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![Ants' stigmergic behaviour in finding the shortest route between food and nest [49].](profile/Hazem-Ahmed-6/publication/264457775/figure/fig1/AS:392368047050753@1470559240305/Ants-stigmergic-behaviour-in-finding-the-shortest-route-between-food-and-nest-49.png)
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Nature has always been a rich inspirational source over the ages, with much still to
learn from and discover about. Swarm Intelligence (SI) is a relatively new and potentially
promising branch of Artificial Intelligence that is used to model the collective intelligent
behavior of social swarms in nature. This technical report provides a general int...
Context in source publication
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... that happened to pick the shortest route to food will be the fastest to return to the nest, and will reinforce this shortest route by depositing food trail pheromone on their way back to the nest. This route will gradually attract other ants to follow, and as more ants follow the route, it becomes more attractive to other ants as shown in Figure 1. This autocatalytic or positive feedback process is an example of a self- organizing behaviour of ants in which the probability of an ant's choosing a route increases as the count of ants that already passed by that route increases. ...
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The study addresses the optimization of land transportation in the context of vehicle routing, a critical aspect of transportation logistics. The specific objectives are to employ various meta-heuristic optimization techniques, including Genetic Algorithms (GA), Ant Colony Optimization (ACO), Firefly Algorithm (FA), Particle Swarm Optimization (PSO...
Swarm intelligence (SI) represents a paradigm in computational and decision-making processes derived from the collective behavior of decentralized agents such as insects, birds, and fish. This study explores the comprehensive application and evolution of SI within the decision sciences, emphasizing its role in enhancing decision-making across vario...
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![Figure 1 (b): An obstacle placed on the way between nest and food [9]...](profile/Rebika-Rai/publication/310314002/figure/fig1/AS:428726975504385@1479227884750/b-An-obstacle-placed-on-the-way-between-nest-and-food-9-16-17-21-22-24_Q320.jpg)
![Figure 1 (c): Ants randomly choosing the path [9] [16] [17] [21] [22]...](profile/Rebika-Rai/publication/310314002/figure/fig2/AS:428726975504386@1479227884793/c-Ants-randomly-choosing-the-path-9-16-17-21-22-24_Q320.jpg)
The biologically inspired world comprising of social insect metaphor for solving out wide range of dilemma has become potentially promising area in most recent duration focusing on indirect or direct coordination's among diverse artificial agents. Swarm [8] apparently is a disorganized collection / population of moving individual that tends to clus...
Citations
... Insect − > Swarms: Wasps, bees, fireflies, ants, and termites are representing the prominent examples of swarm behavior [15,16], which create an emergent system from and through interactions of 'dumb' single insect entities. Similar processes operate in bird flocks (simulated by boids), fish schools, and animal herds. ...
This contribution serves as a brief primer in understanding emergence in not only biological systems. The latest progress that has been reached in the understanding of the operation of massively-parallel computing environments is revealing the critical importance of the decoding of multi-scale emergent systems that are operating within biological systems in all of their functional scales. Recently studied artificial emergent systems demonstrated unexpected insights into the capabilities of biological systems in their ability to recreate themselves and their adaptation to ever-changing environments. Simultaneously, it is a well-known fact that biological systems are capable of keeping their functionality and adapting to ever-changing conditions while their computing substrate is being 133 continuously repaired and even replaced. These observations are converging towards research, which will be partially reviewed in this contribution, that is decoding the very mathematical principles of error-resilient computations existing within massively-parallel computing environments. As already said, the prominent example is provided by all living systems. In this context, ecosystems could be understood within this framework as goal-oriented systems operating above the ever-changing substrate made of all species living within them.
... In the same space, new instances are mapped, and their anticipated class is determined by which side of the hyperplane they fall on. Support vectors are a relatively small subset of the training data that determines the SVM hyperplane, and the remaining training data has no effect on the final classifier [12,13]. ...
... In general, metaheuristic algorithms can be classified into four main categories [4]: Swarm Intelligence (SI) optimization algorithms, Physics-based Algorithms (PHA), Evolutionary Algorithms (EA) and Human-based optimization algorithms. SI algorithms are inspired by the herd behavior of animal groups and are implemented by modeling collective behavior and interactions between group members [6,7]. For example, Particle Swarm Optimization (PSO) [8], Grey Wolf Optimizer (GWO) [9], Hippo Swarm Optimization (HSO) [10] and Whale Optimization Algorithm (WOA) [11]. ...
... Scout population individuals move through Eq. (7). ...
