Use Of Bio-inspired Algorithms Research Articles

Neutrosophic goal programming technique with bio inspired algorithms for crop land allocation problem

In agriculture, crop planning and land distribution have been important research subjects. The distribution of land involves several multi-functional tasks, such as maximizing output and profit and minimizing costs. These functions are influenced by a variety of uncertain elements, including yield, crop price, and indeterminate factors like seed growth and suitable fertilizer. In order to address this problem, other researchers have used fuzzy and intuitionistic fuzzy optimization approaches, which did not include the indeterminacy membership functions. However, the neutrosophic optimization technique addresses the problem by using individual truth, falsity, and indeterminacy membership functions. So, to improve the optimal solution, the Neutrosophic Goal Programming (NGP) problem with hexagonal intuitionistic parameters is employed in this study. The membership functions for truth, indeterminacy, and falsity are constructed using hyperbolic, exponential, and linear membership functions. Minimizing the under deviations of truth, over deviations of indeterminacy, and falsity yields the NGP achievement function, which is used to attain optimal expenditure, production, and profit under the constraints of labour, land, food requirements, and water. Bio-inspired computing has been a major research topic in recent years. Optimization is mostly accomplished through the use of bio-inspired algorithms, which draw inspiration from natural behaviour. Bio-inspired algorithms are highly efficient in exploring large solution spaces, and helps to manage trade-offs between various goals, and providing the global optimal solution. Consequently, bio-inspired algorithms such as Grey Wolf Optimization (GWO), Social Group Optimization (SGO), and Particle Swarm Optimization (PSO) are employed in the current work to determine the global optimal solutions for the NGP achievement function. The data for the study was collected from the medium-sized farmers in Ariyalur District, Tamil Nadu, India. To illustrate the uniqueness and application of the developed method, the optimal solutions of the suggested method are compared with Zimmermann, Angelov, and Torabi techniques. The proposed technique demonstrates that the bioinspired algorithms’ optimal solution to the neutrosophic goal is superior to the existing approaches.

A High-Speed Acoustic Echo Canceller Based on Grey Wolf Optimization and Particle Swarm Optimization Algorithms.

Currently, the use of acoustic echo cancellers (AECs) plays a crucial role in IoT applications, such as voice control appliances, hands-free telephony and intelligent voice control devices, among others. Therefore, these IoT devices are mostly controlled by voice commands. However, the performance of these devices is significantly affected by echo noise in real acoustic environments. Despite good results being achieved in terms of echo noise reductions using conventional adaptive filtering based on gradient optimization algorithms, recently, the use of bio-inspired algorithms has attracted significant attention in the science community, since these algorithms exhibit a faster convergence rate when compared with gradient optimization algorithms. To date, several authors have tried to develop high-performance AEC systems to offer high-quality and realistic sound. In this work, we present a new AEC system based on the grey wolf optimization (GWO) and particle swarm optimization (PSO) algorithms to guarantee a higher convergence speed compared with previously reported solutions. This improvement potentially allows for high tracking capabilities. This aspect has special relevance in real acoustic environments since it indicates the rate at which noise is reduced.

Development of a solution search method using a combined bio-inspired algorithm

The object of the study is decision support systems. The subject of the study is the decision-making process in management problems using a combined bio-inspired algorithm, consisting of: – the improved wolf optimization algorithm and the improved sparrow search algorithm – for solving optimization problems regarding the object state; – an advanced genetic algorithm – for selecting the best agents in flocks; – an advanced training method – for deep training of agents to improve the optimization characteristics of agents. A solution search method using an improved bio-inspired algorithm is proposed. The method has the following sequence of actions: – input of initial data; – initialization of the search for a flock of sparrows and its parameters; – ranking and selection of sparrow agents using an advanced genetic algorithm; – updating the sparrow location for the discoverer; – checking the conditions for updating the position of sparrows; – initialization of additional search parameters; − running the gray wolf optimization algorithm; – training agents’ knowledge bases; – determining the amount of necessary computing resources of the intelligent decision support system. The originality of the proposed method lies in the combined use of bio-inspired algorithms, setting agents taking into account the uncertainty of the initial data, advanced global and local search procedures. The method makes it possible to increase the efficiency of data processing at the level of 19 % using additional improved procedures. The proposed method should be used to solve the problems of evaluating complex and dynamic processes in the interest of solving national security problems

The development of a method for assessing the security of complex technical systems using artificial immune systems

Ensuring the security of complex technical systems of various functional purposes requires a constant search for new scientific and practical approaches in order to ensure its proper level against a growing list of new risks and threats. Nowadays, no state in the world is able to work on the creation and implementation of artificial intelligence in isolation from others. Artificial intelligence technologies are actively used to solve both general and highly specialized tasks in various spheres of society. The problem of synthesis of management of complex technological processes is an urgent task in management theory. A promising direction in the design of such complex ones is the use of bio-inspired algorithms that are effectively used while solving optimization tasks. Thus, the object of research is complex technical systems. The subject of research is the state security of complex technical systems. The research developed a method for assessing the security of complex technical systems using artificial immune systems. The novelty of the proposed method consists in: ‒ taking into account while calculating the correction factor for the degree of uncertainty about the state of a complex technical system; ‒ reducing computing costs while assessing the state of a complex technical system; ‒ improved implementation of procedures for solving the task of influencing relationships in a complex technical system; ‒ creating a multi-level and interconnected description of hierarchical complex technical systems; ‒ the possibility of performing calculations with source data that are different in nature and units of measurement. It is advisable to implement the mentioned technique in specialized software, which is used to analyze the state of complex technical systems and make decisions.

