Conference
International conference on Stochastic Algorithms: foundations and applications
About: International conference on Stochastic Algorithms: foundations and applications is an academic conference. The conference publishes majorly in the area(s): Randomized algorithm & Stochastic programming. Over the lifetime, 104 publications have been published by the conference receiving 4411 citations.
Topics: Randomized algorithm, Stochastic programming, Simulated annealing, Local search (optimization), Metaheuristic
Papers
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26 Oct 2009
TL;DR: In this article, a new Firefly Algorithm (FA) was proposed for multimodal optimization applications. And the proposed FA was compared with other metaheuristic algorithms such as particle swarm optimization (PSO).
Abstract: Nature-inspired algorithms are among the most powerful algorithms for optimization. This paper intends to provide a detailed description of a new Firefly Algorithm (FA) for multimodal optimization applications. We will compare the proposed firefly algorithm with other metaheuristic algorithms such as particle swarm optimization (PSO). Simulations and results indicate that the proposed firefly algorithm is superior to existing metaheuristic algorithms. Finally we will discuss its applications and implications for further research.
3,436 citations
13 Dec 2001
TL;DR: A new heuristics that takes advantage of the structure of the problem in terms of constraints and variables and can guide the search more precisely than a global cost function to optimize (such as for instance the number of violated constraints).
Abstract: We propose a generic, domain-independent local search method called adaptive search for solving Constraint Satisfaction Problems (CSP). We design a new heuristics that takes advantage of the structure of the problem in terms of constraints and variables and can guide the search more precisely than a global cost function to optimize (such as for instance the number of violated constraints). We also use an adaptive memory in the spirit of Tabu Search in order to prevent stagnation in local minima and loops. This method is generic, can apply to a large class of constraints (e.g. linear and non-linear arithmetic constraints, symbolic constraints, etc) and naturally copes with over-constrained problems. Preliminary results on some classical CSP problems show very encouraging performances.
121 citations
22 Sep 2003
TL;DR: It is shown that on rather mild conditions, including that of linear increment of the sample size, the algorithm converges with probability one to the globally optimal solution of the stochastic combinatorial optimization problem, and can usually be recommended for practical application in an unchanged form, i.e., with the ”theoretical” parameter schedule.
Abstract: The paper presents a general-purpose algorithm for solving stochastic combinatorial optimization problems with the expected value of a random variable as objective and deterministic constraints. The algorithm follows the Ant Colony Optimization (ACO) approach and uses Monte-Carlo sampling for estimating the objective. It is shown that on rather mild conditions, including that of linear increment of the sample size, the algorithm converges with probability one to the globally optimal solution of the stochastic combinatorial optimization problem. Contrary to most convergence results for metaheuristics in the deterministic case, the algorithm can usually be recommended for practical application in an unchanged form, i.e., with the ”theoretical” parameter schedule.
103 citations
13 Dec 2001
TL;DR: This paper reports on a simple, decentralized, anytime, stochastic, soft graph-colouring algorithm designed to quickly reduce the number of colour conflicts in large, sparse graphs in a scalable, robust, low-cost manner.
Abstract: This paper reports on a simple, decentralized, anytime, stochastic, soft graph-colouring algorithm. The algorithm is designed to quickly reduce the number of colour conflicts in large, sparse graphs in a scalable, robust, low-cost manner. The algorithm is experimentally evaluated in a framework motivated by its application to resource coordination in large, distributed networks.
75 citations
13 Sep 2007
TL;DR: In this article, it was shown that PPcc is strictly included in UPPcc, which is the same class of complexity classes as UPPCC with weakly restricted bias and unrestricted bias.
Abstract: Many models in theoretical computer science allow for computations or representations where the answer is only slightly biased in the right direction. The best-known of these is the complexity class PP, for "probabilistic polynomial time". A language is in PP if there is a randomized polynomial-time Turing machine whose acceptance probability is greater than 1/2 if, and only if, its input is in the language.
Most computational complexity classes have an analogous class in communication complexity. The class PP in fact has two, a version with weakly restricted bias called PPcc, and a version with unrestricted bias called UPPcc. Ever since their introduction by Babai, Frankl, and Simon in 1986, it has been open whether these classes are the same. We show that PPcc is strictly included in UPPcc. Our proof combines a query complexity separation due to Beigel with a technique of Razborov that translates the acceptance probability of quantum protocols to polynomials. We will discuss some complexity theoretical consequences of this separation. This presentation is bases on joined work with Nikolay Vereshchagin and Ronald de Wolf.
61 citations