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

In this paper we propose the application of Pareto ant colony optimization (PACO) in solving a bi-objective capacitated vehicle routing problem with route balancing (CVRPRB) The objectives of the problem are minimization of the tour length and balancing the routes We propose PACO as our response to the deficiency of the Pareto-based local search (P-LS) approach, which we also developed to solve CVRPRB The deficiency of P-LS is the lack of information flow among its pools of solutions PACO is a natural choice in addressing this deficiency since PACO and P-LS are similar in structure It resolves the absence of information flow through its pheromone values Several test instances are used to demonstrate the contribution and importance of information flow among the pools of solutions Computational results show that PACO improves P-LS in most instances with respect to different performance metrics

http://prolog.univie.ac.at/research/publications/downloads/Pas_2007_330.pdf

Solving a bi-objective vehicle routing problem by Pareto-ant colony optimization

Optimal Control and Dynamic Games

This paper considers a firm, which has to acquire a certain amount of pollution control equipment in order to comply with government pollution standards. Due to political battles and lobbying efforts, the compliance date and the date when it is announced are not known in advance. Furthermore, the target stock of pollution control equipment is also unknown. This amounts to an optimal control problem, in which the terminal time and the terminal state are random variables for which certain probability distributions can be estimated. The model explicitly considers technological progress in the production of abatement equipment, which leads to decreasing installation costs over time. Furthermore it is assumed that having a high stock of abatement equipment at early stages improves the firm's public image and thus its revenue. Tax deductions and other benefits for health conscious firms are also taken into account.

Using optimal control theory the optimal investment in pollution control equipment is obtained, and the sensitivity with respect to discounting, wear out, technological progress and with respect to the parameters of the probability distributions is investigated.

Optimal Acquisition of Pollution Control Equipment Under Uncertainty

Optimal periodic development of a pollution generating tourism industry

In many routing applications, it is necessary to place limits on the duration of the individual routes. When demands are stochastic and restocking during route execution is allowed, the durations o...

Richard F. Hartl

Papers

Solving a bi-objective vehicle routing problem by Pareto-ant colony optimization

Optimal Control and Dynamic Games

Optimal Acquisition of Pollution Control Equipment Under Uncertainty

Optimal periodic development of a pollution generating tourism industry

A branch-and-price algorithm for the vehicle routing problem with stochastic demands and probabilistic duration constraints