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 2013
TL;DR: This chapter considers the nonlinear optimization problem for reconstructing the shape of an extended target from multistatic data and introduces iterative algorithms for solving this problem.
Abstract: In this chapter we consider the nonlinear optimization problem for reconstructing the shape of an extended target from multistatic data. Because of the nonlinearity of the problem, iterative algorithms have to be introduced.
5 citations
TL;DR: Several extensions taking into account constraints on the weight system and inverse problems against a specific algorithm are considered under this point of view and devise both complexity and approximation results.
5 citations
01 Jan 2008
TL;DR: This paper chooses the traveling salesman problem as a model combinatorial optimization problem and proposes several greedy algorithms for it based on tolerances, and reports extensive computational experiments that clearly demonstrate that these tolerance-based algorithms outperform their weight-based counterpart.
Abstract: Most research on algorithms for combinatorial optimization use the costs of the elements in the ground set for making decisions about the solutions that the algorithms would output. For traveling salesman problems, this implies that algorithms generally use arc lengths to decide on whether an arc is included in a partial solution or not. In this paper we study the eect of using element tolerances for making these decisions. We choose the traveling salesman problem as a model combinatorial optimization problem and propose several greedy algorithms for it based on tolerances. We report extensive computational experiments on benchmark instances that clearly demonstrate that our tolerance-based algorithms outperform their weight-based counterpart. This indicates that the potential for using tolerance-based algorithms for various optimization problems is high and motivates further investigation of the approach.
5 citations
TL;DR: A new hybrid local search method based on spin glass for using adaptive distributed system capability, extremal optimization (EO) for using evolutionary local search algorithm and SA for escaping from local optimum states and trap to global ones is investigated.
5 citations
16 Jan 2012
TL;DR: In this article, a heuristic optimization module based on Variable Neighborhood Search (VNS) is used to find extremal or near extremal graphs, i.e., graphs that minimize or maximize an invariant.
Abstract: Using a heuristic optimization module based upon Variable Neighborhood Search (VNS), the system AutoGraphiX's main feature is to find extremal or near extremal graphs, i.e., graphs that minimize or maximize an invariant. From the so obtained graphs, conjectures are found either automatically or interactively. Most of the features of the system relies on the optimization that must be efficient but the variety of problems handled by the system makes the tuning of the optimizer difficult to achieve. We propose a learning algorithm that is trained during the optimization of the problem and provides better results than all the algorithms previously used for that purpose.
5 citations