Swarm intelligence is a relatively new approach to problem solving that takes inspiration from the social behaviors of insects and of other animals In particular, ants have inspired a number of methods and techniques among which the most studied and the most successful is the general purpose optimization technique known as ant colony optimization Ant colony optimization (ACO) takes inspiration from the foraging behavior of some ant species These ants deposit pheromone on the ground in order to mark some favorable path that should be followed by other members of the colony Ant colony optimization exploits a similar mechanism for solving optimization problems From the early nineties, when the first ant colony optimization algorithm was proposed, ACO attracted the attention of increasing numbers of researchers and many successful applications are now available Moreover, a substantial corpus of theoretical results is becoming available that provides useful guidelines to researchers and practitioners in further applications of ACO The goal of this article is to introduce ant colony optimization and to survey its most notable applications

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Ant Colony Optimization

The author systematically examines one of the important issues of information — establishing the market price. He introduces the concept of «search» — where a buyer wanting to get a better price, is forced to question sellers. The article deals with various aspects of finding the necessary information.

Economics of Information

The talk with present the key results of the IPCC Working Group III 5th assessment report. Concluding four years of intense scientific collaboration by hundreds of authors from around the world, the report responds to the request of the world's governments for a comprehensive, objective and policy neutral assessment of the current scientific knowledge on mitigating climate change. The report has been extensively reviewed by experts and governments to ensure quality and comprehensiveness.

Climate change 2014 - Mitigation of climate change

This article presents an overview of recent work on ant algorithms, that is, algorithms for discrete optimization that took inspiration from the observation of ant colonies' foraging behavior, and introduces the ant colony optimization (ACO) metaheuristic. In the first part of the article the basic biological findings on real ants are reviewed and their artificial counterparts as well as the ACO metaheuristic are defined. In the second part of the article a number of applications of ACO algorithms to combinatorial optimization and routing in communications networks are described. We conclude with a discussion of related work and of some of the most important aspects of the ACO metaheuristic.

http://www.iro.umontreal.ca/~dift6751/paper_aco.pdf

Ant algorithms for discrete optimization

http://www-lia.deis.unibo.it/Courses/SistInt/articoli/max-min-ant.pdf

MAX-MIN Ant system

This paper considers the vehicle routing problem with stochastic demands under optimal restocking. We develop an exact algorithm that is effective for solving instances with many vehicles and few c...

New exact algorithm for the vehicle routing problem with stochastic demands

On the generalization of constraint programming and boolean satisfiability solving techniques to schedule a resource-constrained project consisting of multi-mode jobs

In this paper, a Stochastic Dynamic Facility Location Problem (SDFLP) is formulated. In the first part, an exact solution method based on stochastic dynamic programming is given. It is only usable for small instances. In the second part a Monte Carlo based method for solving larger instances is applied, which is derived from the Sample Average Approximation (SAA) method.

Dynamic facility location with stochastic demands

This paper deals with the one-dimensional bin packing problem and presents a metaheuristic solution approach based on Ant Colony Optimization. Some novel algorithm design features are proposed and the comprehensive computational study performed, shows both the contribution of using these features as well as the overall quality of the approach as compared to state of the art competing metaheuristics.

AntPacking: An Ant colony optimization approach for the one-dimensional bin packing problem

In this paper we consider the dynamic behavior of a firm that is subject to environmental regulation. It is assumed that, in order to prevent firms from polluting the environment excessively, the government imposes an emissions tax. We determine how an emissions tax influences the firm's decisions concerning investments and abatement efforts. In the model we incorporate the realistic property that a given abatement expenditure leads to more pollution reduction when pollution is large. This property implies increasing returns to scale with respect to pollution reduction. It turns out that, together with the “usual” assumption of decreasing returns to scale with respect to production, this property leads to the occurrence of history-dependent equilibria in case the pollution tax rate is sufficiently large. It is possible to derive an explicit formula for the threshold tax rate above which these history-dependent equilibria can occur. We show that an investment grant by the government can influence the firm so as to approach the equilibrium with a higher capital stock. Finally, we compare our results with those of a related model where the firm faces a strict pollution standard rather than an emissions tax. Among other things, we show that growth is more suppressed under a tax than under a standard when the firm is small.

/pdf/capital-accumulation-of-a-firm-facing-an-emissions-tax-47j9m1xyab.pdf

Richard F. Hartl

Papers

New exact algorithm for the vehicle routing problem with stochastic demands

On the generalization of constraint programming and boolean satisfiability solving techniques to schedule a resource-constrained project consisting of multi-mode jobs

Dynamic facility location with stochastic demands

AntPacking: An Ant colony optimization approach for the one-dimensional bin packing problem

Capital accumulation of a firm facing an emissions tax