Applied Soft Computing

Reinforcement Learning (RL) has gained significant popularity in recent years for its ability to solve complex control problems. However, most existing RL algorithms are designed to train policies for each environment in isolation, limiting their ...

• Propose a multitask extension of ARS for training related continuous control problems.
• Derived an online data-driven measurement to preempt the transfer of solutions incomplementary.
• Conduct experiments on synthetic benchmarks and ...

This paper presents a novel optimal reference tracking control approach resulted from the combination of a popular policy gradient Reinforcement Learning (RL) algorithm, namely Proximal Policy Optimization (PPO), and a metaheuristic Slime Mould ...

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• A combination of Proximal Policy Optimization and metaheuristic SMA is given.
• The adaptive approach mitigates the drawbacks of constant learning rates.
• SMA computes optimal values of learning rates in each learning process step.

The transportation Metaverse, by integrating real and virtual vehicular networks, brings significant benefits to the development of smart cities. However, the difficulty and high cost of conducting large-scale traffic and driving simulations in ...

• New Hybrid Framework Proposed: We propose TD-GAN, combining data-driven and knowledge-driven method.
• Prior Knowledge Reduces Learning Difficulty: We use prior knowledge of travel demand to reduce learning difficulties.
• Capturing ...

As an extension of partition, fuzzy β-covering can provide a more realistic and accurate description of incomplete information. In this paper, we mainly integrate the idea of fuzzy β-covering with dual hesitant fuzzy (DHF) information and ...

• Proposed neighborhood operators in dual hesitant fuzzy environment.
• Three-way decision based on dual hesitant fuzzy probabilistic rough sets.
• Three-way decision based on dual hesitant fuzzy decision-theoretic rough sets.
• Three-...

The detection of bacterial and viral microbes is pivotal for both human and animal well-being in the public health services and for veterinary care. Even in a laboratory, the isolation of microorganisms requires time-consuming procedures and ...

• Introduces hybrid Neural Networks with Spiking layers for classifying microbes.
• Compares model performance and energy use against traditional Deep Neural Networks.
• Enables microbial classification using less energy, supporting ...

This study develops a novel variant of particle swarm optimization (PSO), which improves its balance of exploration and exploitation by modifying neighborhood topology, self-adaptive parameter strategies and deep search, namely differential ...

• Propose a novel PSO variant method based on orthogonal learning to balance exploration and exploitation.
• Apply to a real-world non-linear optimization OPF problem.
• Develop a wind energy conversion system model WOPF for wind ...

Structural damage identification based on physical models is often transformed into an optimization problem that minimizes the difference between measurement information of structure being monitored and the model prediction in the parametric ...

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• A new inverse analysis approach through multi-objective optimization is synthesized to facilitate structural damage identification.
• An enhanced particle swarm optimizer is developed with particles intellectualized through learning ...

Risks are uncertain events that can affect criteria such as project cost, quality, and completion time and ultimately lead to project failure. That is why the project risk management process, which is one of the most fundamental parts of project ...

• Presenting a new multi-objective programming model for project risk responses.
• Proposing multi-mode activity and resource considerations in project risk modeling.
• Introducing a new interval-valued fuzzy solution approach to handle ...

This study addresses the issue of scheduling batch machine to minimize total tardiness. Vacuum heat treatment allows multiple jobs to be processed as a batch, as long as they do not exceed the machine's weight and size limits; the weight and size ...

• A new mixed-batch scheduling problem involves batch times influenced by job weight/size, constrained by machine weight/size.
• An intelligent algorithm based on DRL (Deep Reinforcement Learning) was proposed to address batch scheduling ...

The paper presents the application of neural networks evolving under the Hill Climb Assembler Encoding (HCAE) algorithm to control follower autonomous underwater vehicles that are members of a swarm consisting of one leader vehicle and a group of ...

• Neuro-evolutionary controller for a swarm of autonomous underwater vehicles is proposed.
• The task of the controller is to keep the swarm in one compact group while moving along a predefined trajectory.
• The only information provided ...

In the field of eXplainable Artificial Intelligence (XAI), the generation of interpretable models that are able to match the performance of state-of-the-art deep learning methods is one of the main challenges. In this work, we present a novel ...

• A new interpretable model, that results from the fusion of a concept extractor and a soft-decision tree.
• Analyze and compare different training approaches for the fusion of concept leaning and soft-decision trees.

For warehouse operations, efficiently scheduling the available resources is crucial to improve the productivity and customer satisfaction. This paper proposes a simulation-based evolutionary algorithm for order scheduling and multi-robot task ...

• We present a proactive evolutionary algorithm for the stochastic RMFS scheduling problem.
• We consider the integrated scheduling problem with processing variability.
• We implement efficient evaluation, resource allocation and ...

In concrete construction, ensuring the quality of vibration is paramount for maintaining the strength, durability, and quality of structures. This study proposed a method for monitoring the vibration quality of vibrating robots to replace ...

• Developed a concrete vibration quality monitoring method.
• Integrated EfficientNetV2 with image patching technology.
• Pioneered the application of temporal fusion strategy in vibration quality assessment.
• Experimental results ...

The strategic management of reverse logistics (RL) is essential for enterprises to enhance their operational efficiency, customer satisfaction, and sustainability performance in today's competitive marketplace. Many manufacturing firms have to ...

• A novel group decision making with incomplete triangular fuzzy multiplicative preference relations for the selection of an optimal third-party reverse logistics provider.
• Give a concept of acceptable incomplete triangular fuzzy ...

