A novel dynamic crowding distance based diversity maintenance strategy for MOEAs

Yang, Ling; Guan, Yuyang; Sheng, Weiguo

doi:10.1109/icmlc.2017.8107766

Cited by 6 publications

(5 citation statements)

References 14 publications

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“…To sort solutions, NSGA II uses a crowding distance [17,22,23]. It is used to estimate the density of solutions surrounding an individual in the population by considering the difference of the objective values of the nearest neighbor as shown in Figure 5.…”

Section: The Crowding Distancementioning

confidence: 99%

“…This formulation maintains diversity in the population by eliminating redundant individuals but suffers from a loss of both vertical and horizontal diversity as explained by Ref. [22]. To improve the diversity in the final front, an improvement of the crowding distance has been proposed by Ref.…”

Section: = = ∞mentioning

confidence: 99%

“…Performed tests guide the choice of the type of selection, crossover and mutation operators. To solve the considered problem (see Equations (22) and (23)), the NSGA II is implemented with an initial population generated randomly with 1000 individuals. The crossover probability is set to 90% to generate the offspring using the flat crossover.…”

Section: Problem Resolutionmentioning

confidence: 99%

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Cost–Benefit Prediction of Asset Management Actions on Water Distribution Networks

Nafi

Brans

2019

Water

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The potential costs and benefits of a combination of asset management actions on the water distribution network are predicted. Two types of actions are considered: maintenance actions and renewal actions. Leak detection and reparation of failures on connections and pipes define the set of potential maintenance actions to be carried out. Renewal actions concern connections, pipes, and meters. All these actions represent the model’s decision variables in order to determine a trade-off between two objectives: (i) the maximization of the water efficiency rate and (ii) the minimization of the total cost of actions to be carried out on the water system. The assessment of objective functions is ensured by an artificial neural network (ANN) trained on a French mandatory database «SISPEA». A non-dominated sorting genetic algorithm (NSGA-II) is coupled to the ANN to reach the set of compromised solutions representing potential actions to achieve. Applied to a real water distribution system in the southeast of France, the proposed decision model indicates that the improvement of water efficiency rate (WER) in the short term requires increasing operation expenditures (OPEX), which represent 99% of the total cost. Results show the existence of a threshold effect that implies to use the budget in a certain way to improve performance. A potential solution can be chosen by the decision maker among the generated Pareto front with regard to the constraint on the budget and the targeted WER.

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Section: The Crowding Distancementioning

confidence: 99%

Section: = = ∞mentioning

confidence: 99%

Section: Problem Resolutionmentioning

confidence: 99%

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Cost–Benefit Prediction of Asset Management Actions on Water Distribution Networks

Nafi

Brans

2019

Water

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“…Over the recent years, various researchers have attempted to enhance this multi-objective optimization algorithm, by proposing many approaches such as finding a novel strategy to select the solutions. The crowding distance method was initially proposed in the form of NSGA-II [2] to achieve a high level of exploration and solution diversity, a variant NSGA-II in this context was introduced by modifying the formula for crowding distance, a novel dynamic crowding distance (NDCD) was proposed in [3]. The NDCD is calculated based on the degree of deviation of each individual relative to adjacent individuals and incorporates a deletion mechanism for solutions with the lowest crowding distance.…”

Section: Introductionmentioning

confidence: 99%

Searching With Direction Awareness: Multi-Objective Genetic Algorithm Based on Angle Quantization and Crowding Distance MOGA-AQCD

Metiaf

Aljeroudi

2019

IEEE Access

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Multi-objective optimization (MOO) is widely used for solving various engineering real-life problems. Meta-heuristic optimization has been regarded as an effective solution for such problems because it enables the successful examination of a broad range of candidate solutions and the selection of optimal ones. However, there is a high probability of the algorithms becoming ensnared in local minima due to the complex optimization surface and the unlimited number of viable solutions. Therefore, to provide the decision maker with the optimal non-dominated set of solutions, significant improvements must be made to the search process, where the efficient exploration of the population has a vital role in maintaining a good non-dominated solution in evolutionary algorithms. NSGA-II is regarded as the state of the art for the meta-heuristic MOO. NSGA-II and its variants have adopted the concept of crowding distance as a measure that can leverage the characteristics of the distribution of solutions in the search space and provide a highlevel of exploration. However, this method is not sufficient to effectively explore the search space because it ignores the direction of the exploration. In this paper, the angle quantization of solutions is combined with the crowding distance to create the MOGA-AQCD algorithm, which preserves equal exploration in all directions and aims at finding equal distribution of solutions within the search space. This approach is applied to a set of standard benchmark MOO functions. The results show that MOGA-AQCD is superior to NSGA-II and NSGA-III on the most evaluation measures for MOO.

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“…The improvement or replacement of the diversity-based selection criterion is the second option. An example of the above is the novel diversity preserving strategy based on dynamic crowding distance proposed by Yang et al (2017). The third option consists of providing MOEAs that do not make use of Pareto-based selection (Liu et al 2017): decomposition-based MOEAs, grid-based MOEAs and indicator-based MOEAs.…”

Section: Introductionmentioning

confidence: 99%

Selection methods and diversity preservation in many-objective evolutionary algorithms

Martí

Segredo

Sánchez-Pi

et al. 2018

DTA

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Purpose One of the main components of multi-objective, and therefore, many-objective evolutionary algorithms, is the selection mechanism. It is responsible for performing two main tasks simultaneously. First, it has to promote convergence by selecting solutions which are as close as possible to the Pareto optimal set. And second, it has to promote diversity in the solution set provided. In the current work, an exhaustive study that involves the comparison of several selection mechanisms with different features is performed. Particularly, Pareto-based and indicator-based selection schemes, which belong to well-known multi-objective optimisers, are considered. The paper aims to discuss these issues. Design/methodology/approach Each of those mechanisms is incorporated into a common multi-objective evolutionary algorithm framework. The main goal of the study is to measure the diversity preserved by each of those selection methods when addressing many-objective optimisation problems. The Walking Fish Group test suite, a set of optimisation problems with a scalable number of objective functions, is taken into account to perform the experimental evaluation. Findings The computational results highlight that the the reference-point-based selection scheme of the Non-dominated Sorting Genetic Algorithm III and a modified version of the Non-dominated Sorting Genetic Algorithm II, where the crowding distance is replaced by the Euclidean distance, are able to provide the best performance, not only in terms of diversity preservation, but also in terms of convergence. Originality/value The performance provided by the use of the Euclidean distance as part of the selection scheme indicates this is a promising line of research and, to the best of the knowledge, it has not been investigated yet.

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A novel dynamic crowding distance based diversity maintenance strategy for MOEAs

Cited by 6 publications

References 14 publications

Cost–Benefit Prediction of Asset Management Actions on Water Distribution Networks

Cost–Benefit Prediction of Asset Management Actions on Water Distribution Networks

Searching With Direction Awareness: Multi-Objective Genetic Algorithm Based on Angle Quantization and Crowding Distance MOGA-AQCD

Selection methods and diversity preservation in many-objective evolutionary algorithms

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