The Dwarf Mongoose Optimization algorithm is a metaheuristic approach designed to solve single-objective optimization problems. However, DMO has certain limitations, including slow convergence rates and a tendency to get stuck in local optima, particularly when applied to multimodal and combinatorial problems. This paper introduces an enhanced version of the DMO, referred to as HDMO, which is based on a hybrid strategy. Firstly, a sine chaotic mapping function is integrated to enhance the diversity of the initial population. Secondly, the study aims to improve the algorithm’s performance through the integration of nonlinear control, adaptive parameter tuning, hybrid mutation strategies, and refined exploration–exploitation mechanisms. To evaluate the performance of the proposed HDMO, we conducted tests on the CEC2017, CEC2020, and CEC2022 benchmark problems, as well as 19 engineering design problems from the CEC2020 real-world optimization suite. The HDMO algorithm was compared with various algorithms, including (1) highly cited algorithms such as PSO, GWO, WOA and SSA; (2) recently proposed advanced algorithms, namely, BOA, GBO, HHO, SMA and STOA; and (3) high-performance algorithms like LSHADE and LSHADE_SPACMA. Experimental results demonstrate that, compared to other algorithms, HDMO exhibits superior convergence speed and accuracy. Wilcoxon rank-sum test statistics confirm the significant performance improvement of HDMO, highlight its potential in practical engineering optimization and design problems.
... The convergence of these movements reveals an exciting future. Imagine real-time processing at the edge using quantum-powered algorithms that improve swarm intelligence systems' capacity for learning [33]. This combination may result in unprecedented levels of effectiveness, flexibility, and problem-solving ability. ...
Artificial intelligence (AI) and digital twins are acknowledged as a revolutionary force transforming sectors such as manufacturing, healthcare, and urban planning. This chapter explores the mutually beneficial interaction between digital twins and AI, providing insights into the distinct possibilities of each technology. It looks at how real-time observation, assessment, and prediction of physical system behaviors are made possible by digital twins acting as a link between the digital and physical domains. The chapter focuses on AI’s ability to use computer vision, natural language processing, and machine learning (ML) to examine enormous data sets. The story demonstrates the possibilities of digital twins and AI by fusing their respective advantages. Digital twins’ predictive capabilities are improved by AI algorithms, which help businesses to detect mistakes, streamline workflows, and increase productivity. The chapter discusses the difficulties and moral questions this convergence raises, such as data security, privacy, and prejudice in AI systems. It stresses the changing responsibilities that humans play in the symbiotic connection and talks about ethical frameworks and laws intended to reduce these hazards. It also emphasizes the significance of ethical innovation and human-AI collaboration. The chapter predicts possible advancements and societal effects that may arise from the continued integration of AI and digital twins.
... The PSO algorithm finds optimal solutions using a set of flying particles with velocities that are dynamically adjusted according to their historical performance, neighboring particles, and especially the "leader" in the search space. Each particle looks in a specific direction and then, by communicating with other particles, identifies the particle in the best location [44,45]. Figure 4 shows the basic flowchart of the PSO algorithm. ...
... The PSO algorithm finds optimal solutions using a set of flying particles with velocities that are dynamically adjusted according to their historical performance, neighboring particles, and especially the ʺleaderʺ in the search space. Each particle looks in a specific direction and then, by communicating with other particles, identifies the particle in the best location [44,45]. Figure 4 shows the basic flowchart of the PSO algorithm. ...
Uninterruptible Power Supplies (UPSs) protect electronic equipment by delivering consistent power. Among the core components of a UPS is the inverter, which converts stored DC energy from batteries into AC power. This work focuses on a cascaded multilevel inverter topology for its ability to reduce voltage Total Harmonic Distortion (THD), which is essential for maintaining UPS efficiency and power quality. Using an ANFIS (Adaptive Neuro-Fuzzy Inference System) model, enhanced with the Particle Swarm Optimization (PSO) algorithm, the switching angles were optimized to minimize THD. This work focused on an online UPS with a seven-level inverter structure powered by three LifePo4 S17 batteries, with critical load levels (100%, 95%, 50%, 15%, and 5%) represented in 35 experimental cases. The experimental design allowed the ANFIS-PSO model to adapt to varying voltages, achieving robust THD reduction. The results demonstrated that this combination of ANFIS and PSO provided effective angle optimization, with a low standard deviation of 0.06 between the training and simulated %THD, highlighting the process’s accuracy. The analysis showed that, in most cases, the simulated THD values closely aligned with, or even improved upon, the calculated values, with discrepancies not exceeding 0.2%. These findings support the ANFIS-PSO model’s potential in enhancing power electronics applications, particularly in critical sectors like renewable energy and power transmission, where THD minimization is crucial.
... All together, it gradually led to the understanding that intelligence can operate without the presence of a nervous system and brain. Vital examples encompass: insect colonies, for example, ants or wasps; amoebas like Dictyostelium discoideum [1]; axolotls (salamander subspecies); bacterial biofilms; fish schools, etc. Swarms [2], stigmergy [3], and those derived from agent-based modeling (ABM) [4] are representing a set of very useful methods in studying self-organizing [5], emergent systems [6]. ...
Man-made systems, including artificial intelligence (AI) and machine learning (ML) methods, are usually constructed using mechanistic approaches, which inevitably fail with a failure of any of their single constituting components. Contrary to them, biological systems are typically self-organizing emergent systems operating far-from-equilibrium and capable of self-repair. The outputs of research from experimental biology, behavior of insect swarms, morphological growth, limb regrowth, and other areas are confirming the above statement. This leads us to the central question of this chapter: “Can intelligence be achieved without the presence of neurons and brain structures?” That is why research on emergent information processing (EPI) is reviewed and deepened in this contribution. What are the constituting elements of the Life? According to this theoretical research, it is hypothesized that, using a certain level of abstraction, the Life is created by a set of microprocesses running above a matrix, which cease to exist along with the matrix and processes governing it. Let us see where it takes us using the open-source Python cellular automata simulating software GoL-N24 v1.4.
