Bioinspired Algorithms

Swarm intelligence optimization algorithms demonstrate excellent global search capabilities and robustness in solving complex optimization problems. Based on observations and abstractions of sika deer ecological behavior, this paper proposes a Spotted Deer Optimization Algorithm (SDOA). This algorithm transforms sika deer behaviors such as seasonal migration, social status formation, arena competition, fawn learning, and pheromone regulation into a mathematical model to achieve a dynamic balance between global and local search. The paper first constructs a mathematical description of the sika deer population and a search strategy formula. Then, mathematical expressions for individual migration, status renewal, energy modeling, fawn perturbations, and pheromone regulation are derived in detail. Finally, complexity analysis and convergence proof demonstrate the theoretical feasibility and effectiveness of the algorithm.

In the current era, we can see that a lot of data lots of data is getting generated and it is getting stored in the cloud. Like earlier systems, people are not using a floppy disk, hard disk, and pen drive for storing the data. Each and every individual and organization are opting for cloud storage because of its flexibility. So, the medical field is also progressed and they are also storing their patient's sensitive data on the cloud. Benefits of storage there come to the security issues for protecting the privacy of the data. In this paper, we have implemented a framework to protect patients' sensitive information on the cloud. We have combined two metaheuristic algorithms, the Glowworm Swarm Optimization algorithm and the Whale Optimization algorithm, and developed GSOA that is a glowworm Swarm whale Optimization algorithm. The experiments are performed on the Cleveland data set.

Resumen En los últimos años, el interés por digitalizar los procesos críticos de diversos entornos intensivos, como la medicina, utilizando el paradigma del Internet de las Cosas (IoT), ha crecido sustancialmente. No obstante, al digitalizar un proceso crítico mediante una aplicación IoT, la aplicación IoT pasa también a ser crítica, lo que se ve reflejado en requisitos estrictos de Calidad de Servicio (QoS), que deben cumplirse para que funcione correctamente. Para optimizar esta QoS, el despliegue de los microservicios de la aplicación IoT es clave, especialmente en escenarios complejos y grandes. En este trabajo se presenta GAAP (Genetic Algorithm for Application Placement), un framework heurístico bioinspirado para optimizar dicho despliegue soportando grandes escenarios.

The present work formulates bioinspired metaheuristic optimization methods informed by biological functions including metabolism, ecology, and physiology. Unlike approaches based solely on genetic or swarm analogies, these methods are motivated by functional mechanisms that govern regulation, adaptation, and interaction in natural systems. Specifically, the frameworks draw inspiration from the Krebs cycle [1], the Calvin cycle [2], allelopathic regulation [3], and ecological dispersal. A total of thirteen algorithms are developed, each representing a distinct biological strategy and translated into computational procedures for search and adaptation. To assess their effectiveness, a comprehensive benchmark evaluation is conducted on widely used optimization problems, including the traveling salesman and knapsack problems [4]. The results indicate competitive performance across multiple metrics, with consistent advantages under uncertainty and in rugged fitness landscapes. Beyond solution quality, the models demonstrate robustness across instances, scalability with problem size, and improved interpretability through their explicit biological analogies. These findings suggest that functional biological processes provide a promising foundation for diversifying metaheuristic optimization and advancing the design of adaptable problem-solving methods.

Vehicular ad hoc network (VANET) is an ad hoc network system built on the idea of a mobile ad hoc network (MANET), where individual vehicles are linked to one another using wireless technology. However, the Network performance is affected by the routing problems, like high message latency, regular route interruptions, and inconsistent energy usage. To solve these issues a novel Bacteria foraging Optimized Self Secure based Rout(e)ing (BOSS-ROUTE) has been proposed for self-secure routing in VANET to find the secured optimal path. Optimization is the process of determining the optimal inputs to achieve the highest or lowest output at the lowest possible cost. Bacterial Foraging Optimization is able to find a much better spread of solutions and faster routing convergence. The suggested approach improves the energy efficiency of the VANET by finding optimal path to determine the best course of action. The proposed approach was implemented on NS2 simulator. The effectiveness of the proposed method is assessed using measures such as network delay, energy efficiency, throughput and packet delivery ratio. The observational findings shows that the BOSS-ROUTE achieves an end-to-end delay of 1.5 sec, whereas existing CORA, AODV-OLSR, ECBLTR technique achieves 3.0s, 3.7s and 4.5 s, respectively.

