Swarm Intelligence - Science topic

The Special Issue "Innovations in Optimization and Operations Research" aims to bring together cutting-edge research and novel advancements in the fields of optimization and operations research. This Special Issue seeks to provide a platform for researchers, practitioners, and experts from diverse disciplines to share their innovative ideas, method...

Swarm intelligence algorithms have been in recent years one of the most used tools for planning the trajectory of a mobile robot. Researchers are applying those algorithms to find the optimal path, which reduces the time required to perform a task by the mobile robot. In this paper, we propose a new method based on the grey wolf optimizer algorithm...

Structural damage identification Integrated surrogate model Weighted average strategy Improved Termite life cycle optimizer Damaged dam model A B S T R A C T Structural Damage Identification (SDI) is a crucial branch in the field of structural health monitoring, providing an essential support for the safe operation of structures. In this paper, a n...

span lang="EN-US">This study introduces a new metaheuristic method: the best-worst northern goshawk optimizer (BW-NGO). This algorithm is an enhanced version of the northern goshawk optimizer (NGO). Every BW-NGO iteration consists of four phases. First, each agent advances toward the best agent and away from the worst agent. Second, each agent move...

Cooperative localization plays a significant role in various applications, such as emergency rescue and navigation path planning. The advent of swarm intelligence has opened doors to agent-based cooperative localization. However, sharing data between agents during the cooperative localization process can compromise privacy. One of the key challenge...

A new technology that is gaining popularity today is the Wireless Sensor Network. Smart sensors are being used in a variety of wireless network applications, including intruder detection, transportation, the Internet of Things, smart cities, the military, industrial, agricultural, and health monitoring, as a result of their rapid expansion. Sensor...

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Neural networks have been widely used as compensational models for aircraft control designs and as surrogate models for other optimizations. In the case of tiltrotor aircraft, the total number of aircraft states and controls is much greater than that of both traditional fixed-wings and helicopters. This requires, in general, a huge amount of traini...

As a new swarm intelligence algorithm, sparrow search algorithm (SSA) has the advantages of fewer parameters, simplicity, strong global and local search capability, and has been successfully applied in continuous problem and its engineering applications. Meanwhile, SSA for job shop scheduling problem (JSP) is studied rarely and would arise new prob...

Initiating a transportation process during heavy traffic conditions or adverse weather conditions has a retracting impact on the cost, even if the transit path chosen is optimal. This paper proposes an algorithmic framework for a dynamic transportation process using a bilevel multi-objective optimization approach. Dynamic detection of road traffic...

rain storm optimization (BSO) is a swarm-intelligence clustering-based algorithm inspired by the human brainstorming process. Electromagnetism-like mechanism for global optimization (EMO) is a physics-inspired optimization algorithm. In this study we propose a novel hybrid metaheuristic evolutionary algorithm that combines aspects from both BSO and...

Annotation. The solution of the inverse gravity exploration problem for a two-dimensional horizontal prism using the particle swarm method is considered. After a relatively small number of iterations, it was possible to localize the perturbing object with high accuracy. It is concluded that the use of swarm intelligence for modeling the sources of...

Machine learning has been used in solving various bioinformatics tasks such as storage of biomedical data, naming conventions for new and existing organisms, gene expression, protein folding, genome classification, clustering, and sequence prediction. All these processes are needed for disease detection and study, as well as drug design and discove...

With the development of artificial intelligence, numerous researchers are attracted to study new heuristic algorithms and improve traditional algorithms. Artificial bee colony (ABC) algorithm is a swarm intelligence optimization algorithm inspired by the foraging behavior of honeybees, which is one of the most widely applied methods to solve optimi...

The Twelveth International Workshop on Mathematical Models and their Applications (IWMMA 2023) will provide an international forum for the presentation of original results in mathematical modeling for software-and hardware applications in various fields. It will stimulate lively discussions among researchers as well as industrialists. Papers may di...

The Fe-based Fischer–Tropsch synthesis (FTS) catalyst shows a rich phase chemistry under pre-treatment and FTS conditions. The exact structural composition of the active site, whether iron or iron carbide (FeCx), is still controversial. Aiming to obtain an insight into the active sites and their role in affecting FTS activity, the swarm intelligenc...

One of the most widely referenced Swarm Intelligence (SI) algorithms is the Grey Wolf Optimizer (GWO), which is based on the pack hunting and natural leadership organization of grey wolves. The GWO algorithm offers several significant benefits, including simple implementation, rapid convergence, and superior convergence outcomes, leading to its eff...

Vortex search (VS) algorithm is a recently proposed swarm intelligence or evolutionary algorithm for solving continuous optimization problems inspired by the behavior of whirlpool. In this study, an approach based on VS algorithm is proposed to deal with uncapacitated facility location problem (UFLP) which is a pure problem in binary domain. The up...