... Swarm intelligence optimization algorithms can be implemented by examining the possible behaviors of individuals in a population and employing mathematical modeling to construct the operational mechanism of the population system. This includes analyzing the cooperation and competition among individuals within the population and the interaction between the population and the external environment [14,15]. ...
... Exponential and binomial crossovers are frequently employed crossover strategies. The binomial crossover is expressed According to Equation (14): ...
This paper introduces a novel hybrid optimization algorithm, PDO-DE, which integrates the Prairie Dog Optimization (PDO) algorithm with the Differential Evolution (DE) strategy. This research aims to develop an algorithm that efficiently addresses complex optimization problems in engineering design and network intrusion detection systems. Our method enhances the PDO's search capabilities by incorporating the DE's principal mechanisms of mutation and crossover, facilitating improved solution exploration and exploitation. We evaluate the effectiveness of the PDO-DE algorithm through rigorous testing on 23 classical benchmark functions, five engineering design problems, and a network intrusion detection system (NIDS). The results indicate that PDO-DE outperforms several state-of-the-art optimization algorithms regarding convergence speed and accuracy, demonstrating its robustness and adaptability across different problem domains. The PDO-DE algorithm's potential applications extend to engineering challenges and cybersecurity issues, where efficient and reliable solutions are critical; for example, the NIDS results show significant results in detection rate, false alarm, and accuracy with 98.1%, 2.4%, and 96%, respectively. The innovative integration of PDO and DE contributes significantly to stochastic optimization and swarm intelligence, offering a promising new tool for tackling diverse optimization problems. In conclusion, the PDO-DE algorithm represents a significant scientific advancement in hybrid optimization techniques, providing a more effective approach for solving real-world problems that require high precision and optimal resource utilization.
... Subsequently, the pheromone array of each iteration of calculation is updated according to the formula (Eq.11). After completing the aforementioned task, the probability calculation is initiated, using the established pheromone array , and then advancing to subsequent iterations of computation [28,29]. ...
... Examples of biologically observed agent systems: D. discoideum [30], stigmergy [31], and swarms [32]. Introduction [33,34], advanced [35,36], and agent-based software [34,37]. ...
... MPCs have the proven potential [1,[30][31][32] to process information in so-far poorly understood, distributed forms, which are computationally richer and simultaneously very robust when compared to classical logic gates used in designed computing devices. ...
... The morphological growth of living organisms represents the best example. It is known that Dictyostelium discoideum [1,30] (slime mold) solves complicated spatio-temporal tasks while searching for food; the same goes for ants and other social insects [32]. Additionally, D. discoideum is capable of assembling single cells into a fruiting body using simple signaling techniques, which serves as an example of a primitive anatomy plan. ...
Science is currently becoming aware of the challenges in the understanding of the very root mechanisms of massively parallel computations that are observed in literally all scientific disciplines, ranging from cosmology to physics, chemistry, biochemistry, and biology. This leads us to the main motivation and simultaneously to the central thesis of this review: “Can we design artificial, massively parallel, self-organized, emergent, error-resilient computational environments?” The thesis is solely studied on cellular automata. Initially, an overview of the basic building blocks enabling us to reach this end goal is provided. Important information dealing with this topic is reviewed along with highly expressive animations generated by the open-source, Python, cellular automata software GoL-N24. A large number of simulations along with examples and counter-examples, finalized by a list of the future directions, are giving hints and partial answers to the main thesis. Together, these pose the crucial question of whether there is something deeper beyond the Turing machine theoretical description of massively parallel computing. The perspective, future directions, including applications in robotics and biology of this research, are discussed in the light of known information.
... PSO has surpassed other computational techniques like GA and BA [12]- [14]. This is primarily because it is easy to implement, converges quickly, is robust to control parameters, and is computationally efficient [15]. However, it has the drawback of having its particles imprisoned in a local minimum as opposed to a global minimum, making the local minimum to be the outcome. ...
A key factor in the design of a car is the comfort and safety of its passengers. The quarter-car suspension system is a feature of the car that ensures load-carrying capacity as well as comfort and safety. It comprises links, springs, and shock absorbers (dampers). Due to its significance, several research has been conducted, to increase its road handling and holding capability while trying to keep its cost moderate. To enhance customer comfort and load carrying, the road holding capacity of an active quarter car suspension was improved/controlled in this study, using the Global Best Inertia Weight Modified Particle Swarm Optimization Algorithm. The observation of the closed loop and open loop systems after designing and simulating on MATLAB reveals a significant improvement in the closed loop system's road holding ability compared to the open loop, in that, when the system was subjected to pothole, the deflection of sprung mass reached steady state in 37.37 seconds as opposed to 7000 seconds for the open loop.