Nowadays, Vehicular communication is used in intelligent transmission applications. The number of vehicles used in a particular region has numerously increased energy consumption, computation delay, and
computation overhead. In this paper, Multi-Objective Optimization in Satellite Assisted UAVs (MO-SAUVs) is proposed under an improved Ant Colony Optimization (IACO) algorithm. The procedures that are considered for the process of MO are optimal logistics distribution, path prediction-based pheromone deposition, and evaporation. Using this method, effective region selection for the UAVs is performed which leads to improving the network energy efficiency by decreasing energy consumption and delay. The simulation is performed in NS2 and the proposed MO-SAUAVs method is compared with the TA-SAUAVs method and PL-SAUAVs method according to different parameters. The results show that the proposed MO-SAUAVs
method achieves lower computation delay (70ms to 110ms), higher energy efficiency (6% to 16%), lower energy consumption (7% to 14%), and packets lower computation overhead (500 packets to 700) when we
were compared with TA-SAUAVs and PL-SAUAVs

Resumo: O problema do caixeiro viajante é um problema clássico de otimização combinatorial, cuja resolução por busca exaustiva é inviável em alguns casos, como por exemplo, se considerarmos um número grande de cidades. Por ser aplicável a muitos problemas da vida real, o TSP é amplamente estudado, buscando-se abordagens mais eficiente para solucioná-lo. Neste trabalho é proposto um algoritmo memético para solução do TSP. O objetivo é mostrar que o uso de tal algoritmo possibilita encontrar uma solução satisfatório para uma instância do TSP em um tempo aceitável. O algoritmo memético se mostra adequado nessa circunstância pois combina operadores genéticos tradicionais e operadores de busca local, aumentando assim a qualidade das soluções. Serão apresentadas comparações entre diferentes combinações de representações de indivíduos e operadores. Ainda, no intuito de comprovar a eficiência do algoritmo proposto, serão apresentados resultados de testes que utilizam uma função de benchmark da conhecida biblioteca TSPLIB.

Biogeography is the study of the geographical dis tribution of biological organisms. Mathematical equations that govern the distribution of organisms were first discovered and developed during the 1960s. The mindset of the engineer is that we can learn from nature. This motivates the application of bio geography to optimization problems. Just as the mathematics of biological genetics inspired the development of genetic algorithms (GAs), and the mathematics of biological neurons inspired the development of artificial neural networks, this paper considers the mathematics of biogeography as the basis for the development of

Influence Maximization (IM) is a fundamental problem in social network analysis that seeks to identify a small set of highly influential nodes that can maximize the spread of information. Due to its NP-hard nature, finding an exact solution is computationally infeasible for largescale networks. To address this, this paper introduces an enhanced discrete Manta-Ray Foraging Optimization (MRFO) algorithm tailored for IM. The proposed method integrates degree, closeness, and betweenness centrality measures into the fitness function and introduces a fused centrality index to improve the identification of influential nodes. To handle the discrete search space, the continuous MRFO is adapted with novel discretization mechanisms. Experimental evaluations on five real-world networks (NetScience, Email, Hamsterster, Ego-Facebook, and Pages-PublicFigure) demonstrate that the proposed method achieves higher influence spread compared to existing baseline algorithms, with average improvements of 14.63%, 12.81%, 19.03%, 15.24%, and 18.76%, respectively. These results validate the effectiveness, robustness, and practical applicability of the proposed approach for large-scale IM.

Metaheuristic algorithms have emerged as essential tools for solving complex optimization problems across disciplines such as engineering, logistics, finance, and healthcare. Their ability to efficiently explore large, nonlinear, and uncertain search spaces makes them highly effective where traditional methods often fail.

This book, Metaheuristics Algorithms: Overview, Applications, and Modifications, offers a structured overview of key algorithmic families—including evolutionary, swarm-based, physics-inspired, and human-based approaches—supported by theoretical foundations, classifications, and real-world applications. Emphasis is placed on recent advancements, hybridizations, and performance-enhancing modifications.

Designed for students, researchers, and practitioners, the content balances academic rigor with practical relevance, aiming to guide both implementation and innovation in metaheuristic optimization.

I am grateful to my colleagues and reviewers for their valuable input, and to Reta N. Mussa for the attractive design of the book cover. My appreciation also extends to Deep Science Publishing for enabling its open-access dissemination. I hope this work contributes meaningfully to advancing research in computational intelligence and intelligent optimization.

The global rise in diabetes cases highlights the urgent need for effective, non-invasive blood glucose monitoring solutions. This study introduces a pioneering Bio-Impedance Sensing system, enhanced by Artificial Intelligence (AI) and optimized using the Bio-Inspired Puffer Fish Optimization (PFO) Algorithm, for real-time, noninvasive glucose estimation. Bio-impedance spectroscopy is utilized to detect glucose-induced changes in tissue electrical properties, while AI-based machine learning models analyse these signals to predict glucose levels with high accuracy. The Puffer Fish Optimization Algorithm plays a key role in feature selection and hyperparameter optimization, leading to improved model performance. A comprehensive dataset, consisting of bio-impedance signals and reference glucose levels obtained through standard invasive techniques, was used for model training and validation. The proposed system demonstrated a remarkable mean absolute error (MAE) of 3.2 mg/dL and a correlation coefficient (R) of 0.97, indicating exceptional predictive accuracy and reliability. This innovative approach presents a pain-free, cost-effective, and real-time solution for glucose monitoring, offering significant potential to enhance diabetes management. Future research will focus on further optimization of signal acquisition, refinement of AI models, and large-scale clinical trials to validate and enhance real-world applicability.

