Metaheuristic

Summary

In computer science and mathematical optimization, a metaheuristic is a higher-level procedure or heuristic designed to find, generate, tune, or select a heuristic (partial search algorithm) that may provide a sufficiently good solution to an optimization problem or a machine learning problem, especially with incomplete or imperfect information or limited computation capacity. Metaheuristics sample a subset of solutions which is otherwise too large to be completely enumerated or otherwise explored. Metaheuristics may make relatively few assumptions about the optimization problem being solved and so may be usable for a variety of problems. Compared to optimization algorithms and iterative methods, metaheuristics do not guarantee that a globally optimal solution can be found on some class of problems. Many metaheuristics implement some form of stochastic optimization, so

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https://en.wikipedia.org/wiki/Metaheuristic

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Trust and forms of capital in business-to-business activities and relationships

Niki Hynes

The IMP group's ARA model, with its emphasis on interaction, captures social and economic dimensions of exchanges. We draw on it to compare three cases and assess how actors act in skilful and nuanced ways in mobilizing resources, given the very different norms of social and economic capital. We prioritize action ahead of structure and, given the inherent uncertainties that actors encounter in connection with resources, argue that trust is a cognitive and heuristic dimension of action, distinct from resources, actor-bonds and social capital. We conclude that for business practice, managers should be mindful that in order to trust others, they need to act in the distinct realms of social and economic capital, tempting though it is to interpret and measure the costs of actions economically. (c) 2010 Elsevier Inc. All rights reserved.

Elsevier2010

Ant Methaheuristics with Adapted Personalities for Vehicle Routing Problem

Jaime Farres, Rémy Glardon, Nicolas Zufferey

At each generation of an ant algorithm, each ant builds a solution step by step by adding an element to it. Each choice is based on the greedy force (short term profit or heuristic information) and the trail system (central memory which collects information during the search process). Usually, all the ants of the population have the same char- acteristics and behaviors. In contrast in this paper, a new type of ant metaheuristic is proposed. It relies on the use of ants with different per- sonalities. Such a method has been adapted to the well-known vehicle routing problem, and the obtained average results are very encouraging. On one benchmark instance, new best results have been found.

2015

Ant Metaheuristic with Adapted Personalities for the Vehicle Routing Problem

Jaime Farres, Rémy Glardon, Nicolas Zufferey

At each generation of an ant algorithm, each ant builds a solution step by step by adding an element to it. Each choice is based on the greedy force (short term profit or heuristic information) and the trail system (central memory which collects information during the search process). Usually, all the ants of the population have the same characteristics and behaviors. In contrast in this paper, a new type of ant metaheuristic is proposed. It relies on the use of ants with different personalities. Such a method has been adapted to the well-known vehicle routing problem, and even if it does not match the best known results, its performance is encouraging (on one benchmark instance, new best results have however been found), which opens the door to a new ant algorithm paradigm.

Springer Int Publishing Ag2015