Top PDF Uncovering the social interaction network in swarm intelligence algorithms

Uncovering the social interaction network in swarm intelligence algorithms

Uncovering the social interaction network in swarm intelligence algorithms

swarm algorithms as systems, removing the algorithm particularities from the analyses while focusing on the structure of the swarm social interaction. Introduction Swarm intelligence refers to the global order that emerges from simple social com- ponents interacting among themselves (Bonabeau et al. 1999 ; Kaufmann 1993 ; Vicsek 2001 ; Kennedy and Eberhart 2001 ; Engelbrecht 2006 ). In the past three decades, swarm intelligence has inspired many algorithmic models (i.e., computational swarm intelli- gence), allowing us to understand social phenomena and to solve real-world problems (Engelbrecht 2006 ). The field of computational intelligence has witnessed the develop- ment of various swarm-based techniques that share the principle of social interaction while having different natural inspirations such as ants (Dorigo and Di Caro 1999 ), fishes (Bastos-Filho et al. 2008 ), fireflies (Yang 2009 ), birds (Kennedy and Eberhart 1995 ), cats (Chu et al. 2006 ), to name a few. Though researchers have studied such techniques in detail, the absence of general approaches for assessing these systems prevents us from © The Author(s). 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly
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Optimization of a Five Echelon Supply Chain Network using Particle Swarm Intelligence Algorithms

Optimization of a Five Echelon Supply Chain Network using Particle Swarm Intelligence Algorithms

communication. [1]. Just-in-time production system adopted by many companies is a remarkable illustration of reliable SC which helped reduce the CC. TC at initial stages would seem high, to maintain a low inventory levels across all echelons but is justifiable with the benefits and would continuously optimize in long term [11]. In current scenario, industries are using the resources optimally for cost computation and enhancing its profit margins. [12]. Researchers working on resource optimization deploy various evolutionary algorithms that mimic nature. Particle swarm intelligence optimization (PSO) is an evolutionary algorithm developed by Eberhart and Kennedy in 1995 that mimics the behavior of swarm of birds or school of fish. Sathish Kumar et al. (2018) developed a three echelon SCM using goal programming and used PSO to optimize the resources [14]. PSO explores and exploits the solution space thoroughly very efficiently that it is usually employed to solve problems of complex nature [13]. PSO works on its own cognitive behavior and shared social behavior of swarm.
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Swarm Intelligence an Inspiration from Social Insect Behaviour in Various Decision Making Algorithms

Swarm Intelligence an Inspiration from Social Insect Behaviour in Various Decision Making Algorithms

The ants in search of food move out of their colonies in random directions and once they find food, they return to their colony leaving behind a trail of chemical substance called pheromone. The term pheromone comes from two Greek words, pherein which means “to transport” and hormone which means “to stimulate”. The Ants communicate with each other using this volatile chemical substance. Other ants will detect the pheromone and follow the same path. The number of times the path will be visited depends on two factors, first being the amount of pheromone on the path and the second one being the length of the path as pheromone naturally evaporates over time. Hence a more concise path will be preferred by the other ants as the ants already ensuing that path will keep on adding pheromone making the concentration rate larger than evaporation rate. This process leads to the emergence of the shortest path from food source to the colony. This path selection philosophy shows the self-organizing behavior of ants in which the probability of an ant choosing a route increases as the count of ants that already passed by that route increases. The Self-organization in ants leans on several factors. The first factor is Positive feedback or auto- catalytic process which means the avocation of other insects to forage a food source. The second factor is Negative feedback, means circumspection on behavior of the members caused by events such as the depletion of a food source or presence of dangerous predators. The third factor is Amplification of fluctuations which means the cause of unaimed events, such as an ant getting disoriented but finding a new source of food. The last factor is Reliance on Multiple interactions. It can be either direct like visual, physical, chemical or indirect interaction like when an already rooted way to the food source is hindered by an obstacle then the ants try to delve into new routes. This type of indirect interaction through the environment where some ants change the environment and other ants nose out and respond to the changes is known as stigmergy. The process of stigmergy relates the local and global behaviors of the ants. The local behavior of an individual ant modifies the environment which in turn modifies the behavior of other individuals.
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A General Framework for Accelerating Swarm Intelligence Algorithms on FPGAs, GPUs and Multi-core CPUs

A General Framework for Accelerating Swarm Intelligence Algorithms on FPGAs, GPUs and Multi-core CPUs