This paper provides a real application of a popular swarm-intelligence optimisation method. The aim is to analyse the efficiency of various settings of the marine predator algorithm (MPA). Four crucial numerical parameters of the MPA are statistically analysed to propose the most efficient setting for solving engineering problems. Besides populatio...

Fast Computer-Aided Diagnostic Systems (CAD) have become instrumental in diagnosing diseases. Brain tumors, in particular, pose a significant health challenge. Traditional tumor detection methods relied on radiologists and biopsy, which are time-consuming and detrimental to patients. Early detection is crucial for effective treatment. This system l...

Microarray data represents a valuable tool for the identification of biomarkers associated with diseases and other biological conditions. Genes, in particular, are a type of biomarker that holds great importance for the identification and understanding of various types of tumors, including brain, lung, and breast cancers. However, a significant por...

This study introduces an innovative smart grid (SG) intrusion detection system, integrating Game Theory, swarm intelligence, and deep learning (DL) to protect against complex cyber-attacks. This method balances training samples by employing conditional DL using Game Theory and CGAN. The Aquila optimizer (AO) algorithm selects features, mapping them...

Swarm intelligence has promising applications for firm search and decision-choice problems and is particularly well suited for examining how other firms influence the focal firm’s search. To evaluate search performance, researchers examining firm search through simulation models typically build a performance landscape. The NK model is the leading t...

Computer technology has brought greater convenience and effect to landscape architecture design. A multimedia-based landscape architecture method based on the optimization calculation method of swarm intelligence is proposed in this paper. Composition is performed through creation of the environment for the landscape architecture; then the landscap...

The accurate determination of uniaxial compressive strength (UCS) plays a vital role in the initial design phase of rock engineering and rock geotechnics. Traditionally, this assessment entails costly, time-intensive and labor-demanding experimental tests. Consequently, there is significant promise in exploring machine learning techniques for UCS p...

For mixed additive and multiplicative random error models (MAM models), due to the complex correlation between the parameters and the model power array, derivative operations will be inevitable in the actual calculation. When the observation equation is in nonlinear form, the operations will be more complicated. The swarm intelligence optimization...

Software projects need a strong programming team, time, cost, and experience to succeed. In many software projects, the project progress factors need to be better provided, and the project defect. Estimating the effort of software projects makes it possible to predict the failure of software projects in the early stages and reduce the loss caused b...

Cities attract a large number of inhabitants due to their more advanced industrial and commercial sectors and more abundant and convenient living conditions. According to statistics, more than half of the world’s population resides in urban areas, contributing to the prosperity of cities. However, it also brings more crime risks to the city. Crime...

The advent of the cloud computing paradigm has enabled innumerable organizations to seamlessly migrate, compute, and host their applications within the cloud environment, affording them facile access to a broad spectrum of services with minimal exertion. A proficient and adaptable task scheduler is essential to manage simultaneous user requests for...

The major goal of this study was to develop a robust fuzzy model to mimic the generation of biodiesel from the transesterification of dairy-washed milk scum (DWMS) oil. Four process parameters were considered: the molar ratio of methanol to oil, the concentration of KOH, the reaction temperature, and the reaction time. The proposed technique was di...

Conversational Swarm Intelligence (CSI) is a new method for enabling large human groups to hold real-time networked conversations using a technique modeled on the dynamics of biological swarms. Through the novel use of conversational agents powered by Large Language Models (LLMs), the CSI structure simultaneously enables local dialog among small de...

Image contrast is an important factor in distinguishing objects in the image from their background. Low-contrast images, caused by various factors such as poor lighting, are insufficient for human visual perception and many image processing applications. Therefore, image contrast enhancement (ICE) is a necessary preprocessing step in different imag...

Metaheuristic optimization algorithms (Nature-Inspired Optimization Algorithms) are a class of algorithms that mimic the behavior of natural systems such as evolution process, swarm intelligence, human activity and physical phenomena to find the optimal solution. Since the introduction of meta-heuristic optimization algorithms, they have shown thei...

Over the past few decades, the application of iterated function systems (IFS) in reconstructing fractal images has been a challenging research area. Numerous methods have been proposed to address this issue. However, they generally focus on binary or grayscale images, neglecting the color component of the process. Consequently, they are unsuitable...

The virtual enterprise is undoubtedly exposed to various risks from multiple angles owing to its dynamic operational setting, diverse constituents, and dispersed characteristics. Identifying crucial information that establishes a connection between the owner and the partners represents a potential gap that impedes sound decision-making. Therefore,...

First of all, in this research, we solve the problem of the next release ((NRP) (Next Release Problem)), which is classified as a multi-objective difficult problem (NP_ hard problem) using swarm intelligence, since the programs are spread in all areas of our life and process The development on it is constantly ongoing and the selection of the optim...