In this chapter one gives an introduction to different kinds of Particle Swarm Optimization (PSO) algorithms. One also introduces Artificial Neural Networks (ANN), Support Vector Machines (SVM) and Evolutionary Computing (EC) to show how PSO may be used to determine optimal parameters using an ANN or SVM regime, for classification of DNA-strings. In addition, PSO is used in the design of a SVM based clustering algorithm. Ant Colony Optimization (ACO) algorithms are also introduced in the chapter. Using ACO algorithms has been of great success to solve many discrete optimization and NP-hard problems, for instance the Travelling Salesman Problem (TSP). The behaviour of ants is also used, for instance, to design an algorithm for data clustering. We want to develop a similar application based on this clustering algorithm and compare it with the SVM one using PSO.

The Artificial Bee Colony algorithm is an innovative optimization technique inspired by the foraging behavior of honeybees. Its ability to balance exploration and exploitation makes it effective for addressing complex challenges, particularly in medicine. This paper explores its applications in medical image segmentation, disease detection, and biomedical signal processing. Notable achievements include improving tumor segmentation in noisy MRI scans and enhancing disease classification. However, challenges like high computational demands and scalability remain. Hybrid approaches, such as combining ABC with neural networks, show promise. Future research could focus on real-time healthcare applications and integrating ABC with the Internet of Medical Things. This study underscores the potential of ABC to drive significant advancements in healthcare.

Social network is one of the most prominent phenomena that greatly influence life in the present time. These media have changed the way people interact with others, the ways they communicate, and participate in global and local events. individuals on social network platform greatly influence each other's opinions and trends, but the degree of influence varies from one person to another greatly depending on many factors, such as fame, published content, target audience, and interaction with followers. This leads to varying influence of individuals on these platforms. Influential people can be opinion leaders on social platforms. In fact, these leaders constitute a large part of the process that influences the decisions of many individuals and companies, whether at the level of product selection or cultural and social trends. Discovering and identifying opinion leaders is very important for several reasons: directing public opinion, marketing and advertising, political and social analysis, and others. In this paper, we proposed a method to discover opinion leaders by focusing on the structure of social graphs and the position occupied by individuals. The centrality of nodes representing individuals were calculated by utilizing two essential measures: the degree centrality and betweenness centrality, but with different weights that were determined in a studied way. The method was tested on real-world dataset for three social networks and the results were promising when compared with the baseline.

Image registration is one of the popular image transformation models in satellite and medical imaging currently. Image registration refers to image mapping with two or more than images. The groundbreaking fusion of the whale optimization algorithm and nonrigid demons registration (WOA-NDR) is applied in the current work to improve image registration's precision and effectiveness. NDR is an effective method for aligning images that have pliable structures. Nevertheless, it frequently runs into issues with local minima and massive deformations. To address these issues, WOAwhich draws inspiration from whale hunting behavior-is integrated into the NDR architecture. The WOA-NDR approach intelligently explores the solution space, enhancing convergence and avoiding premature convergence. With the innovative WOA and NDR integration, the nonrigid image registration process is revolutionized and yields superior outcomes in terms of robustness and accuracy. The efficiency of the suggested strategy is demonstrated by experimental findings on a dataset of monomodal images. The obtained results are also compared with particle swarm optimization (PSO) based framework.

Mixed integer nonlinear programming (MINLP) addresses optimization problems that involve continuous and discrete/integer decision variables, as well as nonlinear functions. These problems often exhibit multiple discontinuous feasible parts due to the presence of integer variables. Discontinuous feasible parts can be analyzed as subproblems, some of which may be highly constrained. This significantly impacts the performance of evolutionary algorithms (EAs), whose operators are generally insensitive to constraints, leading to the generation of numerous infeasible solutions. In this article, a variant of the differential evolution algorithm (DE) with a gradientbased repair method for MINLP problems (G-DEmi) is proposed. The aim of the repair method is to fix promising infeasible solutions in different subproblems using the gradient information of the constraint set. Extensive experiments were conducted to evaluate the performance of G-DEmi on a set of MINLP benchmark problems and a real-world case. The results demonstrated that G-DEmi outperformed several stateof-the-art algorithms. Notably, G-DEmi did not require novel improvement strategies in the variation operators to promote diversity; instead, an effective exploration within each subproblem is under consideration. Furthermore, the gradient-based repair method was successfully extended to other DE variants, emphasizing its capacity in a more general context.