DALIN LI received the bachelor’s and master’s degrees in mechatronic engineering from Liaoning Technical University, Fuxin, China, in 2005 and 2008, respectively. He is currently pursuing the Ph.D. degree in computer science with the College of Computer Science and Technology, Jilin Uni- versity, Changchun, China. His current research interests include high performance computing, swarm intelligence, and machine learning. LAN HUANG received the Ph.D. degree. He is currently a Professor and a supervisor for Ph.D. candidates. She is mainly engaged in business intelligence theory and application research. She was one of the outstanding youth project funding winners of Jilin Province in 2005, and the person in charge of Young and Middle-aged Leader and Innovation Team of Jilin Province in 2012. She was invited to Italy Trento University as a Senior Visitor in 2010. As PI and Co-PI, he has been undertaking or accomplished more than 10 teaching and scientific research projects, granted by the National 863 Hi-tech Research and Development Program, the National Science Foundation China, provincial/ministerial foundations, and other sources. The works that she participated as main investigator, were awarded the first prize for the National Commercial Science and Technology Award bestowed by the China General Chamber of Commerce (the first prizewinner in 2010), the second prize for the Jilin Province Scientific and Technological Progress Award (first prizewinner in 2011), the second prize for the National Commercial Science and Technol- ogy Award (forth prizewinner in 2007, sixth prizewinner in 2004), the second prize for the Jilin Province Scientific and Technological Progress Award (forth prizewinner in 2004), and the second prize of Jilin Province Teaching Achievements (second prizewinner in 2005). In recent years, her research interests focus on business intelligence application and social network min- ing algorithm. She has published 64 academic papers, and obtained seven software copyrights. The software results researched and developed by her team have brought good economic benefit for the cooperative enterprises and application enterprises.
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An Analysis of Foraging and Echolocation Behavior of Swarm Intelligence Algorithms in Optimization: ACO, BCO and BA

An Analysis of Foraging and Echolocation Behavior of Swarm Intelligence Algorithms in Optimization: ACO, BCO and BA

DOI: 10.4236/ijis.2018.81001 2 International Journal of Intelligence Science as of late risen as a bunch of nature driven, population based algorithm that are equipped for delivering minimal effort, quick, and strong answers for some complex issues. Swarm Intelligence (SI) can be defined as the joined mentality of decentralized or self-sorted out frameworks in regular or simulated. The motiva- tion originates from generally organic framework. Swarm knowledge is a cha- racteristic calculation since it is made by following the development and work- ing conduct of common creatures and creepy crawlies. A flock of birds is an example of a swarm of birds. Bees’ swarming around their hive is another exam- ple of a swarm whose individual agents are bees. On the off chance that we nearly watch a solitary ant or honey bee, we will comprehend that they are not so keen but rather their settlements are. Swarm knowledge can help people to solve complex frameworks, from truck steering to military robots. A colony can solve any issue, for example, Ant Colony Optimization algorithm is used for finding the shortest path in the network routing problem, and Particle Swarm Intelligence is used in optical network optimization. As an individual, the swarm may be little fakers, yet as colonies they react rapidly and adequately to their en- vironment. There are two types of social interactions among swarm individuals, namely direct interaction and indirect interaction. Direct interactions are the obvious interaction through visual or audio contact, for example, birds interact with each other with sound. Indirect interaction is called Stigmergy [1], where agents interact with the environment. A pheromone trail of ants is an example of indirect interaction.
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Double Layer Security in the Swarm
          Intelligence P2P Network

Double Layer Security in the Swarm Intelligence P2P Network

Swarm-based algorithms have been followed and admired by the behavior of some social living beings such as termites, fishes, birds and ants. Self-organization and decentralized control are most important and remarkable features of swarm-based systems and in nature it leads to an emergent behavior. Emergent behavior is implemented through the local interactions among such system components and it will not possible to be done by any of the components of the system which is acting alone swarm intelligence algorithms were devised for continuous optimization problems. Two important principles of swarm intelligence are self-organization which is based on activity amplification by the positive feedback and activity balancing by the negative feedback and amplification of random fluctuations multiple interactions and second one is stimulation by work which is based on work as a behavioral response to the environmental state where an environment may serve as a work state memory that does not depend on the specific agents.
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Comparison of social network structure for particle swarm optimization

Comparison of social network structure for particle swarm optimization

topological neighbors [32]. An individual was influenced by its best performance acquired in the past and the best experience observed in its neighborhood. The experimental results show that the topologies influenced in these two variants are important. [33] Modified the mechanism of PSO individual interacts with its neighbors. The performance of an individual depends on population topology as well as algorithm version. It appears that a fully informed particle swarm is more susceptible to alterations in the topology, but with a good topology, it can outperform the canonical version. In the next section, a brief comparison of GA and PSO is given to further discerning their differences.
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Introductory Chapter: Swarm Intelligence and Particle Swarm Optimization