Swarm intelligence algorithm is a bionic random probability heuristic search algorithm, which is a kind of algorithm that transforms the collective behaviors in biological study into engineering application in practice. In this paper, a multi-group cooperative evolutionary optimization algorithm was proposed by referring to the interaction behavior...

Motivation Recent frameworks based on deep learning have been developed to identify cancer subtypes from high-throughput gene expression profiles. Unfortunately, the performance of deep learning is highly dependent on its neural network architectures which are often hand-crafted with expertise in deep neural networks (DNNs), meanwhile, the optimiza...

In recent years, autonomous electric vehicles (A-EVs) have attracted the attention of academia and industry. In urban mobility, this topology requires consensus to control behaviours under swarm robotics. Although several model-based solutions have successfully enhanced accuracy and overcome some limitations, specific technological, methodological,...

The preventive measures taken to curb the spread of COVID-19 have emphasized the importance of wearing face masks to prevent potential infection with serious diseases during daily activities or for medical professionals working in hospitals. Due to the mandatory use of face masks, various methods employing artificial intelligence and deep learning...

Power prediction is now a crucial part of contemporary energy management systems, which is important for the organization and administration of renewable resources. Solar and wind powers are highly dependent upon environmental factors, such as wind speed, temperature, and humidity, making the forecasting problem extremely difficult. The suggested c...

A swarm intelligence-based optimization algorithm, named Duck Swarm Algorithm (DSA), is proposed in this study. This algorithm is inspired by the searching for food sources and foraging behaviors of the duck swarm. The performance of the DSA is verified by using eighteen benchmark functions, where its statistical (best, mean, standard deviation, an...

Swarm intelligence optimization algorithms have been proven to significantly improve the efficiency and accuracy of solving inverse kinematics problems for manipulators. This is of great importance for research in the fields of manipulator trajectory tracking control and path optimization. Distinguished from conventional swarm intelligence algorith...

Water scarcity is a challenging global risk. Urban wastewater treatment technologies, which utilize processes based on single-stage ultrafiltration (UF) or nanofiltration (NF), have the potential to offer lean-and-green cost-effective solutions. Robustifying the effectiveness of water treatment is a complex multidimensional characteristic problem....

Classification is a widely used supervised learning technique that enables models to discover the relationship between a set of features and a specified label using available data. Its applications span various fields such as engineering, telecommunication, astronomy, and medicine. In this paper, we propose a novel rule-based classifier called RUCI...

Variable-length anomalous subsequence detection in time series has many important applications in the real world, yet the methods presented in existing studies are computationally expensive, as the detection techniques are mostly brute-force approaches. In this work, we formalize the detection problem into a subsequence segmentation problem (SSP) o...

The Dung Beetle Optimization (DBO) algorithm is a powerful metaheuristic algorithm that is widely used for optimization problems. However, the DBO algorithm has limitations in balancing global exploration and local exploitation capabilities, often leading to getting stuck in local optima. To overcome these limitations and address global optimizatio...

Multi-Agent Reinforcement Learning (MARL) is widely used to solve various problems in real life. In the multi-agent reinforcement learning tasks, there are multiple agents in the environment, the existing Proximal Policy Optimization (PPO) algorithm can be applied to multi-agent reinforcement learning. However, it cannot deal with the communication...

Extending the network lifetime as long as possible is one of the critical issues for wireless sensor networks (WSNs), which is usually resolved by using clustering and routing protocols. The clustering and routing processes are considered as an NP-hard problem popularly solved by swarm intelligence optimization algorithm. In this paper, a novel par...

Real-time monitoring of rock stability during the mining process is critical. This paper first proposed a RIME algorithm (CCRIME) based on vertical and horizontal crossover search strategies to improve the quality of the solutions obtained by the RIME algorithm and further enhance its search capabilities. Then, by constructing a binary version of C...

The use of probabilistic analysis of slopes has been a useful technique for determining the level of uncertainty in various variables has grown. In this study, a probabilistic analysis of a representative embankment with a height of 12 m under seismic conditions was carried out utilizing the UPSS ADD-INs 3.0 and Subset Simulation methodologies...

Vehicle Ad-hoc Networks (VANETs) are a kind of Internet of Things system where groups of vehicles connect and vehicle tracking infrastructure to enhance driver and public security as well as lifestyles. In a VANET, vehicles oversee the communicating status to the controller and other drivers for processing, as well as the statuses of the road, traf...

Efficient and accurate detection and providing early warning for citrus psyllids is crucial as they are the primary vector of citrus huanglongbing. In this study, we created a dataset comprising images of citrus psyllids in natural environments and proposed a lightweight detection model based on the spatial channel interaction. First, the YOLO-SCL...