In recent years, significant progress has been made in the application of data-driven, learning-based approaches to fault detection in distributed networks. These methods are optimized for quickly detecting and identifying faulty instruments, whether originating from within a single vehicle or from a network of connected vehicles. This paper provides a preliminary review of typical Fault Detection and Identification (FDI) techniques, with a focus on platoons of vehicles arranged in a rectilinear formation using a leader-follower architecture. Specifically, this paper discusses the advantages and disadvantages of data-driven versus model-based methods for addressing the FDI problem. In particular, the main characteristics of a novel immunity-based bio-inspired data-driven technique are highlighted, and numerical simulations of a multi-vehicle system under normal and faulty conditions are presented to support the discussion.

A finalidade do método Rápido é determinar de modo objetivo e confiável o comprimento da maior pista de vento, ou fetch, em um corpo de água continental de grande superfície, como lagos e reservatórios. Constitui-se em um método gráfico inovador que deve ser aplicado diretamente sobre um mapa ou imagem de satélite dotado de escala gráfica. O método pode ser aplicado via traçado manual ou ainda, utilizando interface computacional. Além do caráter fundamental em permitir a determinação rápida de elementos de onda como a amplitude, o período e o comprimento, via equações paramétricas, um método rápido de determinação do fetch máximo permitiria classificar o corpo de água de acordo com a magnitude das maiores alturas de ondas geradas pelo vento. A aplicação do método independe de características geométricas dos contornos do corpo hídrico, como dendriticidade, sinuosidade e escala espacial. Pelo presente estudo o método será apresentado e validado pela aplicação ao reservatório de Itaipu.

Esse artigo apresenta uma adaptação do algoritmo genético na resolução do problema de alocação de turmas no campus Politécnico da Universidade Federal do Paraná. Para tanto, foram analisados diversos métodos, sendo o escolhido, devido sua eficiência e adaptabilidade, o algoritmo genético. O algoritmo desenvolvido, além de ser testado com dados reais e atuais da instituição, foi comparado com o processo atual aplicado no campus, obtendo resultados mais satisfatórios em relação ao mesmo. O sistema criado conseguiu alocar, através da inserção de novos conceitos e adaptações nos operadores genéticos do algoritmo genético clássico, 1348 turmas em 58 salas em diversos períodos e dias da semana de forma rápida e eficiente em função das restrições e particularidades do problema.

Este trabalho apresenta um estudo comparativo do desempenho de diferentes combinações de operadores genéticos comumente utilizados na resolução do Problema do Caixeiro Viajante. A avaliação considerou somente os custos obtidos pelas combinações e foi realizada com seis instâncias da versão simétrica do problema. Os testes foram feitos com o uso do elitismo para todas as possíveis combinações. Os resultados obtidos mostraram que, individualmente, os operadores Torneio (seleção), CX (Cruzamento) e Inversão (Mutação) foram os mais eficientes. Palavras-chave: algoritmos genéticos; inteligência artificial; otimização, problema do caixeiro viajante.

Bat Algorithm 8.1 Introduction Bat algorithm is an innovative or population-based technique which belongs to the swarm intelligence. It is also referred to as a metaheuristic algorithm developed by Yang [1]. The bat algorithm as a unique algorithm provides a suitable solution technique than numerous and prevalent classical and heuristic algorithms. The algorithm is used for quick decision making and for solving complex problems in diverse fields of operations ranging from engineering, business, transportation, and other fields of human endeavor. It is important to understand the communication and navigational pattern of bats while defining the algorithm since the algorithm is based on the micro-bats echolocation (EL) [1]. EL is an enchanting and captivating sonar propensity produced bats. The appealing wave of sound made by the bats is a great strength they often exhibit while searching for prey. The wave of sound is a formula they adopt not only in food search but serves other purposes [2]. For instance, while searching for food in a completely dark environment, there might be obstacles or dangers on their way to the food source. This can easily be detected in some magical manner as they can sense and discern possible danger on their way to the food source as shown in Fig. 8.1 [3]. The structure and flying position of a bat is shown in Fig. 8.1. BA is therefore a very powerful algorithm as it uses the frequency-tuning methodology for range intensification of the solutions in the population. At the same time, the BA system implements the instinctive skyrocketing or zooming procedure to maintain stability during the food search or exploration process. In developing the BA, the pattern of direction finding, manipulation, and exploration during prey hunting is taken into consideration by mimicking the disparities in terms of emission rates of pulse and intensity of sound released by the bats while hunting or searching for prey. The algorithm demonstrates a high level of efficiency with a distinctive swift start process (Fig. 8.2).