Introductory Chapter: Swarm Intelligence and Particle Swarm Optimization

Particle swarm optimization (PSO) is accepted as the second population-based algorithm inspired from animals. Since James Kennedy (a social psychologist) and Russell C. Eberhart simulated the bird flocking and fish schooling foraging behaviors, they have used this simula- tion to the solution of an optimization problem and published their idea in a conference in 1995 [3] for the optimization of continuous nonlinear functions. There are two main concepts in the algorithm: velocity and coordinate for each particle. Each particle has a coordinate and an initial velocity in a solution space. As the algorithm progresses, the particles converge toward the best solution coordinates. Since PSO is quite simple to implement, it requires less memory and has no operator. Due to this simplicity, PSO is also a fast algorithm. Different versions of PSO have been developed, using some operators since the first version of PSO was published. In the first versions of PSO, the velocity was calculated with a basic formula using current veloc- ity, personal best and local best values in the formula, multiplying stochastic variables. The cur- rent particle updates its previous velocity, not only its previous best but also the global best. The total probability was distributed between local and global best using stochastic variables. In the next versions, in order to control the velocity, an inertia weight was introduced by Shi and Eberhart in 1998 [4]. Inertia weight balances the local and global search ability of algo- rithm. Inertia weight specifies the rate of contribution of previous velocity to its current velocity. Researchers made different contributions to the inertia weight concept. Linearly, exponential or randomly decreasing or adaptive inertia weight was introduced by different researchers [5]. In the next version of PSO, a new parameter called constriction factor was introduced by Clerc and Kenedy [6, 7]. Constriction factor (K) was introduced in the studies on stability and convergence of PSO. Clerc indicates that the use of a constriction factor insured convergence of the PSO. A comparison between inertia weight and constriction factor was published by Shi and Eberhart [8]. Nearly all engineering discipline and science problems have been solved with PSO. Some of the most studied problems solved with PSO are from Electrical Engineering, Computer Sciences, Industrial Engineering, Biomedical Engineering, Mechanical Engineering and Robotics. In Electrical Engineering, power distribution problem [9] is solved with PSO. Another most stud- ied problem in Electrical Engineering is economic dispatch problem [10, 11]. In Computer Sciences, face localization [12], edge detection [13], image segmentation [14], image denoising
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Swarm Intelligence for Logistics Controlling

Swarm Intelligence for Logistics Controlling

Swarm robotics is currently one of the most important application areas for swarm intelligence. Swarms provide the possibility of enhanced task performance, high reliability (fault tolerance), low unit complexity and decreased cost over traditional robotic systems. They can accomplish some tasks that would be impossible for a single robot to achieve. Swarm robots can be applied to many fields, such as flexible manufacturing systems, space crafts, inspection/maintenance, construction, agriculture and medicine work. Swarm robotics is the study of how large number of relatively simple physically embodied agents can be designed such that a desired collective behaviour emerges from the local interactions among agents and between the agents and the environment. It is a novel approach to the coordination of large numbers of robots. It is inspired from the observation of social insects’ ants, termites, wasps and bees which stand as fascinating examples of how a large number of simple individuals can interact to create collectively intelligent systems. Robustness is the ability to cope with the loss of individuals. In social animals,
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Comparison of social network structure for particle swarm optimization

Comparison of social network structure for particle swarm optimization

topological neighbors [32]. An individual was influenced by its best performance acquired in the past and the best experience observed in its neighborhood. The experimental results show that the topologies influenced in these two variants are important. [33] Modified the mechanism of PSO individual interacts with its neighbors. The performance of an individual depends on population topology as well as algorithm version. It appears that a fully informed particle swarm is more susceptible to alterations in the topology, but with a good topology, it can outperform the canonical version. In the next section, a brief comparison of GA and PSO is given to further discerning their differences.
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Swarm Intelligence for Traffic Routing

Swarm Intelligence for Traffic Routing

ABSTRACT: Swa rm Technology is basically a system which works on real time conditions and the me mbers in the group interact with each other in a decentralized manner to achieve a particular objective via self-organization. Natural e xa mples are ant colonies, schooling of fishes, etc. Swarm in telligence is a field of a rtific ia l intelligence. Artific ial Intelligence of machine or software is that which studies and develops intelligent machine and software to make day to day life of hu mans much easier. Swa rm behavior is a co llect ive behavior e xh ibited by simila r types of species which all together perform a particula r task.
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Intrusion Detection using Artificial Neural Network and Swarm Intelligence Algorithm

