Convergence results for continuous-time dynamics arising in ant colony optimization

This paper studies the asymptotic behavior of several continuous-time dynamical systems which are analogs of ant colony optimization algorithms that solve shortest path problems. Local asymptotic stability of the equilibrium corresponding to the shortest path is shown under mild assumptions. A complete study is given for a recently proposed model called EigenAnt: global asymptotic stability is shown, and the speed of convergence is calculated explicitly and shown to be proportional to the difference between the reciprocals of the second shortest and the shortest paths.Comment: A short version of this paper was published in the preprints of the 19th World Congress of the International Federation of Automatic Control, Cape Town, South Africa, 24-29 August 201

Comparison between five stochastic global search algorithms for optimizing thermoelectric generator designs

In this study, the best settings of five heuristics are determined for solving a mixed-integer non-linear multi-objective optimization problem. The algorithms treated in the article are: ant colony optimization, genetic algorithm, particle swarm optimization, differential evolution, and teaching-learning basic algorithm. The optimization problem consists in optimizing the design of a thermoelectric device, based on a model available in literature. Results showed that the inner settings can have different effects on the algorithm performance criteria depending on the algorithm. A formulation based on the weighted sum method is introduced for solving the multiobjective optimization problem with optimal settings. It was found that the five heuristic algorithms have comparable performances. Differential evolution generated the highest number of non-dominated solutions in comparison with the other algorithms

TUNING OPTIMIZATION SOFTWARE PARAMETERS FOR MIXED INTEGER PROGRAMMING PROBLEMS

The tuning of optimization software is of key interest to researchers solving mixed integer programming (MIP) problems. The efficiency of the optimization software can be greatly impacted by the solver’s parameter settings and the structure of the MIP. A designed experiment approach is used to fit a statistical model that would suggest settings of the parameters that provided the largest reduction in the primal integral metric. Tuning exemplars of six and 59 factors (parameters) of optimization software, experimentation takes place on three classes of MIPs: survivable fixed telecommunication network design, a formulation of the support vector machine with the ramp loss and L1-norm regularization, and node packing for coding theory graphs. This research presents and demonstrates a framework for tuning a portfolio of MIP instances to not only obtain good parameter settings used for future instances of the same class of MIPs, but to also gain insights into which parameters and interactions of parameters are significant for that class of MIPs. The framework is used for benchmarking of solvers with tuned parameters on a portfolio of instances. A group screening method provides a way to reduce the number of factors in a design and reduces the time it takes to perform the tuning process. Portfolio benchmarking provides performance information of optimization solvers on a class with instances of a similar structure

Optimisation sous contrainte d'un générateur thermoélectrique pour la récupération de chaleur par différents algorithmes heuristiques

La présente étude porte sur le développement et l’optimisation d’un modèle de générateur thermoélectrique placé sur la surface d’une source de chaleur. La particularité de ce modèle est que la source de chaleur est sujette à un flux de chaleur et à une température de surface fixes. L’objectif principal est de développer un modèle de générateur thermoélectrique d’intérêt dans ce contexte particulier qui pourra s’adapter à différentes sources de chaleur et qui pourra inclure différents systèmes de refroidissement. Le modèle a été créé intégralement à l’aide du logiciel Matlab. Un algorithme génétique multi objectif est ensuite utilisé comme outil d’optimisation afin de maximiser les performances tout en minimisant les coûts du générateur thermoélectrique. Les objectifs d’optimisation proposés sont donc de maximiser la puissance électrique et de minimiser le nombre de modules. Lorsqu’un collecteur thermique est inclus au système, il est aussi nécessaire de minimiser la puissance de pompage et l’aire totale d’échange du collecteur. Une première étude considère uniquement la puissance comme objectif d’optimisation afin d’observer l’impact des contraintes de température et de flux de chaleur de la source sur les designs optimaux. Des cas multiobjectifs seront ensuite étudiés avec les différents objectifs énoncés. Finalement, les performances de différents algorithmes d’optimisation heuristiques seront comparées entre eux en utilisant le modèle thermoélectrique développé comme banc d'essai. Les forces et faiblesses de chaque algorithme seront analysées selon divers critères de performance, lorsqu’appliqués à un cas d’optimisation complexe.This study presents a model of a thermoelectric generator placed directly on the surface of a heat source. One unique feature of this model is that the heat source is subject to fixed heat flux and surface temperature that the system must respect. The main objective is to develop this model in this particular context with the possibility to be adapted to any heat source and the option to add a cooling system. The model has been developed entirely on the software Matlab. Then, a genetic algorithm is used to perform an optimisation in order to find the design with the maximal power output and minimal number of thermoelectric modules. With the cooling system included, the total surface of exchange and pumping power is also considered. A preliminary analysis is conducted to analyse the impact of the heat flux and surface temperature constraint on such system. Thereafter, a multi-objective optimisation is performed to find the optimal design considering multiple optimisation objectives. Finally, different heuristic algorithms are compared for solving the thermoelectric model proposed. The performance is discussed using different performance criteria to show the pros and cons of each heuristic algorithm when solving a complex optimisation design problem