Extremal optimization

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

A novel hybrid penguins search optimization algorithm to solve travelling salesman problem

Abstract: Metaheuristics form a family of optimization algorithms for solving combinatorial optimization problems by applying the research procedures to quickly find a good approximation of the best solution. In this paper we proposed a new metaheuristic novel hybrid penguins search optimization algorithm (NPeSOA) which is based on the combination of penguins search optimization algorithm (PeSOA) and harmony search algorithm (HS) to solve the Travelling Salesman Problem. The search for harmony was added to improve the research technique of PeSOA method. The results of this experience are tested by the instances of TSPLib, and compared with the methods of PeSOA and HS to show the efficiency of NPeSOA.

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 (EO) is also analyzed here. We provided a numerical confirmation that, for any possible value of its free parameter τ, the EO 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.

Optimal protein structure alignment using modified extremal optimization

Abstract: Proteins are important biochemical compounds that have biogenic functions for biological activities. The three-dimensional structures of proteins are closely related to its biological functions, and therefore, techniques for comparing them have been studied. Many of these techniques for comparing protein structures are based on protein structure alignment, which is one of the most effective methods. CMO (Contact Map Overlap) is formulated as combinatorial optimization to find the optimal structure alignments. In this paper, we propose a novel heuristic using Modified Extremal Optimization (MEO) for CMO. Our MEO-based heuristic is characterized by three features. First, the proposed heuristic uses MEO for alternation generations. Second, an initial solution is created by dynamic programming (DP). Third, state transition is executed using the best admissible move strategy.

Combining extremal optimization with singular value decomposition for effective point matching

Abstract: Feature point matching is a key step for most problems in computer vision. It is an ill-posed problem and suffers from combinatorial complexity which becomes even more critical with the increase in data and the presence of outliers. The work covered in this paper describes a new framework to solve this problem in order to achieve robust registration of two feature point sets assumed to be available. This framework combines the use of extremal optimization heuristic with a clever startup routine which exploits some properties of singular value decomposition. The role of the latter is to produce an interesting matching configuration whereas the role of the former is to refine the initial matching by generating hypothetical matches and outliers using a far-from-equilibrium based stochastic rule. Experiments on a wide range of real data have shown the effectiveness of the proposed method and its ability to achieve reliable feature point matching.