Intrusion Detection using Artificial Neural Network and Swarm Intelligence Algorithm

In this paper ID with different combination of neural network are used to achieve a good accuracy. We use five different set of data sets namely DEFCON, NSL- KDD, DARPA, ISCX-UNB and KDD 1999 Cup. The attacks in the data set flows in four categories DOS: denial of service, R2L: Remote to User, U2R: User to Root, Probing. To reduce the false positive rate and also increase the ability of detection the paper also suggested a new Swarm Intelligence(SI) approach to pre-process the data. Figure 1 shows the proposed work Architecture. It converts the non-numerical value into numerical value. It also used to clarify a complex optimization problem. After completing the pre-processing the data is trained by using five different types of neural network such as Feed Forward Neural Network (FFNN), Deep Neural Network (DNN), and Joint Evolution Neural Network (JENN), Radial Basic Function Neural Network (RBNN), Hybrid Neural Network (HNN).optimization is a technique used to giving a resources to the perfect possible effect. After implementing these network function an artificial bee colony (ABC)optimization method is applied to give a better accuracy rate and efficiency to improve the system which is Joint Evaluation Neural Network.
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A General Optimization Framework for Multi Document Summarization Using Genetic Algorithms and Swarm Intelligence

A General Optimization Framework for Multi Document Summarization Using Genetic Algorithms and Swarm Intelligence

• Scouting Bees An employed bee stays in place if the neighbor it evaluates is not better than its current location. After several iterations at the same place, it abandons it and becomes a scouting bee. To move to another place, the scouting bee selects a random valid summary. This is equivalent to generating an individual from the initial population in the Genetic Summarizer. The number of iterations before becoming a scouting bee is the second hyper-parameter of the Swarm Summarizer. Genetic vs. Swarm In the Genetic Summarizer, the reproduction produces significant changes in the summaries studied, because, on average, half of the genotype of the child is different from its parents. But at the same time, it stays in a reasonable distance range from its parents because it keeps half of the genotype from each parent (on average). In this sense, we say that the Genetic Summarizer has efficient mid-range search capabilities. The local search is much reduced because it is done via mutations happening randomly and rarely in the population. The long-range search is done via insertion of random individuals into the population whenever the population becomes too small. A new completely random individual is quite likely to have a low fitness score and to die in the next generation with few opportunities to reproduce or mutate.
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Swarm Intelligence as an Optimization Technique

Swarm Intelligence as an Optimization Technique

Ant colony optimization (ACO) was one of the first techniques for approximate optimization inspired by swarm intelligence. More specifically, ACO is inspired by the foraging behavior of ant colonies. The basic principle is based on finding the shortest path from food source to anthill by smelling pheromones (chemical substances they leave on the ground during walk).

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Swarm Intelligence in Portfolio Selection

Swarm Intelligence in Portfolio Selection

DelValle Y., G. K. Venayagamoorthy, S. Mohagheghi, J.-C. Hernandez & R.G. Harley ( 2007), Particle Swarm Optimization: Basic Concepts, Variants and Applications in Power Systems , Evolutionary Computation, IEEE Transactions, PP.,99., 1-1 Eberhart R. & Y. Shi (1998), Comparison Between Genetic Algorithms and Particle Swarm Optimization, In e. a. V. William Porto, editor, Springer, Lecture Notes in Computer Science, In Proceedings of the 7th International Conference on Evolutionary

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A TOOL FOR IDENTIFYING SWARM INTELLIGENCE

A TOOL FOR IDENTIFYING SWARM INTELLIGENCE

TOOL DESIGN AND PROTOTYPE DEVELOPMENT The design of the tool is guided by text mining and swarm intelligence. A prototype based upon the proposed design is under development as a proof-of-concept (Gregg et al., 2001). The prototype enables a user to input keywords. Visualizations are produced and indicate interconnected relationships of the community subgroup participating in the mailing list, as shown in Figure 1 (next page). The horizontal axis represents time and the vertical axis represents individual posters. The points in the visualization represent one email. The points are color-coded to indicate the presence of the keywords. Lines are used to indicate possible social influences found amongst the posts, and are drawn when recent posts include the same search term. A visualization of a particle swarm algorithm can also be produced, as shown in Figure 2 (next page). The goal of the proof-of- concept is to demonstrate how a user could compare the visualizations of mailing list data and particle swarm algorithm. For the user of the tool, the objective is exploration. The user explores by entering keywords and examining the resulting visualizations for interesting patterns that appear to match the baseline PSO patterns. The prototype should be tested to determine if the tool allows a user to detect whether or not email messages of subgroups of a FOSS development community exhibit swarm behavior.
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Swarm Intelligence and Network Administration: Applications in Ad Hoc Wireless Auto-Configuration

