831 research outputs found
Optimising a nonlinear utility function in multi-objective integer programming
In this paper we develop an algorithm to optimise a nonlinear utility
function of multiple objectives over the integer efficient set. Our approach is
based on identifying and updating bounds on the individual objectives as well
as the optimal utility value. This is done using already known solutions,
linear programming relaxations, utility function inversion, and integer
programming. We develop a general optimisation algorithm for use with k
objectives, and we illustrate our approach using a tri-objective integer
programming problem.Comment: 11 pages, 2 tables; v3: minor revisions, to appear in Journal of
Global Optimizatio
Multi-objective integer programming: An improved recursive algorithm
This paper introduces an improved recursive algorithm to generate the set of
all nondominated objective vectors for the Multi-Objective Integer Programming
(MOIP) problem. We significantly improve the earlier recursive algorithm of
\"Ozlen and Azizo\u{g}lu by using the set of already solved subproblems and
their solutions to avoid solving a large number of IPs. A numerical example is
presented to explain the workings of the algorithm, and we conduct a series of
computational experiments to show the savings that can be obtained. As our
experiments show, the improvement becomes more significant as the problems grow
larger in terms of the number of objectives.Comment: 11 pages, 6 tables; v2: added more details and a computational stud
On the representation of the search region in multi-objective optimization
Given a finite set of feasible points of a multi-objective optimization
(MOO) problem, the search region corresponds to the part of the objective space
containing all the points that are not dominated by any point of , i.e. the
part of the objective space which may contain further nondominated points. In
this paper, we consider a representation of the search region by a set of tight
local upper bounds (in the minimization case) that can be derived from the
points of . Local upper bounds play an important role in methods for
generating or approximating the nondominated set of an MOO problem, yet few
works in the field of MOO address their efficient incremental determination. We
relate this issue to the state of the art in computational geometry and provide
several equivalent definitions of local upper bounds that are meaningful in
MOO. We discuss the complexity of this representation in arbitrary dimension,
which yields an improved upper bound on the number of solver calls in
epsilon-constraint-like methods to generate the nondominated set of a discrete
MOO problem. We analyze and enhance a first incremental approach which operates
by eliminating redundancies among local upper bounds. We also study some
properties of local upper bounds, especially concerning the issue of redundant
local upper bounds, that give rise to a new incremental approach which avoids
such redundancies. Finally, the complexities of the incremental approaches are
compared from the theoretical and empirical points of view.Comment: 27 pages, to appear in European Journal of Operational Researc
Disjunctive programming for multiobjective discrete optimisation
In this paper, I view and present the multiobjective discrete optimisation problem as a particular case of disjunctive programming where one seeks to identify efficient solutions from within a disjunction formed by a set of systems. The proposed approach lends itself to a simple yet effective iterative algorithm that is able to yield the set of all nondominated points, both supported and nonsupported, for a multiobjective discrete optimisation problem. Each iteration of the algorithm is a series of feasibility checks and requires only one formulation to be solved to optimality that has the same number of integer variables as that of the single objective formulation of the problem. The application of the algorithm show that it is particularly effective, and superior to the state-of-the-art, when solving constrained multiobjective discrete optimisation problem instances
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