134 research outputs found
System optimization by multiobjective genetic algorithms and analysis of the coupling between variables, constraints and objectives
This paper presents a methodology based on Multiobjective Genetic Algorithms (MOGA’s) for the design of electrical engineering systems. MOGA’s allow to optimize multiple heterogeneous criteria in complex systems, but also simplify couplings and sensitivity analysis by determining the evolution of design variables along the Pareto-optimal front. A rather simplified case study dealing with the optimal dimensioning of an inverter – permanent magnet motor – reducer – load association is carried out to demonstrate the interest of the approach
An evaluation of best compromise search in graphs
This work evaluates two different approaches for multicriteria graph
search problems using compromise preferences. This approach focuses search on
a single solution that represents a balanced tradeoff between objectives, rather
than on the whole set of Pareto optimal solutions. We review the main concepts
underlying compromise preferences, and two main approaches proposed for their
solution in heuristic graph problems: naive Pareto search (NAMOA
), and a k-shortest-path approach (kA
). The performance of both approaches is evaluated
on sets of standard bicriterion road map problems. The experiments reveal that
the k-shortest-path approach looses effectiveness in favor of naive Pareto search
as graph size increases. The reasons for this behavior are analyzed and discussedPartially funded by P07-TIC-03018, Cons. InnovaciĂłn, Ciencia y
Empresa (Junta AndalucĂa), and Univ. Málaga, Campus Excel. Int. AndalucĂa Tec
Enhanced Multi-Objective A* with Partial Expansion
The Multi-Objective Shortest Path Problem (MO-SPP), typically posed on a
graph, determines a set of paths from a start vertex to a destination vertex
while optimizing multiple objectives. In general, there does not exist a single
solution path that can simultaneously optimize all the objectives and the
problem thus seeks to find a set of so-called Pareto-optimal solutions. To
address this problem, several Multi-Objective A* (MOA*) algorithms were
recently developed to quickly compute solutions with quality guarantees.
However, these MOA* algorithms often suffer from high memory usage, especially
when the branching factor (i.e. the number of neighbors of any vertex) of the
graph is large. This work thus aims at reducing the high memory consumption of
MOA* with little increase in the runtime. By generalizing and unifying several
single- and multi-objective search algorithms, we develop the Runtime and
Memory Efficient MOA* (RME-MOA*) approach, which can balance between runtime
and memory efficiency by tuning two user-defined hyper-parameters.Comment: 8 pages, 4 figure
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