During the search process, the characteristics of the feasible regions encountered by the population continually change in Constrained Multiobjective Optimization Problems (CMOPs). This variability poses a challenge for traditional evolutionary ...

• Establishes dual stage and dual population cooperative mechanism.
• Two populations collaborate using the method of weak cooperation.
• Utilizes GA and DE operators to generate offspring in different search stages.
• Applies two ...

To address the limitations of agricultural UAV in performing trajectory planning in complex hilly terrain, a trajectory planning model based on hilly characteristic terrain and agricultural scheduling requirements is proposed. For obtaining an ...

• A novel IIE-GWO algorithm for UAV trajectory planning problem in complex hilly terrain.
• An interference enhancement model is proposed to improve trajectory search accuracy.
• Fusing Nonlinear Weights and Gaussian Perturbations to ...

This work unveils design flaws within most metaheuristics, with a specific focus on issues associated with the arithmetic optimization algorithm (AOA). Despite being a simple metaheuristic optimizer inspired by mathematical operations, AOA holds ...

• Defects of the arithmetic optimization algorithm (AOA) are revealed.
• The search equation of AOA exhibits certain limitations.
• AOA is structurally biased toward the origin of axes.

Synchronous motors play a significant role in a wide range of industrial applications. Their reliable operation is paramount. Any faults in synchronous motors can lead to costly downtime, decreased productivity, and potential safety hazards. By ...

• A novel model is proposed for synchronous motor fault diagnostics.
• A deep residual neural network effectively extracts fault features.
• Multiple support vector machines enhance the detection capability.
• Proposed model is ...

Remaining useful life (RUL) prediction and health status (HS) assessment are two key tasks in aero-engine prognostics and health management (PHM) system. However, existing deep learning-based prognostic models perform RUL prediction and HS ...

• A M2STCNN is proposed that can achieve joint learning of RUL prediction and HS assessment.
• MSF can extract multi-scale degradation features, and MLC can integrate important information from different levels.
• MTL is constructed and ...

The Data Lake Organization Problem consists of optimized data navigation structures generation to reduce the user’s time exploring all available data. The goal is to find a data organization that maximizes the expected probability of table ...

• Data Lake Organization Problem concerns optimal browsing structures in Big Data.
• We introduce a simulated annealing and a set of benchmark instances for the problem.
• The simulated annealing outperformed other methods for the ...

Using satellite-based SAR (Synthetic Aperture Radar) imagery to detect and track ships is a formidable challenge. However, accurate analysis is hampered by inherent difficulties such as obscured edges, multiple targets, varying dimensions and ...

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• A new version of YOLOv7 model was developed based on Swin Transformer.
• Different data sources were used for training and testing new model for ship detection.
• The performance of new model was compared with other 18 older models.

Human-Inspired Control (HIC) holds promise for endowing machines with human-like cognition, decision-making, and adaptability. In this study, we employ a fusion of cognitive modeling, machine learning, and control theory as the foundational ...

• The study focuses on integrating cognitive modeling, machine learning, and control theory in HIC, exploring methods like fuzzy logic.
• It delves into robotics’ similarity inference control systems, optimizing line-following, tackling ...

Topological data analysis (TDA) is a powerful mathematical framework that extracts valuable insights about the shape and structure of complex datasets by identifying and analyzing underlying topological features, including connected components, ...

• Topological analysis of intuitionistic fuzzy distance measures (IFDMs) have been conducted.
• Utilizing IFDMs, Intuition Fuzzy Vietoris Rips Complex (IFVRC) generated.
• It is shown how to extract topological features from IFVRC.
• ...

Optimization research often confronts the challenge of developing time consuming processes. This article introduces an innovative approach that leverages the computational power of Graphics Processing Units (GPUs) to speed up that optimization ...

• An adaptation of Particle Swarm Optimisation (PSO) is introduced for multiobjective optimization.
• The GPU/CUDA parallel techniques accelerates the process of neural network execution.
• The results show a significant improvement in ...

Knowledge graph (KG) is a key component of artificial intelligence. In recent years, many large-scale knowledge graphs have been produced and put into practical applications. At present, researchers have proposed many methods to reason facts that ...

• Proposing a multi-agent reinforcement learning model in TKG.
• Proposing a new reward function, considering entity, path, accuracy and diversity.
• Evaluating our approach to demonstrate its superiority in experiments.

This paper develops a new method for lifelong learning referred to as Sparsify Dynamically Expandable Network (SDEN) via Variational Dropout, which explores a sparse model while preserving the performance. Dynamically Expandable Network (DEN) can ...

• Comprehensively consider model performance, parameter storage space, and model training and test time.
• Dynamically increase network capabilities with necessary number of neurons via Variational Dropout.
• Achieve similar performance ...

Our work aims to investigate methods for solving the mixed-model assembly line balancing problem (MALBP) under uncertainty with the objective of minimizing the number of workstations. Specifically, we model task processing time as fuzzy ...

• We are the first to consider the Assembly Line Balancing Problem with fuzzy random processing time.
• a new method to rank fuzzy stochastic processing times is introduced by comparing interval values.
• The problem is formulated as ...

As social media platforms grow rapidly, multi-modal data is becoming more and more prevalent. A user can better understand events by analyzing multimodal data for topics. Automatic topic detection from multimodal data can potentially have ...

• A multimodal topic modeling framework is proposed using deep transfer learning.
• Various deep CNNs architectures are explored & deployed for multimodal topic modeling
• Proposed method has been compared with state-of-the-art using ...