28,622 research outputs found
A Computational Study of Genetic Crossover Operators for Multi-Objective Vehicle Routing Problem with Soft Time Windows
The article describes an investigation of the effectiveness of genetic
algorithms for multi-objective combinatorial optimization (MOCO) by presenting
an application for the vehicle routing problem with soft time windows. The work
is motivated by the question, if and how the problem structure influences the
effectiveness of different configurations of the genetic algorithm.
Computational results are presented for different classes of vehicle routing
problems, varying in their coverage with time windows, time window size,
distribution and number of customers. The results are compared with a simple,
but effective local search approach for multi-objective combinatorial
optimization problems
A Parallel Divide-and-Conquer based Evolutionary Algorithm for Large-scale Optimization
Large-scale optimization problems that involve thousands of decision
variables have extensively arisen from various industrial areas. As a powerful
optimization tool for many real-world applications, evolutionary algorithms
(EAs) fail to solve the emerging large-scale problems both effectively and
efficiently. In this paper, we propose a novel Divide-and-Conquer (DC) based EA
that can not only produce high-quality solution by solving sub-problems
separately, but also highly utilizes the power of parallel computing by solving
the sub-problems simultaneously. Existing DC-based EAs that were deemed to
enjoy the same advantages of the proposed algorithm, are shown to be
practically incompatible with the parallel computing scheme, unless some
trade-offs are made by compromising the solution quality.Comment: 12 pages, 0 figure
Application of multi-objective optimization to structural damage estimation via model updating
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A new evolutionary search strategy for global optimization of high-dimensional problems
Global optimization of high-dimensional problems in practical applications remains a major challenge to the research community of evolutionary computation. The weakness of randomization-based evolutionary algorithms in searching high-dimensional spaces is demonstrated in this paper. A new strategy, SP-UCI is developed to treat complexity caused by high dimensionalities. This strategy features a slope-based searching kernel and a scheme of maintaining the particle population's capability of searching over the full search space. Examinations of this strategy on a suite of sophisticated composition benchmark functions demonstrate that SP-UCI surpasses two popular algorithms, particle swarm optimizer (PSO) and differential evolution (DE), on high-dimensional problems. Experimental results also corroborate the argument that, in high-dimensional optimization, only problems with well-formative fitness landscapes are solvable, and slope-based schemes are preferable to randomization-based ones. © 2011 Elsevier Inc. All rights reserved
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