With technological advancements in 6G and Internet of Things (IoT), the incorporation of Unmanned Aerial Vehicles (UAVs) and cellular networks has become a hot research topic. At present, the proficient evolution of 6G networks allows the UAVs to offer cost-effective and timely solutions for real-time applications such as medicine, tracking, surveillance, etc. Energy efficiency, data collection, and route planning are crucial processes to improve the network communication. These processes are highly difficult owing to high mobility, presence of non-stationary links, dynamic topology, and energy-restricted UAVs. With this motivation, the current research paper presents a novel Energy Aware Data Collection with Routing Planning for 6G-enabled UAV communication (EADCRP-6G) technique. The goal of the proposed EADCRP-6G technique is to conduct energy-efficient cluster-based data collection and optimal route planning for 6G-enabled UAV networks. EADCRP-6G technique deploys Improved Red Deer Algorithm-based Clustering (IRDAC) technique to elect an optimal set of Cluster Heads (CH) and organize these clusters. Besides, Artificial Fish Swarm-based Route Planning (AFSRP) technique is applied to choose an optimum set of routes for UAV communication in 6G networks. In order to validated whether the proposed EADCRP-6G technique enhances the performance, a series of simulations was performed and the outcomes were investigated under different dimensions. The experimental results showcase that the proposed model outperformed all other existing models under different evaluation parameters.

Resumo. Este trabalho propõe uma formulação matemática de programação linear binária para distribuição dos encargos didáticos em uma instituição de ensino que procura maximizar a satisfação dos professores. A formulação, em duas variantes, é implementada na designação de professores às turmas ofertadas pelo Departamento de Matemática da Universidade Federal do Paraná-UFPR. Foi desenvolvido um sistema que permite que os professores preencham on-line um questionário com suas restrições de horários e preferências por turmas. Após a coleta das informações o sistema gera as matrizes necessárias para implementar o modelo matemático. Para analisar os resultados, define-se um índice médio de satisfação dos professores. Palavras-chave. Modelagem matemática, programação linear inteira, distribuição de encargos didáticos.

Tasks allocation is an important step for obtaining high performance in distributed computing system (DCS). This article attempts to develop a mathematical model for allocating the tasks to the processors in order to achieve optimal cost and optimal reliability of the system. The proposed model has been divided into two stages. Stage-I, makes the ‘n' clusters of set of ‘m' tasks by using k-means clustering technique. To use the k-means clustering techniques, the inter-task communication costs have been modified in such a way that highly communicated tasks are clustered together to minimize the communication costs between tasks. Stage-II, allocates the ‘n' clusters of tasks onto ‘n' processors to minimize the system cost. To design the mathematical model, executions costs and inter tasks communication costs have been taken in the form of matrices. To test the performance of the proposed model, many examples are considered from different research papers and results of ...

Atualmente, diversas Instituicoes de ensino possuem uma grande dificuldade com relacao a geracao de horarios escolares, visto que, para gerar estes horarios manualmente e necessario respeitar uma serie de restricoes. Desta forma, um problema com tais caracteristicas exige, para a sua resolucao, metodos para decomposicao e busca de solucoes. Neste contexto, a automatizacao do processo de geracao de horario escolar pode trazer beneficios, justificando, deste modo, investigacoes nesta area. Partindo deste principio e utilizando o conceito de Problemas de Satisfacao de Restricoes (PSR), esta pesquisa visa o desenvolvimento de uma ferramenta que tratara todas as informacoes pertinentes ao problema citado, a fim de gerar automaticamente os horarios, respeitando a cada restricao imposta ao longo do processo. A ferramenta sera desenvolvida considerando um ambiente especifico de uma universidade e curso particular.

The Transmission Expansion Planning (TEP) entails to determine all the changes needed in the electric transmission system infrastructure in order to allow the balance between the projected demand and the power supply, at minimum investment and operational costs. In some type of TEP studies, the DC model is used for the medium and long term time frame, while the AC model is used for the short term. This chapter proposes a load shedding based TEP formulation using the DC and AC model, and four Particle Swarm Optimization (PSO) based algorithms applied to the TEP problem: Global PSO, Local PSO, Evolutionary PSO, and Adaptive PSO. Comparisons among these PSO variants in terms of robustness, quality of the solution, and number of function evaluations are carried out. Tests, detailed analysis, guidelines, and particularities are shown in order to apply the PSO techniques for realistic systems.

In the advent of the cyber world, all know that cyber security is randomly used research area for researchers to secure host, network, and data because of increasingly complex attacks. In the advent of anomaly-based intrusion detection system, various techniques are applied to detect intrusion on system or network. This approach attains an extreme detection rate and accuracy but there may be overhead acquired to build and training them. The objective of this paper is to detect the intrusion of a system by proposing a Data mining technique which is based on supervised learning algorithm for training dataset. Artificial neural network (ANN) and Ant Colony Optimization (ACO) with feature selection are the basics of the proposed scheme. ACO work on a population-based algorithm and is motivated by the pheromone trail laying behavior of real ants, in which NSL-KDD Cup99 Dataset is used. Empirical Results clearly explain that the proposed system can attain an overall detection rate of 88% and time complexity of 0.343 sec, which is satisfactory when compared to other anomaly-based schemes.

