Configuration design of mechanical systems corresponds to finding the placement of a set of components such that performance criteria are optimized while satisfying design constraints. In this paper, the configuration design of a midsize truck for optimum vehicle dynamic behavior, survivability, and maintainability is considered within a multi-criteria decision making framework in which decisio...

The proposed methodology is based on efficient clustering technique for facilitating the decision-maker in the analysis of the solutions of multi-objective problems .Choosing a solution for system implementation from the Pareto-optimal set can be a difficult task, generally because Pareto-optimal sets can be extremely large or even contain an infinite number of solutions. The proposed technique...

We study the problem of allocating a set of indivisible goods to a set of agents having additive preferences. We introduce two new important complexity results concerning efficiency and fairness in resource allocation problems: we prove that the problem of deciding whether a given allocation is Pareto-optimal is coNP-complete, and that the problem of deciding whether there is a Pareto-efficient...

Multicriteria optimization applications can be implemented using Pareto optimization techniques including evolutionary Multicriteria optimization algorithms. Many real world applications involve multiple objective functions and the Pareto front may contain a very large number of points. Choosing a solution from such a large set is potentially intractable for a decision maker. Previous approache...

This paper adresses the problem of diversity in multiobjective evolutionary algorithms and its implications for the quality of the approximated set of efficient solutions (Pareto set). Current approaches for maintaining diversity are classified and related to the overall fitness assignment strategy. The resulting groups of complex selection operators are presented and tested on different object...

Pareto optimality is someway ineffective for optimization problems with several (more than three) objectives. In fact the Pareto optimal set tends to become a wide portion of the whole design domain search space with the increasing of the numbers of objectives. Consequently, little or no help is given to the human decision maker. Here we use fuzzy logic to give two new definitions of optimality...

The quality of solution sets generated by decomposition-based evolutionary multiobjective optimisation (EMO) algorithms depends heavily on the consistency between a given problem’s Pareto front shape and the specified weights’ distribution. A set of weights distributed uniformly in a simplex often lead to a set of well-distributed solutions on a Pareto front with a simplex-like shape, but may f...

We study a concept in multicriteria optimization called compromise solutions (introduced in 1973 by Yu [20]) and a generalized version of this, termed reference point solutions. Our main result shows the power of this concept: Approximating reference point solutions is polynomially equivalent to constructing an approximate Pareto set as in [16]. A reference point solution is the solution closes...

In real world problems, one is often faced with the problem of multiple, possibly competing, goals, which should be optimized simultaneously. These competing goals give rise to a set of compromise solutions, generally denoted as Pareto-optimal. If none of the objectives have preference over the other, none of these trade-off solutions can be said to be better than any other solution in the set....

The Pareto Optimal Model Assessment Cycle (POMAC), a multiple-criteria model assessment methodology, is described for exploring uncertainty in the relationships between ecological theory, model structure, and assessment data. Model performance is optimized to satisfy, simultaneously, each component of a vector of assessment criteria (model outputs), rather than the usual procedure of optimizing...