Comprehensive Review on UAV Efficient Path Planning Techniques for Optimized Applications

This literature review article compiles works that describe the use of bio-inspired algorithms in Unmanned Aerial Vehicle (UAV) motion planning. This review demonstrates the usefulness of the various frameworks by presenting the contributions and limits of each article. The optimization method also decreases the amount of inaccuracy in the system’s convergence. Furthermore, this study discusses the assessment procedures and draws attention to the novelties and limitations of the explored methods. The paper wraps up with a detailed examination of the current difficulties and potential future research directions. This research will aid scholars in comprehending the state-of-the-art efforts made in UAV motion planning using a variety of optimization strategies.

Bio-Inspired Algorithms for Preserving the Privacy of Data

Security of the data is also concerned with the privacy of the data since the data or the information can be easily disclosed. Data sharing also plays a key role in security. Recently, patterns are disclosed using associative rule mining and the sensitive information are one of the imposing threats to the security aspects in data mining. Preserving the data as well as the privacy of the user using several PPDM approaches leads to provide authorized access for such sensitive information. The security threats for preserving privacy are provided by developing a sanitization process. The sanitization process is considered to be one of the biggest challenges in the mining of data. In this paper, different approaches such as GA-based and PSO based algorithms are surveyed and analyzed for preserving the privacy of data. The purpose of data sanitization and the use of Bio-Inspired algorithms such as Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) are discussed.

A Machine Learning Approach to Predict the Average Localization Error With Applications to Wireless Sensor Networks

Node localisation is one of the significant concerns in Wireless Sensor Networks (WSNs). It is a process in which we estimate the coordinates of the unknown nodes using sensors with known coordinates called anchor nodes. Several bio-inspired algorithms have been proposed for accurate estimation of the unknown nodes. However, use of bio-inspired algorithms is a highly time-consuming process. Hence, finding optimal network parameters for node localisation during the network set-up process with the desired accuracy in a short time is still a challenging task. In this article, we have proposed an efficient way to evaluate the optimal network parameters that result in low Average Localisation Error (ALE) using a machine learning approach based on Support Vector Regression (SVR) model. We have proposed three methods (S-SVR, Z-SVR and R-SVR) based on feature standardisation for fast and accurate prediction of ALE. We have considered the anchor ratio, transmission range, node density and iterations as features for training and prediction of ALE. These feature values are extracted from the modified Cuckoo Search (CS) simulations. In doing so, we found that all the methods perform exceptionally well with method R-SVR outperforming the other two methods with a correlation coefficient (R = 0.82) and Root Mean Square Error (RMSE = 0.147m).

A Comparative Study on Bio-Inspired Algorithms in Layout Optimization of Construction Site Facilities

A good arrangement of site layout on a construction project is a fundamental component of the project’s efficiency. Optimization on site layout is necessary in order to reduce the transportation cost of resources or personnel between facilities. Recently, the use of bio-inspired algorithms has received considerable critical attention in solving the engineering optimization problem. These methods have consistently provided better performance than traditional mathematical-based methods to a variety of engineering problems. This study compares the performance of particle swarm optimization (PSO), artificial bee colony (ABC), and symbiotic organisms search (SOS) algorithms in optimizing site layout planning problems. Three real-world case studies of layout optimization problems have been used in this study. The results show that SOS has a better performance in comparison to the other algorithms. Thus, this study provides useful insights to construction practitioners in the industry who are involved in dealing with optimization problems

Bio-inspired algorithms for improving mixed and incomplete data clustering

This article introduces a novel clustering algorithm for dealing with mixed and incomplete data descriptions of instances: the A2FCPAntSA algorithm. Unlike previous proposals, this new algorithm uses groups of instances as initial clusters in an agglomerative clustering scheme. It also merges in a single step, all of the most similar clusters. In addition, the proposal incorporates the use of bio-inspired algorithms to refine the obtained clusters. The numerical experiments carried out over several repository datasets display the superiority of the proposal with respect to other state of the art clustering algorithms, for mixed and incomplete data, by considering several dissimilarity measures and supervised cluster validity indexes.

