Multi-objective optimization deals with the simultaneous optimization of two or more objectives which are normally in con?ict with each other. Since mul- objective optimization problems are relatively common in real-world appli- tions, this area has become a very popular research topic since the 1970s. However, the use of bio-inspired metaheuristics for solving multi-objective op- mization problems started in the mid-1980s and became popular until the mid- 1990s. Nevertheless, the e?ectiveness of multi-objective evolutionary algorithms has made them very popular in a variety of domains. Swarm intelligence refers to certain population-based metaheuristics that are inspired on the behavior of groups of entities (i.e., living beings) interacting locallywitheachotherandwiththeirenvironment.Suchinteractionsproducean emergentbehaviorthatismodelledinacomputerinordertosolveproblems.The two most popular metaheuristics within swarm intelligence are particle swarm optimization (which simulates a ?ock of birds seeking food) and ant colony optimization (which simulates the behavior of colonies of real ants that leave their nest looking for food). These two metaheuristics havebecome verypopular inthelastfewyears,andhavebeenwidelyusedinavarietyofoptimizationtasks, including some related to data mining and knowledge discovery in databases. However, such work has been mainly focused on single-objective optimization models. The use of multi-objective extensions of swarm intelligence techniques in data mining has been relatively scarce, in spite of their great potential, which constituted the main motivation to produce this book.
Coello Coello / Dehuri / Ghosh
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Weitere Infos & Material
An Introduction to Swarm Intelligence for Multi-objective Problems.- Multi-Criteria Ant Feature Selection Using Fuzzy Classifiers.- Multiobjective Particle Swarm Optimization in Classification-Rule Learning.- Using Multi-Objective Particle Swarm Optimization for Designing Novel Classifiers.- Optimizing Decision Trees Using Multi-objective Particle Swarm Optimization.- A Discrete Particle Swarm for Multi-objective Problems in Polynomial Neural Networks used for Classification: A Data Mining Perspective.- Rigorous Runtime Analysis of Swarm Intelligence Algorithms – An Overview.- Mining Rules: A Parallel Multiobjective Particle Swarm Optimization Approach.- The Basic Principles of Metric Indexing.- Particle Evolutionary Swarm Multi-Objective Optimization for Vehicle Routing Problem with Time Windows.- Combining Correlated Data from Multiple Classifiers.
