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.
