A stopping criterion for decomposition-based multi-objective evolutionary algorithms

Kadhar, K. Mohaideen Abdul; Baskar, S.

doi:10.1007/s00500-016-2331-7

Cited by 11 publications

(4 citation statements)

References 26 publications

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“…Specific stopping criteria must be established to determine when a satisfactory solution has been reached. Common stopping conditions described in the literature include reaching a certain number of generations (𝐺 𝑛 ), reaching a threshold fitness value, or observing improvement over the iteration period [80]. This study used a fixed number of generations as the stopping criterion and concluded that the GA had elapsed after 800 generations.…”

Section: F Stopping Criteriamentioning

confidence: 99%

Balancing Inventory Management: Genetic Algorithm Optimization for A Novel Dynamic Lot Sizing Model in Perishable Product Manufacturing

Leuveano,

Asih,

Ridho

et al. 2023

Journal of Robotics and Control

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In Indonesia, the significant role of perishable products in food wastage has placed the country fourth globally in household food waste. Managing inventory for such products, with their short shelf life and stringent safety standards, emphasizes the need for efficient lot sizing planning. This study introduces a novel Dynamic Lot-Sizing (DLS) model, addressing perishable products and inventory constraints across multiple products, periods, and varying demands. The model aims to optimize production quantity and binary production, minimizing overall system costs. Employing a Genetic Algorithm (GA), this research solves the DLS model under constrained and unconstrained inventory capacities. Real-case data from a bread manufacturing company validates the model, while sensitivity analysis examines perishability's impact on the solution and model performance. The DLS-GA model not only reduces system costs but also effectively considers product perishability, offering optimal production plans.

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Section: F Stopping Criteriamentioning

confidence: 99%

Balancing Inventory Management: Genetic Algorithm Optimization for A Novel Dynamic Lot Sizing Model in Perishable Product Manufacturing

Leuveano,

Asih,

Ridho

et al. 2023

Journal of Robotics and Control

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“…Although different variants of MOEA/D are available in literature, a powerful single version of MOEA/D that integrates different advantages of the current versions is not yet in place. With the aim of developing an efficient version of MOEA/D for real-life problems, a powerful MOEA/D version is therefore developed in this study that is a combination of MOEA/D-DE [22], an adaptive replacement strategy [23], a stopping condition criterion [24], and a constraint-handling technique [25]. The general framework of MOEA/D is presented in Algorithm 1.…”

Section: Moea/d Algorithmmentioning

confidence: 99%

“…The general framework of MOEA/D is presented in Algorithm 1. For more details, readers are encouraged to refer to [22][23][24][25]32].…”

Section: Moea/d Algorithmmentioning

confidence: 99%

“…Furthermore, the efficiency of the MOEA/D version in this paper is also considerably enhanced by the integration of some recently developed advantageous features. These include an adaptive replacement strategy [23], a stopping condition criterion [24], and a constraint-handling technique [25]. Also, to reduce redundant evaluations of infeasible solutions derived from operational constraints during different flight phases, the new trajectory parameterization technique recently proposed in [19] is also applied.…”

Section: Introductionmentioning

confidence: 99%

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An Efficient Application of the MOEA/D Algorithm for Designing Noise Abatement Departure Trajectories

et al. 2017

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Abstract:In an effort to allow to increase the number of aircraft and airport operations while mitigating their negative impacts (e.g., noise and pollutant emission) on near-airport communities, the optimal design of new departure routes with less noise and fuel consumption becomes more important. In this paper, a multi-objective evolutionary algorithm based on decomposition (MOEA/D), which recently emerged as a potential method for solving multi-objective optimization problems (MOPs), is developed for this kind of problem. First, to minimize aircraft noise for departure routes while taking into account the interests of various stakeholders, bi-objective optimization problems involving noise and fuel consumption are formulated where both the ground track and vertical profile of a departure route are optimized simultaneously. Second, in order to make the design space of vertical profiles feasible during the optimization process, a trajectory parameterization technique recently proposed is employed. Furthermore, some modifications to MOEA/D that are aimed at significantly reducing the computational cost are also introduced. Two different examples of departure routes at Schiphol Airport in the Netherlands are shown to demonstrate the applicability and reliability of the proposed method. The simulation results reveal that the proposed method is an effective and efficient approach for solving this kind of problem.

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A new hierarchy framework for feature engineering through multi‐objective evolutionary algorithm in text classification

Asgarnezhad

Monadjemi

Aghaei

2021

Concurrency and Computation

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Sentiment classification is a field of sentiment analysis concerned with analyzing opinions, emotions, evaluations, and attitudes regarding a special topic like a product, an organization, a person, or an incident. With the growth of user-generated content on the Web, this field gained great importance in online reviews. With a wide range of reviews, customers cannot read all reviews. Considering the increasing rate of electronic documents and the urgent need manually mine for keywords that are hard and time-consuming, doing the same automatically is of high demand. A new framework proposed here to mine and classify users' comments based on mining keywords by applying the sequence pattern mining through the Separation-Power concept, a multi-objective evolutionary algorithm based on decomposition with four objectives, and a neural network as the final classifier. Some modifications are made on multi-objective evolutionary algorithm based on decomposition and Apriori algorithms to improve the text classification efficiency. To evaluate the proposed framework, three datasets applied; which compared with the two methods to measure accuracy, precision, recall, and error-index. The results indicate that this framework provides a better outcome than its counterparts with 99.45 precision, 99.34 accuracy, 99.48 recall, and 99.28% f-measure.

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A stopping criterion for decomposition-based multi-objective evolutionary algorithms

Cited by 11 publications

References 26 publications

Balancing Inventory Management: Genetic Algorithm Optimization for A Novel Dynamic Lot Sizing Model in Perishable Product Manufacturing

Balancing Inventory Management: Genetic Algorithm Optimization for A Novel Dynamic Lot Sizing Model in Perishable Product Manufacturing

An Efficient Application of the MOEA/D Algorithm for Designing Noise Abatement Departure Trajectories

A new hierarchy framework for feature engineering through multi‐objective evolutionary algorithm in text classification

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