Implementation of Strength Pareto Evolutionary Algorithm II in the Multiobjective Burnable Poison Placement Optimization of KWU Pressurized Water Reactor

Medical image captioning provides the visual information of medical images in the form of natural language. It requires an efficient approach to understand and evaluate the similarity between visual and textual elements and to generate a sequence of output words. A novel show, attend, and tell model (ATM) is implemented, which considers a visual attention approach using an encoder-decoder model. But the show, attend, and tell model is sensitive to its initial parameters. Therefore, a Strength Pareto Evolutionary Algorithm-II (SPEA-II) is utilized to optimize the initial parameters of the ATM. Finally, experiments are considered using the benchmark data sets and competitive medical image captioning techniques. Performance analysis shows that the SPEA-II-based ATM performs significantly better as compared to the existing models.

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“…Figure 2 shows the flowchart of SPEA-II. For mathematical and other details of the SPEA-II and hyper-parameter tuning issues, see [44][45][46].…”

Section: Proposed Methodologymentioning

confidence: 99%

Medical Image Captioning Using Optimized Deep Learning Model

Singh

Raguru

Prasad

et al. 2022

Computational Intelligence and Neuroscience

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“…This algorithm is based on the same non-domination pareto principle as NSGA-II using an evolutionary process. The fitness is calculated as a combination of the summed scores of the objectives (fitness), and the number of solutions dominated (strength) [123,124] .…”

Section: Further Multi-objective Bio-inspired Algorithmsmentioning

confidence: 99%

Review and Classification of Bio-inspired Algorithms and Their Applications

et al. 2020

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Scientists have long looked to nature and biology in order to understand and model solutions for complex real-world problems. The study of bionics bridges the functions, biological structures and functions and organizational principles found in nature with our modern technologies, numerous mathematical and metaheuristic algorithms have been developed along with the knowledge transferring process from the life forms to the human technologies. Output of bionics study includes not only physical products, but also various optimization computation methods that can be applied in different areas. Related algorithms can broadly be divided into four groups: (1)evolutionary based bio-inspired algorithms, (2) swarm intelligencebased bioinspired algorithms, (3) ecology-based bio-inspired algorithms and (4) multi-objective bioinspired algorithms. Bio-inspired algorithms such as neural network, ant colony algorithms, particle swarm optimization and others have been applied in almost every area of science, engineering and business management with a dramatic increase of number of relevant publications. This paper provides a systematic, pragmatic and comprehensive review of the latest developments in Evolutionary based Bio-

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“…In Fig. 8, we show our results when our developed scheme was compared to SPEA-II (Strength Pareto Evolutionary Algorithm) [49], PESA-II (Pareto Envelope-based Selection Algorithm with region-based selection) [50] and NSGA-III. Our scheme also out performs the SPEA-II and PESA-II algorithms with SPEA-II performing slightly better than PESA-II for the same block selection problem using the same parameters.…”

Section: Experiments and Resultsmentioning

confidence: 99%

Blockchain-IoT peer device storage optimization using an advanced time-variant multi-objective particle swarm optimization algorithm

Nartey

Tchao

Gadze

et al. 2022

J Wireless Com Network

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The integration of Internet of Things devices onto the Blockchain implies an increase in the transactions that occur on the Blockchain, thus increasing the storage requirements. A solution approach is to leverage cloud resources for storing blocks within the chain. The paper, therefore, proposes two solutions to this problem. The first being an improved hybrid architecture design which uses containerization to create a side chain on a fog node for the devices connected to it and an Advanced Time-variant Multi-objective Particle Swarm Optimization Algorithm (AT-MOPSO) for determining the optimal number of blocks that should be transferred to the cloud for storage. This algorithm uses time-variant weights for the velocity of the particle swarm optimization and the non-dominated sorting and mutation schemes from NSGA-III. The proposed algorithm was compared with results from the original MOPSO algorithm, the Strength Pareto Evolutionary Algorithm (SPEA-II), and the Pareto Envelope-based Selection Algorithm with region-based selection (PESA-II), and NSGA-III. The proposed AT-MOPSO showed better results than the aforementioned MOPSO algorithms in cloud storage cost and query probability optimization. Importantly, AT-MOPSO achieved 52% energy efficiency compared to NSGA-III. To show how this algorithm can be applied to a real-world Blockchain system, the BISS industrial Blockchain architecture was adapted and modified to show how the AT-MOPSO can be used with existing Blockchain systems and the benefits it provides.

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Implementation of Strength Pareto Evolutionary Algorithm II in the Multiobjective Burnable Poison Placement Optimization of KWU Pressurized Water Reactor

Cited by 31 publications

References 13 publications

Medical Image Captioning Using Optimized Deep Learning Model

Medical Image Captioning Using Optimized Deep Learning Model

Review and Classification of Bio-inspired Algorithms and Their Applications

Blockchain-IoT peer device storage optimization using an advanced time-variant multi-objective particle swarm optimization algorithm

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