Este trabalho trata do problema de programação de horários em escolas. Dada sua natureza combinatória, ele é resolvido por meio de dois algoritmos metaeurísticos, um baseado em Simulated Annealing e outro em GRASP. Ambos possuem parâmetros auto-adaptativos, dispensando, assim, a calibragem destes. Para testálos são utilizados dados reais do departamento de uma universidade. São apresentados resultados computacionais, comparando-se as soluções produzidas pelos algoritmos propostos com aquelas geradas manualmente pela instituição de ensino. Os resultados obtidos mostram a eficiência dos métodos desenvolvidos perante as soluções manuais e a superioridade do Simulated Annealing, em comparação com o GRASP para as instâncias tratadas.

Este trabalho trata do problema de programação de horários em escolas. Dada sua natureza combinatória, ele é resolvido por meio de dois algoritmos metaeurísticos, um baseado em Simulated Annealing e outro em GRASP Ambos possuem parâmetros auto-adaptativos, dispensando, assim, a calibragem destes. Para testá-los são utilizados dados reais do departamento de uma universidade. São apresentados resultados computacionais, comparando-se as soluções produzidas pelos algoritmos propostos com aquelas geradas manualmente pela instituição de ensino. Os resultados obtidos mostram a eficiência dos métodos desenvolvidos perante as soluções manuais e a superioridade do Simulated Annealing, em comparação com o GRASP para as instâncias tratadas.

Este trabalho trata do problema de alocação de salas. Uma vez que este problema é NP-difícil, muitos métodos heurísticos têm sido propostos para resolvê-lo. Simulated Annealing (SA) e Busca Tabu (BT) são exemplos de tais métodos que têm sido aplicados com sucesso na resolução deste problema. Esta classe de heurísticas encontra uma boa solução melhorando uma alocação inicial através de técnicas de pesquisa em vizinhança. Contrariamente às heurísticas convencionais, SA e BT permitem movimentos de piora como forma de escapar de ótimos locais. Neste artigo relata-se uma experiência com a utilização destas técnicas e propõe-se uma técnica híbrida SA+TS, a qual combina as características mais apropriadas de SA e BT de forma a obter um procedimento mais eficaz. A eficiência dessa combinação é avaliada comparando-a com as versões puras dos métodos envolvidos.

This paper proposes a new population-based simplex method for continuous function optimization. The proposed method, called Adaptive Population-based Simplex (APS), is inspired by the Low-Dimensional Simplex Evolution (LDSE) method. LDSE is a recent optimization method, which uses the reflection and contraction steps of the Nelder-Mead Simplex method. Like LDSE, APS uses a population from which different simplexes are selected. In addition, a local search is performed using a hyper-sphere generated around the best individual in a simplex. APS is a tuning-free approach, it is easy to code and easy to understand. APS is compared with five state-of-the-art approaches on 23 functions where five of them are quasi-real-world problems. The experimental results show that APS generally performs better than the other methods on the test functions. In addition, a scalability study has been conducted and the results show that APS can work well with relatively high-dimensional problems.

Themainpurposeofthisarticleistodemonstratehowevolutionstrategyoptimizerscanbeimproved byincorporatinganefficienthybridizationschemewithrestartstrategyinordertojumpoutoflocal solutionregions.Theauthorsproposeahybrid(μ,λ)ES-NMalgorithmbasedontheNelder-Mead (NM)simplexsearchmethodandevolutionstrategyalgorithm(ES)forunconstrainedoptimization. Atfirst,amodifiedNM,calledAdaptiveNelder-Mead(ANM)isusedthatexhibitsbetterproperties thanstandardNMandself-adaptiveevolutionstrategyalgorithmisappliedforbetterperformance,in additiontoanewcontractioncriterionisproposedinthiswork.(μ,λ)ES-NMisbalancingbetween theglobalexplorationoftheevolutionstrategyalgorithmandthedeepexploitationoftheNelder-Meadmethod.Theexperimentresultsshowtheefficiencyofthenewalgorithmanditsabilityto solveoptimizationproblemsintheperformanceofaccuracy,robustness,andadaptability.

