8,161 research outputs found
A statistical learning based approach for parameter fine-tuning of metaheuristics
Metaheuristics are approximation methods used to solve combinatorial optimization problems. Their performance usually depends on a set of parameters that need to be adjusted. The selection of appropriate parameter values causes a loss of efficiency, as it requires time, and advanced analytical and problem-specific skills. This paper provides an overview of the principal approaches to tackle the Parameter Setting Problem, focusing on the statistical procedures employed so far by the scientific community. In addition, a novel methodology is proposed, which is tested using an already existing algorithm for solving the Multi-Depot Vehicle Routing Problem.Peer ReviewedPostprint (published version
On the use of reference points for the biobjective Inventory Routing Problem
The article presents a study on the biobjective inventory routing problem.
Contrary to most previous research, the problem is treated as a true
multi-objective optimization problem, with the goal of identifying
Pareto-optimal solutions. Due to the hardness of the problem at hand, a
reference point based optimization approach is presented and implemented into
an optimization and decision support system, which allows for the computation
of a true subset of the optimal outcomes. Experimental investigation involving
local search metaheuristics are conducted on benchmark data, and numerical
results are reported and analyzed
Analyzing the Effect of Objective Correlation on the Efficient Set of MNK-Landscapes
In multiobjective combinatorial optimization, there exists two main classes
of metaheuristics, based either on multiple aggregations, or on a dominance
relation. As in the single objective case, the structure of the search space
can explain the difficulty for multiobjective metaheuristics, and guide the
design of such methods. In this work we analyze the properties of
multiobjective combinatorial search spaces. In particular, we focus on the
features related the efficient set, and we pay a particular attention to the
correlation between objectives. Few benchmark takes such objective correlation
into account. Here, we define a general method to design multiobjective
problems with correlation. As an example, we extend the well-known
multiobjective NK-landscapes. By measuring different properties of the search
space, we show the importance of considering the objective correlation on the
design of metaheuristics.Comment: Learning and Intelligent OptimizatioN Conference (LION 5), Rome :
Italy (2011
Non-linear great deluge with learning mechanism for solving the course timetabling problem
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