Evolutionary Algorithms for Multiobjective Optimization with Applications in Porcfolio Optimization

Most ,real world problems involve simultaneously optimizingmultiobjectives where considerations of trade-offs is important. In general,multiobjective optimization requires more computational’ effort thansingle-objective ’optimization. Unless preferences are irrelevant ’. orcompletely understood, solution of several single objective problems maybe necessary to obtain an acceptable final solution. In this chapter some. MOO. methods ’have been presented. A brief description of their mainalgorithms has been discussed. Furthermore, the advantages anddisadvantages of all methods have been summarized.