3,328 research outputs found
A Hierachical Evolutionary Algorithm for Multiobjective Optimization in IMRT
Purpose: Current inverse planning methods for IMRT are limited because they
are not designed to explore the trade-offs between the competing objectives
between the tumor and normal tissues. Our goal was to develop an efficient
multiobjective optimization algorithm that was flexible enough to handle any
form of objective function and that resulted in a set of Pareto optimal plans.
Methods: We developed a hierarchical evolutionary multiobjective algorithm
designed to quickly generate a diverse Pareto optimal set of IMRT plans that
meet all clinical constraints and reflect the trade-offs in the plans. The top
level of the hierarchical algorithm is a multiobjective evolutionary algorithm
(MOEA). The genes of the individuals generated in the MOEA are the parameters
that define the penalty function minimized during an accelerated deterministic
IMRT optimization that represents the bottom level of the hierarchy. The MOEA
incorporates clinical criteria to restrict the search space through protocol
objectives and then uses Pareto optimality among the fitness objectives to
select individuals.
Results: Acceleration techniques implemented on both levels of the
hierarchical algorithm resulted in short, practical runtimes for optimizations.
The MOEA improvements were evaluated for example prostate cases with one target
and two OARs. The modified MOEA dominated 11.3% of plans using a standard
genetic algorithm package. By implementing domination advantage and protocol
objectives, small diverse populations of clinically acceptable plans that were
only dominated 0.2% by the Pareto front could be generated in a fraction of an
hour.
Conclusions: Our MOEA produces a diverse Pareto optimal set of plans that
meet all dosimetric protocol criteria in a feasible amount of time. It
optimizes not only beamlet intensities but also objective function parameters
on a patient-specific basis
Bat Algorithm for Multi-objective Optimisation
Engineering optimization is typically multiobjective and multidisciplinary
with complex constraints, and the solution of such complex problems requires
efficient optimization algorithms. Recently, Xin-She Yang proposed a
bat-inspired algorithm for solving nonlinear, global optimisation problems. In
this paper, we extend this algorithm to solve multiobjective optimisation
problems. The proposed multiobjective bat algorithm (MOBA) is first validated
against a subset of test functions, and then applied to solve multiobjective
design problems such as welded beam design. Simulation results suggest that the
proposed algorithm works efficiently.Comment: 12 pages. arXiv admin note: text overlap with arXiv:1004.417
A bi-objective genetic algorithm approach to risk mitigation in project scheduling
A problem of risk mitigation in project scheduling is formulated as a bi-objective optimization problem, where the expected makespan and the expected total cost are both to be minimized. The expected total cost is the sum of four cost components: overhead cost, activity execution cost, cost of reducing risks and penalty cost for tardiness. Risks for activities are predefined. For each risk at an activity, various levels are defined, which correspond to the results of different preventive measures. Only those risks with a probable impact on the duration of the related activity are considered here. Impacts of risks are not only accounted for through the expected makespan but are also translated into cost and thus have an impact on the expected total cost. An MIP model and a heuristic solution approach based on genetic algorithms (GAs) is proposed. The experiments conducted indicate that GAs provide a fast and effective solution approach to the problem. For smaller problems, the results obtained by the GA are very good. For larger problems, there is room for improvement
MaaSim: A Liveability Simulation for Improving the Quality of Life in Cities
Urbanism is no longer planned on paper thanks to powerful models and 3D
simulation platforms. However, current work is not open to the public and lacks
an optimisation agent that could help in decision making. This paper describes
the creation of an open-source simulation based on an existing Dutch
liveability score with a built-in AI module. Features are selected using
feature engineering and Random Forests. Then, a modified scoring function is
built based on the former liveability classes. The score is predicted using
Random Forest for regression and achieved a recall of 0.83 with 10-fold
cross-validation. Afterwards, Exploratory Factor Analysis is applied to select
the actions present in the model. The resulting indicators are divided into 5
groups, and 12 actions are generated. The performance of four optimisation
algorithms is compared, namely NSGA-II, PAES, SPEA2 and eps-MOEA, on three
established criteria of quality: cardinality, the spread of the solutions,
spacing, and the resulting score and number of turns. Although all four
algorithms show different strengths, eps-MOEA is selected to be the most
suitable for this problem. Ultimately, the simulation incorporates the model
and the selected AI module in a GUI written in the Kivy framework for Python.
Tests performed on users show positive responses and encourage further
initiatives towards joining technology and public applications.Comment: 16 page
Quality Measures of Parameter Tuning for Aggregated Multi-Objective Temporal Planning
Parameter tuning is recognized today as a crucial ingredient when tackling an
optimization problem. Several meta-optimization methods have been proposed to
find the best parameter set for a given optimization algorithm and (set of)
problem instances. When the objective of the optimization is some scalar
quality of the solution given by the target algorithm, this quality is also
used as the basis for the quality of parameter sets. But in the case of
multi-objective optimization by aggregation, the set of solutions is given by
several single-objective runs with different weights on the objectives, and it
turns out that the hypervolume of the final population of each single-objective
run might be a better indicator of the global performance of the aggregation
method than the best fitness in its population. This paper discusses this issue
on a case study in multi-objective temporal planning using the evolutionary
planner DaE-YAHSP and the meta-optimizer ParamILS. The results clearly show how
ParamILS makes a difference between both approaches, and demonstrate that
indeed, in this context, using the hypervolume indicator as ParamILS target is
the best choice. Other issues pertaining to parameter tuning in the proposed
context are also discussed.Comment: arXiv admin note: substantial text overlap with arXiv:1305.116
A similarity-based cooperative co-evolutionary algorithm for dynamic interval multi-objective optimization problems
The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Dynamic interval multi-objective optimization problems (DI-MOPs) are very common in real-world applications. However, there are few evolutionary algorithms that are suitable for tackling DI-MOPs up to date. A framework of dynamic interval multi-objective cooperative co-evolutionary optimization based on the interval similarity is presented in this paper to handle DI-MOPs. In the framework, a strategy for decomposing decision variables is first proposed, through which all the decision variables are divided into two groups according to the interval similarity between each decision variable and interval parameters. Following that, two sub-populations are utilized to cooperatively optimize decision variables in the two groups. Furthermore, two response strategies, rgb0.00,0.00,0.00i.e., a strategy based on the change intensity and a random mutation strategy, are employed to rapidly track the changing Pareto front of the optimization problem. The proposed algorithm is applied to eight benchmark optimization instances rgb0.00,0.00,0.00as well as a multi-period portfolio selection problem and compared with five state-of-the-art evolutionary algorithms. The experimental results reveal that the proposed algorithm is very competitive on most optimization instances
Multi crteria decision making and its applications : a literature review
This paper presents current techniques used in Multi Criteria Decision Making (MCDM) and their applications. Two basic approaches for MCDM, namely Artificial Intelligence MCDM (AIMCDM) and Classical MCDM (CMCDM) are discussed and investigated. Recent articles from international journals related to MCDM are collected and analyzed to find which approach is more common than the other in MCDM. Also, which area these techniques are applied to. Those articles are appearing in journals for the year 2008 only. This paper provides evidence that currently, both AIMCDM and CMCDM are equally common in MCDM
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. Innovación, Ciencia y
Empresa (Junta Andalucía), and Univ. Málaga, Campus Excel. Int. Andalucía Tec
- …