Neste trabalho foi realizado um estudo comparativo entre as metodologias de otimização Gradiente Reduzido Generalizado (GRG) e Algoritmo Genético (AG) para a otimização de processos com múltiplas respostas. Para estimar os parâmetros que minimizam a função objetivo foram utilizadas respostas geradas por planejamento de experimentos de forma aglutinada, as quais foram incorporadas à função objetivo. Os estudos de caso utilizados foram baseados em trabalhos selecionados na literatura e, para cada experimento selecionado, foi realizada a otimização dos valores dos parâmetros do processo utilizando as duas metodologias, o GRG, por meio de uma planilha do Microsoft Excel e o AG utilizando o software Scilab. Foram realizadas 10 replicações e calculada a média dos resultados obtidos. A comparação entre os métodos foi realizada com base em medidas de desempenho, por meio da distância média percentual. O AG apresentou melhores resultados em comparação com o GRG.

Themainpurposeofthisarticleistodemonstratehowevolutionstrategyoptimizerscanbeimproved byincorporatinganefficienthybridizationschemewithrestartstrategyinordertojumpoutoflocal solutionregions.Theauthorsproposeahybrid(μ,λ)ES-NMalgorithmbasedontheNelder-Mead (NM)simplexsearchmethodandevolutionstrategyalgorithm(ES)forunconstrainedoptimization. Atfirst,amodifiedNM,calledAdaptiveNelder-Mead(ANM)isusedthatexhibitsbetterproperties thanstandardNMandself-adaptiveevolutionstrategyalgorithmisappliedforbetterperformance,in additiontoanewcontractioncriterionisproposedinthiswork.(μ,λ)ES-NMisbalancingbetween theglobalexplorationoftheevolutionstrategyalgorithmandthedeepexploitationoftheNelder-Meadmethod.Theexperimentresultsshowtheefficiencyofthenewalgorithmanditsabilityto solveoptimizationproblemsintheperformanceofaccuracy,robustness,andadaptability.

A técnica de modelagem recentemente desenvolvida denominada Paramétrica Bidimensional permite a aplicação de uma equação paramétrica no espaço bidimensional, com as vantagens de ser condicionada somente por informações dinâmicas de vento (intensidade e direção) e fetch e por gerar resultados em um tempo bastante reduzido quando comparado aos modelos numéricos de base física. Um fator a ser superado, no entanto, é a variabilidade dos resultados emitidos pelas equações paramétricas. Pelo presente trabalho é feita uma análise crítica a respeito da topologia dessas equações, visando a obtenção de equações paramétricas mais confiáveis de modo a aproveitar as potencialidades da técnica de modelagem Paramétrica Bidimensional em aplicações como sistemas de previsão em tempo real.

A alocação de horários universitários é um problema enfrentado pelas instituições de ensino em todo o mundo, no inicio de cada período, mobilizando uma quantidade significativa de pessoas, tempo e esforço. Não há garantias de que o resultado é um horário de qualidade, atendendo as necessidades dos alunos e professores. Da mesma forma, não há garantias que os recursos necessários (prédios, salas, laboratórios, etc) são alocados da melhor forma. Aplicando técnicas de programação linear, desenvolvemos uma solução genérica para produzir de forma automática, horários escolares considerando as limitações dos professores, necessidades dos alunos e disponibilidade de salas.

Fizemos adaptações na solução genérica para dois casos de estudo. O primeiro caso de estudo foi para os cursos de licenciatura do Departamento de Engenharia Informática e Sistemas de Informação da Universidade Lusófona de Humanidades e Tecnologia, situada em Lisboa. O segundo caso de estudo foi para os datasets do ITC 2007, concurso internacional do problema de alocação do horário universitário.

Para a Universidade Lusófona a nossa formulação obteve um horário de qualidade significativamente superior do que aquele que elaborado de forma manual pela direção dos cursos. Para o ITC 2007 obtivemos resultados próximos aos trabalhos que ficaram nos primeiros lugares da competição.

Artificial intelligence and machine learning are in widespread use nowadays in order to develop automatic and precise models for different tasks especially in the Internet. In this paper, by the use of machine learning techniques, an intrusion detection system is proposed. An intrusion detection system is involved extensive mass of data; such data is naturally characterized with repetitions and noise which leads to the reduction in the stability and the accuracy of the intrusion detection system. Hence, the issue of reducing features dimensions for achieving a smaller subset of features which can precisely express the results and status of network observations has attracted a lot of researchers' attention. In the proposed method, by using gradually feature removal method, 16 critical features were selected for representing various network visits. By combining ant colony algorithm and ensemble of decision trees, we proposed an efficient and stable classifier for judging a network visit to be normal or not. Despite the selection of 16 features, high accuracy, i.e. 99.92%, and the average value of Matthews correlation coefficient 0.91 are obtained.

