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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TL;DR: Wang et al. as discussed by the authors used the Gannet optimization algorithm (GOA) to optimize the traveling salesman optimization problem (TSP), and the experimental results showed that GOA can find a better solution with less computation time.
Abstract: With the high level of information technology in modern society, a series of intelligent optimization algorithms have emerged to solve classic multi-combinatorial optimization applications. The origin of intelligent algorithms is the intelligent behavior and physical phenomenon of biological communities in nature, and a large number of intelligent optimization algorithms are widely used in various combinatorial optimization problems. Gannet optimization algorithm (GOA) is a newly proposed intelligent optimization algorithm, which is applied to large-scale constrained optimization problems with the advantages of high convergence and high-quality solutions. For the traveling salesman optimization problem (TSP), the original traditional way is very difficult to calculate. The calculation difficulty increases exponentially with the increase in the number of cities and is rarely used in real life. In this paper, we use the GOA to optimize the TSP problem. Experiments are carried out through two TSP instances, it can be seen from the experimental results that GOA can find a better solution with less computation time.
Proceedings Article•
01 Jan 1992TL;DR: The new hyperplane annealing technique is used to solve instances of the NP-complete graph coloring problem with smaller computational complexity than mean fieldAnnealing.
Abstract: This paper introduces a new optimization technique called hyperplane annealing. It is similar to the mean field annealing approach to combinatorial optimization. Both annealing techniques rely on a parallel relaxation dynamic. A connection is shown to a formal model of selforganized pattern formation, the activator-inhibitormodel. The unifying principle of all these relaxation models is a mechanism for the modulation of the nonlinearity of the relaxation dynamic: Activator-inhibitor-systems show spatial modulation by diffusion, whereas the optimization approach uses functional modulation by gradients as well as temporal modulation by annealing of nonlinearity controlling system parameters. The new hyperplane annealing technique combines these modulation mechanisms within an algorithmic formulation with smaller computational complexity than mean field annealing. At a critical nonlinearity a phase transition leads to selforganized pattern formation in the relaxation matrix with the resulting structure corresponding to a solution of the optimization problem. As a concrete example the hyperplane annealing technique is used to solve instances of the NP-complete graph coloring problem.
01 Jan 2016
TL;DR: This paper presents the concepts of three evolutionary algorithms i.e, genetic algorithm, ant colony optimization and particle swarm optimization algorithm which provides solution to various optimization problems.
Abstract: This paper presents the concepts of three evolutionary algorithms i.e, genetic algorithm, ant colony optimization and particle swarm optimization algorithm. An evolutionary algorithm copies the way how evolution occurs in the nature . There are various types of evolutionary algorithms. This paper focuses on Genetic, ACO and PSO algorithms. Genetic algorithm provides solution to various optimization problems. It follows the principle of "survival of the fittest". Various problems such as knapsack problem, TSP(travelling salesman problem) can be solved using genetic algorithm. Ant colony optimization is a heuristic algorithm which follows the behavior of ants i.e, the way ants seek food in their environment by starting from their nest. Particle swarm optimization algorithm(PSO) is also an optimization algorithm which also uses a method of searching using some heuristics.
15 Jun 2004
TL;DR: The combinatorial optimization problem which is NP-complete in M-TSP is solved by enhanced ant algorithm, and simulations on some different dimensions of TSP examples have shown that the ant algorithm has effective convergence with good robustness and is supposed to be practical.
Abstract: The structure of logistic routine problem described corresponding to the structure and parameters of M-TSP is studied. The optimizing method of the main logistic routine problem is brought forward. To deal with the M-TSP, which has the constrains and optimizing objectives of logistic routine problem, the optimizing methods are presented. The combinatorial optimization problem which is NP-complete in M-TSP is solved by enhanced ant algorithm. Simulations on some different dimensions of TSP examples have shown that the ant algorithm has effective convergence with good robustness and is supposed to be practical.
Journal Article•
TL;DR: A new adapted extremal optimization (AEO) for the materialized views selection problem for the development of databases in general and data warehouses in particular is proposed.
Abstract: the development of databases in general and data warehouses in particular, it is now of a great importance to reduce the administration tasks of data warehouses. The materialization of views is one of the most important optimization techniques. The construction of a configuration of views optimizing the data warehouse is an NP-hard problem. On the other hand, the algorithm called extremal optimization is used to solve complex problems. In this paper, we propose a new adapted extremal optimization (AEO) for the materialized views selection problem.