Introduction to the Special Issue: Multicriterion Optimization

Multicriterion optimization methods, as the name suggests, deal with finding optimal solutions to problems having multiple objectives. Many real-world search and optimization problems naturally involve multiple objectives, simply because in these problems, a user is never satisfied by finding one solution that is optimum with respect to a single criterion. Conflicting objectives introduce trade-off solutions and make the task complex yet interesting to execute. The principle of a multicriterion optimization procedure is different from that of a single-criterion optimization. In a multicriterion optimization, the presence of conflicting objectives gives rise to a set of optimal solutions (called Pareto-optimal solutions), instead of a single optimal solution. In the absence of any priority towards any particular objective, all Pareto-optimal solutions become equally important to the user. Thus, it becomes essential that a multicriterion optimization algorithm find a wide variety of Pareto-optimal solutions, instead of just one of them.