On the performance assessment and comparison of stochastic multiobjective optimizers

Fonseca, Carlos M.; Fleming, Peter J.

doi:10.1007/3-540-61723-x_1022

Cited by 295 publications

(184 citation statements)

References 2 publications

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“…In Fonseca and Fleming [10], the performance of an SLS algorithm for multiobjective problems is associated with the probability of attaining (dominating or being equal to) an arbitrary point in the objective space in one single run. This function is called attainment function [36] and it can be seen as a generalization of the distribution function of solution cost [1] to the multiobjective case.…”

Section: Performance Assessment Methodologymentioning

confidence: 99%

“…Instead, we employ a sound methodology that follows three steps. In a first step, the outcomes of algorithms are compared pairwise with respect to outperformance relations [9]; if these comparisons do not yield clear conclusions, we compute in a next step the attainment functions to detect significant differences between sets of outcomes [10,11]. If such differences are detected, the usage of graphical illustrations is used in a third step to examine the areas in the objective space where the results of two algorithms differ more strongly [12].…”

Section: Introductionmentioning

confidence: 99%

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Design and analysis of stochastic local search for the multiobjective traveling salesman problem

Paquete

Stützle

2009

Computers & Operations Research

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Stochastic local search (SLS) algorithms are typically composed of a number of different components, each of which should contribute significantly to the final algorithm's performance. If the goal is to design and engineer effective SLS algorithms, the algorithm developer requires some insight into the importance and the behavior of possible algorithmic components. In this paper, we analyze algorithmic components of SLS algorithms for the multiobjective travelling salesman problem. The analysis is done using a careful experimental design for a generic class of SLS algorithms for multiobjective combinatorial optimization. Based on the insights gained, we engineer SLS algorithms for this problem. Experimental results show that these SLS algorithms, despite their conceptual simplicity, outperform a well-known memetic algorithm for a range of benchmark instances with two and three objectives.

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Section: Performance Assessment Methodologymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Design and analysis of stochastic local search for the multiobjective traveling salesman problem

Paquete

Stützle

2009

Computers & Operations Research

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“…The experimentation also includes attainment surfaces (Fonseca and Fleming 1996) to allow an easy visual comparison of the performance of the algorithms. In addition, we also use the robustness visualization model proposed in Chica et al (2013) for answering the question about how robust a Pareto front is.…”

Section: Multiobjective Performance and Robustness Indicatorsmentioning

confidence: 99%

A multiobjective model and evolutionary algorithms for robust time and space assembly line balancing under uncertain demand

Chica

Bautista

Cordón

et al. 2016

Omega

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Changes in demand when manufacturing different products require an optimization model that includes robustness in its definition and methods to deal with it. In this work we propose the r-TSALBP, a multiobjective model for assembly line balancing to search for the most robust line configurations when demand changes. The robust model definition considers a set of demand scenarios and presents temporal and spatial overloads of the stations in the assembly line of the products to be assembled. We present two multiobjective evolutionary algorithms to deal with one of the r-TSALBP variants. The first algorithm uses an additional objective to evaluate the robustness of the solutions. The second algorithm employs a novel adaptive method to evolve separate populations of robust and non-robust solutions during the search. Results show the improvements of using robustness information during the search and the outstanding behavior of the adaptive evolutionary algorithm for solving the problem. Finally, we analyze the managerial impacts of considering the r-TSALBP model for the different organization departments by exploiting the values of the robustness metrics.

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“…We used the hypervolume of the non- dominated space at each generation as a performance metric since DAKOTA uses this metric to guide the optimization. The hypervolume has been shown to be an effective metric in comparing the performance of various EAs and has also been shown to be safer than many other metrics in that it is Pareto-compliant (Fonseca et al, 2005;Zitzler and Thiele, 1999;Minella et al, 2008). Paretocompliancy indicates that the metric is not susceptible to cases where, when comparing two Pareto front approximations, the front the metric identifies as superior is actually the worse of the two.…”

Section: Optimization Performancementioning

confidence: 99%

A decision making framework with MODFLOW-FMP2 via optimization: Determining trade-offs in crop selection

Fowler

Jenkins

Ostrove

et al. 2015

Environmental Modelling & Software

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a b s t r a c tFarmers in regions experiencing water stress or drought conditions can struggle to balance their crop portfolios. Periods of low precipitation often lead to increased, unsustainable reliance on groundwatersupplied irrigation. As a result, regional water management agencies place limits on the amount of water which can be obtained from groundwater, requiring farmers to reduce acreage for more water-intensive crops or remove them from the portfolio entirely. Real-time decisions must be made by the farmer to ensure viability of their farming operation and reduce the impacts associated with limited water resources. Evolutionary algorithms, coupled with accurate, flexible, realistic simulation tools, are ideal mechanisms to allow farmers to assess scenarios with regard to multiple, competing objectives. In order to effective, however, one must be able to select among a variety of simulation tools and optimization algorithms. Many simulation tools allow no access to the source code, and many optimization algorithms are now packaged as part of a suite of tools available to a user. In this work, we describe a framework for integrating these different software components using only their associated input and output streams. We analyze our strategy by coupling a multi-objective genetic algorithm available in the DAKOTA optimization suite (developed and distributed by Sandia National Laboratory) with the MODFLOW-FMP2 simulation tool (developed and distributed by the United States Geological Survey). MODFLOW-FMP2 has been used extensively to model hydrological and farming processes in agriculture-dominated regions, allowing us to represent both farming and conservation interests. We evaluate our integration by considering a case study related to planting decisions facing farmers experiencing water stress. We present numerical results for three competing objectives associated with stakeholders in a given region (i.e., profitability, meeting demand targets, and water conservation). The data obtained from the optimization are robust with respect to algorithmic parameter choices, validating the ability of the associated evolutionary algorithm to perform well without expert guidance. This is integral to our approach, as a motivation for this work is providing decision-making tools. In addition, the results from this study demonstrate that output from the chosen evolutionary algorithm provides a suite of feasible planting scenarios, giving farmers and policy makers the ability to compromise solutions based on realistic simulation data.

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On the performance assessment and comparison of stochastic multiobjective optimizers

Cited by 295 publications

References 2 publications

Design and analysis of stochastic local search for the multiobjective traveling salesman problem

Design and analysis of stochastic local search for the multiobjective traveling salesman problem

A multiobjective model and evolutionary algorithms for robust time and space assembly line balancing under uncertain demand

A decision making framework with MODFLOW-FMP2 via optimization: Determining trade-offs in crop selection

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