22,863 research outputs found
Efficiency Analysis of Swarm Intelligence and Randomization Techniques
Swarm intelligence has becoming a powerful technique in solving design and
scheduling tasks. Metaheuristic algorithms are an integrated part of this
paradigm, and particle swarm optimization is often viewed as an important
landmark. The outstanding performance and efficiency of swarm-based algorithms
inspired many new developments, though mathematical understanding of
metaheuristics remains partly a mystery. In contrast to the classic
deterministic algorithms, metaheuristics such as PSO always use some form of
randomness, and such randomization now employs various techniques. This paper
intends to review and analyze some of the convergence and efficiency associated
with metaheuristics such as firefly algorithm, random walks, and L\'evy
flights. We will discuss how these techniques are used and their implications
for further research.Comment: 10 pages. arXiv admin note: substantial text overlap with
arXiv:1212.0220, arXiv:1208.0527, arXiv:1003.146
Teaching metaheuristics in business schools
In this work we discuss some ideas and opinions related with teaching Metaheuristics in Business Schools. The main purpose of the work is to initiate a discussion and collaboration about this topic,with the final objective to improve the teaching and publicity of the area. The main topics to be discussed are the environment and focus of this teaching. We also present a SWOT analysis which lead us to the conclusion that the area of Metaheuristics only can win with the presentation and discussion of metaheuristics and related topics in Business Schools, since it consists in a excellent Decision Support tools for future potential users.Metaheuristics, Teaching Business
Metaheuristic algorithms for the simultaneous slot allocation problem
In this paper, we formalize the simultaneous slot allocation problem. It is an extension of the problem currently tackled for allocating airport slots: it deals with all airports simultaneously and it enforces the respect of airspace sector capacities. By solving this novel problem, the system may overcome some major inefficiencies that characterize the current slot allocation process. We tackle the simultaneous slot allocation problem with two algorithms based on metaheuristics, namely Iterated Local Search and Variable Neighborhood Search, and with an integer linear programming model: for each of these three algorithms, we allow a fixed computation time, and we take the best solution found during that time as the final solution. We compare these algorithms on randomly generated instances, and we show that, when small instances are to be tackled, metaheuristics are competitive with the exact model. When medium or large instances are to be tackled, the exact model suffers some major issues in terms of memory and computation time requirements. Metaheuristics, instead, can deal with very large instances, achieving very high quality results.Air Traffic Management; Airport slot allocation; Metaheuristics; Integer linear programming
Analyzing the Effect of Objective Correlation on the Efficient Set of MNK-Landscapes
In multiobjective combinatorial optimization, there exists two main classes
of metaheuristics, based either on multiple aggregations, or on a dominance
relation. As in the single objective case, the structure of the search space
can explain the difficulty for multiobjective metaheuristics, and guide the
design of such methods. In this work we analyze the properties of
multiobjective combinatorial search spaces. In particular, we focus on the
features related the efficient set, and we pay a particular attention to the
correlation between objectives. Few benchmark takes such objective correlation
into account. Here, we define a general method to design multiobjective
problems with correlation. As an example, we extend the well-known
multiobjective NK-landscapes. By measuring different properties of the search
space, we show the importance of considering the objective correlation on the
design of metaheuristics.Comment: Learning and Intelligent OptimizatioN Conference (LION 5), Rome :
Italy (2011
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