Swarm Intelligence and Network Administration: Applications in Ad Hoc Wireless Auto-Configuration

Figure 3. Performance during a partition merger Future Work: Many interesting aspects of the swarm based system are yet to be researched and studied. The applicability of swarm intelligence to network engineering is a fairly recent endeavor and needs high end research. Analysis need not be curtailed to simulations but also should be carried forward to be realized on practical wireless and sensor networks. There are much to be dealt with the simulation set up and analysis itself. To enrich the flow of feedback in the system, multiple originator ants can be incorporated for every node. This would be highly helpful in propagating information in large networks faster. But before we employ such techniques a complete analysis on its fallouts has to be done. Another issue comes up when time timer reaches threshold and has to start over again. To deal with such instances, a maximum hop count limit could be incorporated for keeping entries pointing to the nodes which facilitate dropping redundant entries. Such an observation is yet to observed on a simulative basis. Conclusion: Dynamic Host Configuration Protocols in their present state cannot be extended to ad hoc and sensor networks. The problem with their applicability to these networks is that the DHCP requires a centralized server for executing its functions. Dissatisfaction exists among network administrators in implementing dynamic configuration for most wireless and mobile set-ups. Even today administrators prefer to manually configure nodes in mobile networks rather than automate it. Though manual configuration is the best in a lot of circumstances, highly mobile and large nodes requires something less tedious. Techniques such as duplicate address detection attempts to solve the problem of auto configuration, yet their relevance to present day implementation is merely theoretical. Many problems exist when porting such solution on practical basis. The fact that the establishment of large scale wireless and sensor networks is just incited and has many levels to travel has made this an indispensable problem to
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Swarm intelligence for scheduling: a review

Swarm intelligence for scheduling: a review

Some of the first scheduling publications appear in the industrial engineering and operations research literature associated to Naval Research Logistics Quarterly in the early fifties and contained results by W.E. Smith, S.M. Johnson and J.R. Jackson [18]. The scheduling problem was introduced with some impact to the community of Artificial Intelligence in 1982 by Mark S. Fox, through the paper titled "Job-Shop Scheduling: An Investigation in Constraint- Directed Reasoning" [19].

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Performance analysis of swarm intelligence algorithms in removal of ECG
artefact from tainted EEG signal

Performance analysis of swarm intelligence algorithms in removal of ECG artefact from tainted EEG signal

In this section, a brief review of some important contributions from the existing literature is presented. Sijbers et al. [ 6 ] suggested a method to remove ECG artefact from EEG signals based on adaptive filtering. Before filtering, this method necessitates to detect ECG artefact and to estimate the template of ECG artefact. Joe-Air Jiang et al [ 3 ] proposed a method for detect- ing and removing ECG artefact from EEG signals based on wavelet transform. This method needs a selection of suitable wavelet basis and scales. Lanquart et al. [ 7 ] proposed a method to eliminate QRS peaks using a morphological filter. In this method, a standard fixed shape element needs to be defined according to the artefact. Moreover, this method is appropriate for elim- inating QRS peaks but not for T-wave of ECG artefact. Stephanie Devuyst et al. [ 8 ] applied modified Indepen- dent Component Analysis (ICA) approach for eliminat- ing ECG artefacts from EEG signals and attained the correction rate of 91%. On the other hand, the tech- nique was found to have high computational complex- ity. In [ 9 ] ANFIS tuned by particle Swarm Optimization (PSO) was applied to eliminate ECG artefact from EEG signal and compared the results with that of ANFIS. It was proved that, ANFIS tuned by PSO technique performed better than original ANFIS.
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A SURVEY ON SWARM INTELLIGENCE MODELS

A SURVEY ON SWARM INTELLIGENCE MODELS

Since the computational displaying of swarms was proposed, there has been an unfaltering increment in the quantity of examination papers reporting the effective utilization of Swarm Intelligence calculations in a few improvement undertakings and exploration issues. Swarm Intelligence standards have been effectively connected in an assortment of issue spaces including capacity streamlining issues, finding ideal courses, booking, auxiliary enhancement, and picture and information examination [13] [14]. Computational demonstrating of swarms has been further connected to an extensive variety of different spaces, including machine learning [15], bioinformatics and restorative informatics [16], dynamical frameworks and operations research [17]; they have been even connected in account and business [18].
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