Evolutionary algorithms for multi-objective optimization problems with interval parameters

Gong, Dunwei; Qin, N.; Sun, Xiaoyan

doi:10.1109/bicta.2010.5645160

Cited by 38 publications

(25 citation statements)

References 15 publications

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“…The second is that the user selects the optimal solution based on his/her preferences from the optimization results obtained by IP-MOEA [10], denoted as Method 2. The third is the same as Method 2 except for adopting the approach in [22] to obtain the optimization results, denoted as Method 3.…”

Section: Four Comparative Methodsmentioning

confidence: 99%

“…To be solved, however, is the optimization problem without uncertainties, transformed from the original one. In our recently proposed method for interval MOPs, a novel interval dominance relation is defined to compare different individuals, and a crowding distance based on intervals is presented to further distinguish the individuals with the same rank [22]. In this study, HIMOP is directly solved by using the proposed method in [22].…”

Section: Relation To Our Previous Workmentioning

confidence: 99%

“…This means that this preference relationship preserves the existing dominance relationship and the search would not be biased towards undesired regions of the search space [12]. Based on the above discussion, the detailed sorting scheme is as follows: the interval dominance relationship proposed in [22] is adopted as the primary selection criteria to sort the evolutionary individuals; then those individuals with the same rank are further distinguished based on the user's satisfactory degrees to them. The smaller the rank of an evolutionary individual and the larger the user's satisfactory degree to it, the better its performance.…”

Section: Sorting Scheme Based On User's Preferencesmentioning

confidence: 99%

“…When adopting interval dominance relationship [22] to sort evolutionary individuals, they are grouped into several categories with different ranks assigned. Evolutionary individuals with the same rank are non-dominated, therefore, other indicators, e.g., crowding distance [6], are necessary to distinguish them.…”

Section: Sorting Scheme Based On User's Preferencesmentioning

confidence: 99%

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Evolutionary algorithms with user’s preferences for solving hybrid interval multi-objective optimization problems

Gong

Liu

et al. 2015

Appl Intell

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Hybrid interval multi-objective optimization problems are common in real-world applications. These problems involve both explicit and implicit objectives, and the values of these objectives are intervals. Few previous methods are suitable for them. An evolutionary algorithm with a large population and a user's interval preferences was presented to effectively solve the problems in this paper. In the proposed algorithm, a similarity-based strategy was employed to estimate the interval values of implicit objectives of evolutionary individuals that the user had not evaluated in order to alleviate user fatigue; the user's preferences to different objectives were expressed precisely as intervals by solving an auxiliary optimization problem; a sorting scheme based on the user's preferences was proposed to guide the population evolving toward the user's preferred regions. We applied the proposed method to an interior layout problem, which is a typical optimization problem with both interval parameters in the explicit objective and interval value of the implicit objective. The proposed algorithm was compared with four other optimization algorithms on the interior layout problem. Experimental results validated its effectiveness and superiority over the compared algorithms in terms of solution quality and the number of user's evaluations.

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Section: Four Comparative Methodsmentioning

confidence: 99%

Section: Relation To Our Previous Workmentioning

confidence: 99%

Section: Sorting Scheme Based On User's Preferencesmentioning

confidence: 99%

Section: Sorting Scheme Based On User's Preferencesmentioning

confidence: 99%

See 2 more Smart Citations

Evolutionary algorithms with user’s preferences for solving hybrid interval multi-objective optimization problems

Gong

Liu

et al. 2015

Appl Intell

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“…We do the simulations using the proposed method and the method in [19], and give the results to compare the effectiveness of the two methods. The proposed algorithm is run 30 times independently, and the statistical results are listed in TABLE I.…”

Section: A Interval Dominationmentioning

confidence: 99%

Robot path planning in an environment with many terrains based on interval multi-objective PSO

Geng

Gong

Zhang

2013

2013 IEEE Congress on Evolutionary Computation

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In order to solve the problem of path planning in an environment with many terrains, we propose a method based on interval multi-objective Particle Swarm Optimization (PSO). First, the environment is modeled by the line partition method, and then, according to the distribution of the polygonal lines which form the robot path and taking the velocity's disturbance into consideration, robot's passing time is formulated as an interval by combining Local Optimal Criterion (LOC), and the path's danger degree is estimated through the area ratio between the robot path and the danger source. In addition, the path length is also calculated as an optimization objective. As a result, the robot path planning problem is modeled as an optimization problem with three objectives. Finally, the interval multiobjective PSO is employed to solve the problem above. Simulation and experimental results verify the effectiveness of the proposed method.

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Non‐parametric measure approximations for constrained multi‐objective optimisation under uncertainty

Rivier,

Razaaly,

Congedo

2024

Numerical Meth Engineering

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In this article, we propose non‐parametric estimations of robustness and reliability measures approximation error, employed in the context of constrained multi‐objective optimisation under uncertainty (OUU). These approximations with tunable accuracy permit to capture the Pareto front in a parsimonious way, and can be exploited within an adaptive refinement strategy. First, we illustrate an efficient approach for obtaining joint representations of the robustness and reliability measures, allowing sharper discrimination of Pareto‐optimal designs. A specific surrogate model of these objectives and constraints is then proposed to accelerate the optimisation process. Secondly, we propose an adaptive refinement strategy, using these tunable accuracy approximations to drive the computational effort towards the computation of the optimal area. To this extent, an adapted Pareto dominance rule and Pareto optimal probability computation are formulated. The performance of the proposed strategy is assessed on several analytical test‐cases against classical approaches. We also illustrate the method on an engineering application, performing shape OUU of an Organic Rankine Cycle turbine.

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Evolutionary algorithms for multi-objective optimization problems with interval parameters

Cited by 38 publications

References 15 publications

Evolutionary algorithms with user’s preferences for solving hybrid interval multi-objective optimization problems

Evolutionary algorithms with user’s preferences for solving hybrid interval multi-objective optimization problems

Robot path planning in an environment with many terrains based on interval multi-objective PSO

Non‐parametric measure approximations for constrained multi‐objective optimisation under uncertainty

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