

Abstract: The traveling salesman problem (TSP) is a problem in combinatorial optimization studied in operations research and theoretical computer science. In this paper, we presented a novel heuristic simulated annealing algorithm for solving TSP. The algorithm is fully operational in the genetic role of crossover operator, and mutation operator, to achieve a balance between speed and accuracy. The experiment results show that the algorithm is better than the traditional method.

Random constrained pseudo-Boolean optimization algorithm for multiprocessor systems and clusters

Abstract: In this paper, we consider an approach to developing parallel versions of the algorithms based on the modified probability changing method for constrained pseudo-Boolean optimization Optimization algorithms are adapted for the systems with shared memory (OpenMP) and cluster systems (MPI) The parallel efficiency is estimated for the large-scale non-linear pseudo-Boolean optimization problems with linear constraints and traveling salesman problem

Extremal optimization algorithm with adaptive constants dealing techniques for constrained optimization problems

Abstract: Extremal optimization (EO) has been successfully applied to a variety of combinatorial optimization problems. However, the applications of EO to constrained optimization problems are relatively rare. This paper proposes a novel EO algorithm with adaptive constraints dealing techniques called EO-ACD for constrained optimization problems. The basic idea behind EO-ACD is the combination of real-coded EO and adaptive dealing technique of constraints. The experimental results on 11 benchmark test functions have shown that the proposed EO-ACD is competitive or even better than the existing evolutionary algorithms such as population-based EO (PEO), stochastic ranking (SR) algorithm, simple multimembered evolution strategy (SMES) and genetic algorithm with two-phase genetic framework.

Optimization and self-organized criticality in a magnetic system

Abstract: We propose a kind of Bak-Sneppen dynamics as a general optimization technique to treat magnetic systems. The resulting dynamics shows self-organized criticality with power law scaling of the spatial and temporal correlations. An alternative method of the extremal optimization is also analyzed here. We provided a numerical confirmation that, for any possible value of its free parameter $\tau$, the extremal optimization dynamics exhibits a non-critical behavior with an infinite spatial range and exponential decay of the avalanches. Using the chiral clock model as our test system, we compare the efficiency of the two dynamics with regard to their abilities to find the system's ground state.