Metaheuristic Search Concepts

“…The resulting cost function can be solved with any black box method [37] for discrete optimization -e.g., ant colonies [38]. After discretization of the design parameters, the size of our design space is 4,851, but this can be easily changed in the code by adding or removing levels.…”

Section: Overview Of the Approachmentioning

confidence: 99%

Towards Perceptual Optimization of the Visual Design of Scatterplots

Micallef

Palmas

Oulasvirta

et al. 2017

IEEE Trans. Visual. Comput. Graphics

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Abstract-Designing a good scatterplot can be difficult for non-experts in visualization, because they need to decide on many parameters, such as marker size and opacity, aspect ratio, color, and rendering order. This paper contributes to research exploring the use of perceptual models and quality metrics to set such parameters automatically for enhanced visual quality of a scatterplot. A key consideration in this paper is the construction of a cost function to capture several relevant aspects of the human visual system, examining a scatterplot design for some data analysis task. We show how the cost function can be used in an optimizer to search for the optimal visual design for a user's dataset and task objectives (e.g., "reliable linear correlation estimation is more important than class separation"). The approach is extensible to different analysis tasks. To test its performance in a realistic setting, we pre-calibrated it for correlation estimation, class separation, and outlier detection. The optimizer was able to produce designs that achieved a level of speed and success comparable to that of those using human-designed presets (e.g., in R or MATLAB). Case studies demonstrate that the approach can adapt a design to the data, to reveal patterns without user intervention.

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“…This process is repeated for certain number of iteration called generation in GA. The main GA processes can be summarized as follows [11] :…”

Section: Genetic Algorithmmentioning

confidence: 99%

“…Since ever been introduced, genetic algorithm (GA) [9] have been successfully applied in solving VRPTW [10] [11] . GA operates on a population of solutions as opposite to the classical local search method.…”

Section: Introductionmentioning

confidence: 99%

Multi-parent insertion crossover for vehicle routing problem with time windows

Yassen

Ayob

Nazri

et al. 2012

2012 4th Conference on Data Mining and Optimization (DMO)

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Multi parent crossover has been successfully applied to solve many combinatorial optimization problems such as unconstrained binary quadratic programming problem (UBQP). This because using more than two parents has increased the intensification process by exploiting the information shared by multi parents. However not all type of crossovers are suitable to solve vehicle routing problem (VRP). Therefore, this work introduces a multi parent insertion crossover in solving vehicle routing problem with time windows (VRPTW) by enhancing two parent insertion crossovers. This crossover exchange information among three parents instead of two. Result tested on Solomon VRPTW benchmarks demonstrate that multi parent crossover outperformed two parent crossover on same instances. This prove the effectiveness of having more parents for crossover that can be help the search to find better quality solution.

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Metaheuristic Search Concepts

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Cited by 64 publications

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A computational evaluation of constructive and improvement heuristics for the blocking flow shop to minimise total flowtime

A computational evaluation of constructive and improvement heuristics for the blocking flow shop to minimise total flowtime

Towards Perceptual Optimization of the Visual Design of Scatterplots

Multi-parent insertion crossover for vehicle routing problem with time windows

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