(from book Applied Evolutionary Algorithms in Java)
It is a fact of life that we rarely, if ever, achieve all our current goals, since multiple goals of any agent are frequiently in conflict with each other (a robot to take the shortest route to the target and the need to safely avoid obstacles en route) Most real-world applications for EA methods contain conflicting sets of optimisation criteria; hence we require some means for achieving multiobjective optimisation.
Given some set of competing performance criteria such as resource efficiency, cost, and speed, when no further improvement can be made in one without degrading the quality of another, then the system is said to be Pareto-optimal.
The approaches to multimobjective optimisation can be divided into three categories:
(1) Plain aggregating approaches. Objectives are numerically combined into a single objective function to be optimised (i.e weighted sum approach)
(2) Population-based non-Pareto approaches. Different objectives affect the selection or deselection of different parts of the population in turn (sort of tabu search?)
(3) Pareto-based approaches. The population is ranked, making direct use of the definition of Pareto dominance.
– Genetic Algorithms for Multiobjective Optimization: Formulation, Discussion and Generalization. Carlos M. Fonseca, Peter J. Fleming. Genetic Algorithms: Proceedings of the Fifth International Conference (1993)
– An Overview of Evolutionary Algorithms in Multiobjective Optimization Carlos M. Fonseca, Peter J. Fleming. Evolutionary Computation (1995)