The Evolutionary Optimization of a Company’s Return on Equity Factor: Towards the Agent-Based Bio-Inspired System Supporting Corporate Finance Decisions

Return on Equity (ROE) is an important factor from the perspective of formulating and implementing a company’s financial strategies. It is also one of its evaluation criteria. It presents to investors the effectiveness of using their capital. Increasing profitability may be treated as a symptom of shareholder wealth, while its reduction may be a signal indicating a deterioration of the financial situation of the company. An investment in a company will be attractive to investors if the results obtained by it will enable the benefit from the dividend to be paid and if the share prices will show an upward trend. Therefore, profit and profitability are categories dependent on the company and affect the wealth of its owners. The ROE ratio is synthetic and is linked to, among others with the size of sales, asset use activity, and the size of the company’s debt. However, the decisions regarding the capital structure of a company should be made not only by purely economic and financial analyses but also should take into account the social and environmental effects of economic activities. To take into account not only short-term financial goals but also long-term sustainable development goals during the decision-making process, we need intelligent and creative multi-criteria decision support tools. Bio-inspired artificial intelligence techniques—such as evolutionary algorithms, deep neural networks, or swarm algorithms, to give only a few examples—are gaining more and more popularity in the recent years. Evolutionary algorithms are optimization techniques that are modeled on the processes of evolution that are taking place in natural populations. They can find approximate solutions to the NP-hard global, multi-modal, and multi-objective optimization problems. In this paper, we propose an innovative approach—an agent-based bio-inspired system supporting decisions in the area of corporate finance, which takes into account not only financial goals but also the sustainable development goals. The system will allow for multi-objective optimization with the use of bio-inspired algorithms. In this paper, we will concentrate on one module of the proposed system—the evolutionary algorithm optimizing the ROE factor. During the experiments, we will verify the ability of the proposed algorithm to provide decision makers with reasonable, useful, and, at the same time, also innovative and non-obvious solutions concerning the desired capital structure of a given company, which usually operates in a rapidly changing environment. The proposed system will allow for taking into account more than one criteria and perform multi-objective optimization with the use of an evolutionary algorithm or an agent-based co-evolutionary algorithm, so it will be possible to include also the long-term goals of sustainable development in the future.

Bio-Inspired Algorithms Applied on Microstrip Patch Antennas: a Review

Design of small broadband, multiband and high-directivity microstrip patch antennas (MPAs) is a challenging task for the antenna research community since the classical MPAs do not perform well enough to be used in the real world applications. In this sense, various performance improvement techniques such as stacked patches, air gaps, compact meandering geometries, fractal-shapes and shorting pins are applied to design improved MPAs. Use of bio-inspired algorithms along with these techniques is trending due to their capability of manipulating antenna parameters to achieve optimized performance. Literature presents use of bio-inspired algorithms such as Genetic algorithms (GA), Particle swarm optimization (PSO), Differential evolution (DE), Invasive weed optimization (IWO), Wind driven optimization (WDO) and Ant colony optimization (ACO) on MPAs for performance enhancement. This paper begins with an introduction to MPAs followed by an analysis of the performance improvement techniques. Evolution of bio-inspired algorithms and their applications in the field of MPAs are also presented. Based on the compilation of studies, importance of applying multi-objective bio-inspired algorithms for simultaneous optimization of multiple antenna parameters is emphasized. Further, research voids in the field are revealed and direction is shown to design compact multifunctional MPAs.

Bio-inspired algorithms for cloud computing: a review

Cloud computing provides internet-based services to access different kind of service or resources, eliminating the need for centralised data access. There are several challenges available in cloud computing, where specific issues like resource provisioning, load imbalance and performance improvement can be solved using bio-inspired algorithms. Bio-inspired algorithms have the tendency to solve various kinds of problems naturally by providing optimised solutions. Though it is not used in cloud computing to a greater extent, it is applicable in networking, pattern recognition, data mining, wireless sensor networks, etc. The adaptability influences the use of bio-inspired algorithms to solve the major issues in cloud computing. The paper provides an extensive survey of bio-inspired algorithms for solving and optimising problems related to resource scheduling, load balancing, file searching and security in cloud computing. The work compares the performance, response time and cost optimisation of each algorithm applicable for cloud computing environment.

Special Issue: Bio-Inspired Optimization Techniques for High Performance Computing

Their inherent parallelism and scalability make these kinds of algorithmvery suitable for dynamically changing environments and systems, such as GridComputing, Cloud Computing, and peer-to-peer systems, which are character-ized by decentralized control, large scale and extreme dynamism. The availabil-ity of cheaper and better-performing distributed and parallel architectures haspermitted on the one hand to foster the use of bio-inspired algorithms for thesekinds of architecture, on the other hand to cope with complex problems, whichwere previously hardly solved owing to the large amount of needed time.This special issue is dedicated to high performance bio-inspired algorithmsfor solving hard real world problems and to the design and implementation ofdistributed computing systems using this kind of algorithms. The issue collectsthe revised and extended versions of three papers selected among those presentedat BADS 2010,