Extremal optimization

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

3D IC optimal layout design. A parallel and distributed topological approach.

Abstract: The task of 3D ICs layout design involves the assembly of millions of components taking into account many different requirements and constraints such as topological, wiring or manufacturability ones. It is a NP-hard problem that requires new non-deterministic and heuristic algorithms. Considering the time complexity, the commonly applied Fiduccia-Mattheyses partitioning algorithm is superior to any other local search method. Nevertheless, it can often miss to reach a quasi-optimal solution in 3D spaces. The presented approach uses an original 3D layout graph partitioning heuristics implemented with use of the extremal optimization method. The goal is to minimize the total wire-length in the chip. In order to improve the time complexity a parallel and distributed Java implementation is applied. Inside one Java Virtual Machine separate optimization algorithms are executed by independent threads. The work may also be shared among different machines by means of The Java Remote Method Invocation system.

An Improved Ant Colony Algorithm Solving Global Optimization Problems

Abstract: Aim to the disadvantages that ant colony optimization is not applied to continuous optimization problems and easy to get into local optimum,a fast global ant colony algorithm is proposed.In this algorithm the searching way that searches near the best solution and makes the best solution as the initial solution is adopted in order to widen searching scope to avoid getting into local optimum,and then it is used to test some typical functions.Comparing with other optimizations,the testing result indicates that the improved algorithm is not only applied to continuous optimization problems,but also has fast global optimization,fast searching rate and high optimizing precision.

A human-machine cooperative approach for combinatorial optimization problem

Abstract: The human-computer cooperative approaches for handling the combinatorial optimization problems are introduced. In this paper we propose the human-computer cooperation approach for combinatorial optimization problems. Firstly, the initial solutions are seen as the sampling in human cognition by computer games. The games are developed for special combinatorial optimization problems. Then fuzzy decomposition decomposes the optimization problem into several sub-problems according to initial solutions by human. Moreover, local search strategy is given in conformational space with human strategy development capabilities, which also explores the space of possible search strategies. Last, the stochastic elements of the search in the traditional computational algorithms are gone with human intelligence during the fuzzy composition process. Human-computer cooperation approach can indeed lead to mutual advantage, and it can improve the problem-solving skills. We apply the proposed method to two-echelon vehicle routing problem to verify its effectiveness and usefulness.

Extremal Optimization for Sherrington-Kirkpatrick Spin Glasses

Abstract: Extremal Optimization (EO), a new local search heuristic, is used to approximate ground states of the mean-field spin glass model introduced by Sherrington and Kirkpatrick. The implementation extends the applicability of EO to systems with highly connected variables. Approximate ground states of sufficient accuracy and with statistical significance are obtained for systems with more than N=1000 variables using $\pm J$ bonds. The data reproduces the well-known Parisi solution for the average ground state energy of the model to about 0.01%, providing a high degree of confidence in the heuristic. The results support to less than 1% accuracy rational values of $\omega=2/3$ for the finite-size correction exponent, and of $\rho=3/4$ for the fluctuation exponent of the ground state energies, neither one of which has been obtained analytically yet. The probability density function for ground state energies is highly skewed and identical within numerical error to the one found for Gaussian bonds. But comparison with infinite-range models of finite connectivity shows that the skewness is connectivity-dependent.

Improved Artificial Bee Colony Algorithms Based on Extremal Optimization Strategy

Abstract: In order to enhance the performance of artificial bee colony algorithm in solving optimization problems,this paper proposed an improved artificial bee colony algorithmThe improved algorithm redesigns local search scheme of onlook bees based on evolution method of extremal optimization strategy,and implements operators of component mutations,formulates rules of worst component judgmentThe simulation results of eight typical functions of optimization problems show that the proposed algorithm can attain significant improvement on accuracy and convergent speed,has a better solution capability,compared with the basic artificial bee colony algorithm and known improved algorithm