The cycle life test offers significant sustainment for utilization and maintenance of lithium-ion batteries. The traditional method is continuous charge-discharge testing without interruption, which often takes one year or more. Here, we propose a rapid cycle life test method based on intelligent prediction to replace the continuous test, which sho...

Many swarm intelligence techniques are facing rigorous challenges since they cannot exploit useful information well during the evolutionary procedure. To remedy this issue, this paper raises a reinforced JAYA algorithm (QLJAYA) that employs the Q-learning and gradient search scheme. In QLJAYA, to balance convergence and diversity, a modified search...

A dynamic mutation probability formula is utilized to optimize the model. In order to solve the logistics warehouse path problem, the ant colony optimization algorithm, optimized by a genetic algorithm, is employed to construct a logistics warehouse path optimization model. This model effectively optimizes the logistics warehouse paths. Test result...

Descriptions of types of intelligence or cognition that conceptualize and categorize behavioral capabilities of workers and cooperative groups of eusocial insects have proliferated. Individual workers are described as having cognition, or less frequently, intelligence, and emergent colony-level behavior is typically described as collective intellig...

In the Internet of Things network, sending a packet from the origin to the destination and optimal routing is a fundamental challenge because the packets must be sent in the optimal path with minimum delay and error rate. One of the challenges of optimal routing is the need for more consideration of the problem of congestion in the paths of sending...

In this study, we focused on using microarray gene data from pancreatic sources to detect diabetes mellitus. Dimensionality reduction (DR) techniques were used to reduce the dimensionally high microarray gene data. DR methods like the Bessel function, Discrete Cosine Transform (DCT), Least Squares Linear Regression (LSLR), and Artificial Algae Algo...

Since its original publication in 1978, Lozi’s chaotic map has been thoroughly explored and continues to be. Hundreds of publications have analyzed its particular structure and applied its properties in many fields (e.g., improvement of physical devices, electrical components such as memristors, cryptography, optimization, evolutionary algorithms,...

Multi-level threshold image segmentation is widely used in medical image segmentation. Traditional methods for selecting optimal thresholds suffer from exponentially increasing time complexity as the number of threshold levels increases. In order to solve these problems, we choose African vultures optimization algorithm (AVOA) and introduce a novel...

In recent years technology advances and societal demands for the design and operation of safe and sustainable maritime assets intensified. While the economies of scale continue to drive market dynamics, the need for greater efficiency, economy, safety, and decarbonization prevail. In the future innovation for the design of ships and floating offsho...

Sentiment classification is a prevalent task in text mining in which a text classifies into positive, negative, or neutral classes. Sentiment classification is an essential issue of decision-making for people, companies, etc. Feature selection is the most influential stage in sentiment classification. Due to the NP-hard nature of the problem and a...

Association Rules Mining (ARM) forms one of the important data mining techniques. The classical methods that were previously worked on by researchers have become ineffective to deal with the steady growth of databases, which prompted us to use the mining process for association rules based on metahuristic, and in our work all the correct rules will...

Introduction. In the context of modern technologies and the widespread use of unmanned aerial vehicles (UAVs) in various fields of activity, the study of optimizing their mission planning becomes increasingly relevant. This is particularly true for hybrid systems where UAVs are integrated with ground transportation ("Drone+Vehicle"). The article de...

Optimizing large-scale numerical problems is a significant challenge with numerous real-world applications. The optimization process is complex due to the multi-dimensional search spaces and possesses several locally optimal regions. In response to this issue, various metaheuristic algorithms and variations have been developed, including evolutiona...

This paper introduces the Golf Sport Inspired Search (GSIS) algorithm as an evolutionary search method for numerical optimization. Each solution is generated with the aid of the step-length and search direction. The step-length is determined with the aid of the Tait’s model of the trajectory of the golf ball, which is a physical model. The search d...

The sand cat is a creature suitable for living in the desert. Sand cat swarm optimization (SCSO) is a biomimetic swarm intelligence algorithm, which inspired by the lifestyle of the sand cat. Although the SCSO has achieved good optimization results, it still has drawbacks, such as being prone to falling into local optima, low search efficiency, and...

Recently, the dynamic distribution of resources and task scheduling has played a critical role in cloud computing to achieve maximum storage and performance. The allocation of computational tasks in the cloud is a complicated process that can be affected by some factors, such as available network bandwidth, makespan, and cost considerations. Howeve...

The efficacy of saving energy standards depends on the ability to anticipate the heat loss of buildings. Environmentally friendly materials, also known as eco-friendly or sustainable materials, have a minimal negative impact on the environment throughout their life cycle. These materials are designed to conserve resources, reduce pollution, and pro...