Extremal optimization

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

Spatial planning via extremal optimization enhanced by cell-based local search

Abstract: A new treatment is presented for land use planning problems by means of extremal optimization in conjunction to cell-based neighborhood local search. Extremal optimization, inspired by self-organized critical models of evolution has been applied mainly to the solution of classical combinatorial optimization problems. Cell-based local search has been employed by the author elsewhere in problems of spatial resource allocation in combination with genetic algorithms and simulated annealing. In this paper it complements extremal optimization in order to enhance its capacity for a spatial optimization problem. The hybrid method thus formed is compared to methods of the literature on a specific characteristic problem. It yields better results both in terms of objective function values and in terms of compactness. The latter is an important quantity for spatial planning. The present treatment yields significant compactness values as emergent results.

New Evolutionary Genetic Algorithms for Combinatorial Optimization Problems

Abstract: This paper deals with the NP-complete combinatorial optimization problems. A large number of papers has been devoted to them. A traditional approach of Operational Research has been used (Lawler et al. 1985). Kirkpatrick et al. (1983) used Simulated Annealing. Hopfield and Tank (1985) applied a neural networks for finding suboptimal solution for TSP. In recent years interest has raised to apply evolutionary algorithms to combinatorial optimization problems (Holland 1975; Brady 1985).

Microwave imaging based on two hybrid particle swarm optimization approaches

Abstract: In this paper, the solution of the inverse scattering problem for determining the shape and location of perfectly conducting scatterers by making use of electromagnetic scattered fields is presented. Based on the boundary condition and the measured scattered field, a set of nonlinear integral equations is derived and the imaging problem is reformulated into an optimization one. Then, two evolutionary algorithms are used to solve the inverse scattering problem. To further clarify, our contribution is to test two well-known algorithms in the literature to the problem of microwave imaging. The hybrid approaches combine the standard particle swarm optimization (PSO) with the ideas of the simulated annealing and extremal optimization algorithms, respectively. Both of them are shown to be more efficient than original PSO technique. Reconstruction results by using the two presented schemes are compared with exact shapes of some conducting cylinders; and good agreements with the original shapes are observed.