Topic
Extremal optimization
About: Extremal optimization is a research topic. Over the lifetime, 1168 publications have been published within this topic receiving 104943 citations.
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01 Jan 2016
TL;DR: The purpose of this chapter is to review a broad range of optimization principles, techniques, and algorithms collectively referred to as heuristic or meta-heuristic as well as some general principles that should be considered in the context of developing nonlinear optimization algorithms and techniques.
Abstract: The purpose of this chapter is to review a broad range of optimization principles, techniques, and algorithms collectively referred to as heuristic or meta-heuristic. As the demand for nonlinear optimization with high levels of component and system detail techniques increases so do the benefits of potential tradeoff between precision and computing speed, which is often attempted through the use of “nature” inspired techniques or heuristic optimization. Although the majority of techniques presented and discussed in this chapter have not generally been applied to structural optimization problems, some are readily available in commercial FE packages. This chapter focuses on the potential of developing each individual technique for nonlinear (topology) optimization; this includes reflecting on results from the previous chapters. This chapter also covers some general principles that are not directly optimization related but should be considered in the context of developing nonlinear optimization algorithms and techniques.
3 citations
TL;DR: The determination of the spatial correlation and the probability distribution of the avalanches show that the Extremal Optimization dynamics does not lead the system into a critical self- organized state and argues that biodiversity is an essential prerequisite to preserve the self-organized criticality.
Abstract: By driving to extinction species that are less or poorly adapted, the Darwinian evolutionary theory is intrinsically an optimization theory. We investigate two optimization algorithms with such evo...
3 citations
14 Aug 2009
TL;DR: Results indicate that the new method proposed by this paper is feasible and effective in soft-sensing of jet fuel endpoint and its capability of strong global search and high immunity against premature convergence is proved.
Abstract: A hybrid algorithm based on Extremal Optimization (EO) with adaptive levy mutation and Differential Evolution (HEODE) was proposed in this paper. It applied the idea of combination mechanism of global and local search. In the process of the global search, DE is an evolutionary algorithm based on the difference in group that can quickly approach a approximate optimal solution. During the local search, as a powerful local search capabilities algorithm EO with adaptive levy mutation helps DE out of local maximum points. Simulation study and its application have proved its capability of strong global search and high immunity against premature convergence. Then HEODE is applied to train artificial neural network to construct a practical soft-sensor of jet fuel endpoint of main fractionator of hydrocracking unit. The obtained results indicate that the new method proposed by this paper is feasible and effective in soft-sensing of jet fuel endpoint.
3 citations
01 Jan 2013
TL;DR: The results of comparison show that ant colony is high efficient than genetic algorithm and it requires less computational cost and generally only a few lines of code.
Abstract: The Travelling Salesman Problem (TSP) is a complex problem in combinatorial optimization. The aim of this study is compare the effect of using two distributed algorithm which are ant colony as a Swarm intelligence algorithm and genetic algorithm. In ant colony algorithm each individual ant constructs a part of the solution using an artificial pheromone which reflects its experience accumulated while solving the problem and heuristic information dependent on the problem. The results of comparison show that ant colony is high efficient than genetic algorithm and it requires less computational cost and generally only a few lines of code.
3 citations
TL;DR: In this article, a two-dimensional model for a co-evolving ecosystem that generalizes the extremal coupled map lattice model is proposed, which takes into account the concept of multiobjective optimization.
Abstract: We propose a two-dimensional model for a co-evolving ecosystem that generalizes the extremal coupled map lattice model. The model takes into account the concept of multiobjective optimization. We find that the system is self-organized into a critical state. The distribution of avalanche sizes follows a power law.
3 citations