Multiobjective Optimization Task

AKA: Multi-Criteria Optimization Task, Multi-Attribute Optimization Task, Multi-Objective Task.
See: Multiobjective Evolutionary Algorithm.

References

http://en.wikipedia.org/wiki/Multiobjective_optimization
- Multi-objective optimization (or programming),[1][2] also known as multi-criteria or multi-attribute optimization, is the process of simultaneously optimizing two or more conflicting objectives subject to certain constraints.
- Multiobjective optimization problems can be found in various fields: product and process design, finance, aircraft design, the oil and gas industry, automobile design, or wherever optimal decisions need to be taken in the presence of trade-offs between two or more conflicting objectives. Maximizing profit and minimizing the cost of a product; maximizing performance and minimizing fuel consumption of a vehicle; and minimizing weight while maximizing the strength of a particular component are examples of multi-objective optimization problems.
- If a multiobjective problem is well formed, there should not be a single solution that simultaneously minimizes each objective to its fullest. In each case we are looking for a solution for which each objective has been optimized to the extent that if we try to optimize it any further, then the other objective(s) will suffer as a result. Finding such a solution, and quantifying how much better this solution is compared to other such solutions (there will generally be many) is the goal when setting up and solving a multiobjective optimization problem.

(Coello Coello et al., 2007) ⇒ Carlos A. Coello Coello, Gary B. Lamont, and David A. Van Veldhuizen. (2007). “Evolutionary Algorithms for Solving Multi-Objective Problems, 2nd edition." Springer. ISBN:0387332545
- Synopsis: Solving multi-objective problems is an evolving effort, and computer science and other related disciplines have given rise to many powerful deterministic and stochastic techniques for addressing these large-dimensional optimization problems. Evolutionary algorithms are one such generic stochastic approach that has proven to be successful and widely applicable in solving both single-objective and multi-objective problems.