A Survey of Multiobjective Evolutionary Algorithms based on Decomposition

Trivedi, Anupam; Srinivasan, Dipti; Sanyal, Krishnendu; Ghosh, A.

doi:10.1109/tevc.2016.2608507

Cited by 263 publications

(200 citation statements)

References 111 publications

Supporting

Mentioning

190

Contrasting

Unclassified

Order By: Relevance

“…To simplify this work, based on the opinion of Trivedi et al. [29], the population size of each generation is fixed at 50.…”

Section: Energy-efficient Coverage Control Using Memetic Algorithmmentioning

confidence: 99%

An Effective Scheduling Algorithm for Coverage Control in Underwater Acoustic Sensor Network

Wang

Chang

et al. 2018

Sensors

View full text Add to dashboard Cite

Coverage maintenance is a bottleneck restricting the development of underwater acoustic sensor networks (UASNs). Since the energy of the nodes is limited, the coverage of UASNs may gradually decrease as the network operates. Thus, energy-saving coverage control is crucial for UASNs. To solve the above problems, this paper proposes a coverage-control strategy (referred to as ESACC) that establishes a sleep–wake scheduling mechanism based on the redundancy of deployment nodes. The strategy has two main parts: (1) Node sleep scheduling based on a memetic algorithm. To ensure network monitoring performance, only some nodes are scheduled to work, with redundant nodes in a low-power hibernation state, reducing energy consumption and prolonging the network lifetime. The goal of node scheduling is to find a minimum set of nodes that can cover the monitoring area, and a memetic algorithm can solve this problem. (2) Wake-up scheme. During network operation, sleeping nodes are woken to cover the dead nodes and maintain high coverage. This scheme not only reduces the network energy consumption but takes into account the monitoring coverage of the network. The experimental data show that ESACC performs better than current algorithms, and can improve the network life cycle while ensuring high coverage.

show abstract

“…To simplify this work, based on the opinion of Trivedi et al. [29], the population size of each generation is fixed at 50.…”

Section: Energy-efficient Coverage Control Using Memetic Algorithmmentioning

confidence: 99%

An Effective Scheduling Algorithm for Coverage Control in Underwater Acoustic Sensor Network

Wang

Chang

et al. 2018

Sensors

View full text Add to dashboard Cite

show abstract

“…Kernelized Fuzzy Rough Set Based Semi Supervised Support Vector Machine (KFRS-S3VM) [1] and iii. Multi-objective Particle Swarm Optimization (MPSO) [6], [21][22][23][24] have been discussed in the following subsections.…”

Section: Recently Proposed Data Mining Classifiersmentioning

confidence: 99%

EMOPS: an enhanced multi-objective pswarm based classifier for poorly understood cancer patterns

Subasree

Gopalan²,

Sakthivel

2018

IJET

View full text Add to dashboard Cite

Microarray based Cancer Pattern Classification is one of the popular techniques in Bioinformatics Research. This Research Work is noticed that for studying the expression levels through the Gene Expression profiling experiments, thousands of Genes have to be simultaneously studied to understand the patterns of the Gene Expression or Cancer Pattern. This research work proposed an efficient Cancer Pattern Clas-sifier called An Enhanced Multi-Objective Pswarm (EMOPS) and it is studied thoroughly in terms of Memory Utilization, Execution Time (Processing Time), Sensitivity, Specificity, Classification Accuracy and FScore. The results were compared with the recently proposed classifiers namely Hybrid Ant Bee Algorithm (HABA), Kernelized Fuzzy Rough Set Based Semi Supervised Support Vector Machine (KFRS-S3VM) and Multi-objective Particle Swarm Optimization (MPSO). For analyzing the performances of the proposed model, this work considered a few cancer patterns namely Bladder, Breast, Colon, Endometrial, Kidney, Leukemia, Lung, Melanoma, Mom-Hodgkin, Pancreatic, Prostate and Thyroid. From our experimental results, it was noticed that the proposed model outperforms the identified three classifiers in terms of Memory Utilization, Execution Time (Processing Time), Sensitivity, Specificity, Classification Accuracy and FScore. To improve the performance of the system further in term of Processing Time, the proposed model Enhanced Multi-Objective Pswarm (EMOPS) is implemented under Parallel Framework and evaluated. That is the model is tested with Two, Four, Eight and Sixteen Parallel Processors and from the results, it is established that the Processing Time decreases considerably which will improve the performance of the Proposed Model.

show abstract

“…The multi-objective evolutionary algorithm based on decomposition (MOEA/D), first proposed by Zhang and Li [21], has been recognized as one of the most popular multi-objective evolutionary algorithms to date [30]. In MOEA/D, a multi-objective optimization problem (MOP) is transformed into a set of single optimization sub-problems by applying decomposition approaches, and then evolutionary algorithms are utilized to optimize these sub-problems simultaneously.…”

Section: Moea/d Algorithmmentioning

confidence: 99%

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

et al. 2017

View full text Add to dashboard Cite

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.

show abstract

A Survey of Multiobjective Evolutionary Algorithms based on Decomposition

Cited by 263 publications

References 111 publications

An Effective Scheduling Algorithm for Coverage Control in Underwater Acoustic Sensor Network

An Effective Scheduling Algorithm for Coverage Control in Underwater Acoustic Sensor Network

EMOPS: an enhanced multi-objective pswarm based classifier for poorly understood cancer patterns

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

Contact Info

Product

Resources

About