This research paper presents a modified version of the Elephant Herding Optimization (EHO) algorithm, referred to as the Modified Elephant Herding Optimization (MEHO) algorithm, to enhance its global performance. The focus of this study lies in improving the balance between exploration and exploitation within the algorithm through the modification of two key operators: the matriarch updating operator and the separation updating operator. By reframing the equations governing these operators, the proposed modifications aim to enhance the algorithm’s ability to discover optimal global solutions. The MEHO algorithm is implemented in the MATLAB environment, utilizing MATLAB R2019a. To assess its efficacy, the algorithm is subjected to rigorous testing on various standard benchmark functions. Comparative evaluations are conducted against the original EHO algorithm, as well as other established optimization algorithms, namely the Improved Elephant Herding Optimization (IEHO) algorithm, Par...

In electric vehicle technology, battery energy conservation is paramount due to the dependency of all system operations on the available battery. The proportional, integral and derivative (PID) controller parameters in the electric power assisted steering system for electric vehicle need to be tuned with the optimal performance setting so that less current is needed for its operation. This proposed two methods under the umbrella of swarm-intelligence technique namely particle swarm optimization (PSO) and ant colony optimization (ACO) in order to reduce current consumption and to improve controller performance. The investigation involves an analysis on the convergence behavior of both techniques in search for accurate controller parameters. A comprehensive assessment on the assist current supplied to the assist motor of the system is also presented. Investigation reveals that the proposed controllers, PID-PSO and PID-ACO are able to reduce the assist current supplied to the assist motor as compared to the conventional PID controller. This study also demonstrate the feasibility of applying both swarm-intelligence tuning method in terms of reduced time taken to tune the PID controller as compared to the conventional tuning method.

Esta obra é de natureza digital (e-book). Versões impressas são permitidas, não tendo a Bookerfield Editora qualquer responsabilidade pela confecção e distribuição de exemplares físicos deste conteúdo. Todos os manuscritos da obra passaram por rigorosa avaliação cega pelos pares, baseadas em critérios científicos e imparciais, recebendo a aprovação após atender os critérios técnicos estabelecidos pelo Conselho Editorial. Todo o conteúdo do livro e de artigos individuais é de responsabilidade exclusiva de seus respectivos autores, não sendo a Bookerfield Editora responsável por quaisquer eventuais irregularidades. Situações como plágio, má conduta ética/científica ou dados e resultados fraudulentos são de responsabilidade do autor, comprometendo-se a Bookerfield Editora em investigá-las rigorosamente e tomar as ações cabíveis. O download, compartilhamento e referenciação da obra são permitidos mediante atribuição de crédito aos autores e à Editora. A comercialização desta obra é expressamente proibida. Editora Chefe O conteúdo deste livro está licenciado sob uma licença de Marcia A. A. Marques Atribuição Creative Commons. Atribuição-Não-Comercial Coordenadora Editorial Não Derivativos 4.0 Internacional (CC BY-NC-ND 4.0).

, { 2 rudini, 3 guilherme}@dcc.ufla.br Resumo. Este artigo apresenta uma solução algorítmica para o Problema de Alocação de Salas (PAS) utilizando a meta-heurística Simulated Annealing (Têmpera Simulada). A solução por meta-heurísticas foi escolhida, visto que o PAS é um problema de otimização NP-Difícil [16]. Além disso, escolheu-se a Têmpera Simulada por já haverem outros estudos com esta abordagem para o PAS. Em comparação com esses estudos, os resultados foram satisfatórios, visto que o método permite movimentos de piora como forma de escapar de ótimos locais. Para implementação do algoritmo, usou-se a linguagem de programação Java e três conhecidas instâncias do PAS para testes. Palavras-chaves: Problema de Alocação de Salas, Simulated Annealing, meta-heurísticas, problema NP-Difícil.

This research paper presents a modified version of the Elephant Herding Optimization (EHO) algorithm, referred to as the Modified Elephant Herding Optimization (MEHO) algorithm, to enhance its global performance. The focus of this study lies in improving the balance between exploration and exploitation within the algorithm through the modification of two key operators: the matriarch updating operator and the separation updating operator. By reframing the equations governing these operators, the proposed modifications aim to enhance the algorithm’s ability to discover optimal global solutions. The MEHO algorithm is implemented in the MATLAB environment, utilizing MATLAB R2019a. To assess its efficacy, the algorithm is subjected to rigorous testing on various standard benchmark functions. Comparative evaluations are conducted against the original EHO algorithm, as well as other established optimization algorithms, namely the Improved Elephant Herding Optimization (IEHO) algorithm, Par...

Bio-Inspired optimization algorithms are inspired from principles of natural biological evolution and distributed collective of a living organism such as (insects, animal, …. etc.) for obtaining the optimal possible solutions for hard and complex optimization problems. In computer science Bio-Inspired optimization algorithms have been broadly used because of their exhibits extremely diverse, robust, dynamic, complex and fascinating phenomenon as compared to other existing classical techniques.This paper presents an overview study on the taxonomy of bio-inspired optimization algorithms according to the biological field that are inspired from and the areas where these algorithms have been successfully applied