This document discusses the bee algorithm, which is an optimization algorithm inspired by the decision-making process of honey bees. It begins with an introduction and outline, then describes how bees communicate through waggle dances to share information about food sources. The bee algorithm mimics this process by initializing solutions, evaluating fitness, selecting sites for neighborhood search, and recruiting bees to explore solutions. An example application to mathematical function optimization is provided to illustrate the algorithm. Potential applications discussed include training neural networks, load balancing in cloud computing, and more.
The document describes the Bees Algorithm, which is an optimization algorithm inspired by the foraging behavior of honey bees. It begins with randomly placed scout bees that evaluate potential solutions. The best sites are selected and more bees are recruited to explore near those locations. The fittest bees are kept and the process repeats until termination criteria is met. The algorithm aims to efficiently locate good solutions like honey bees locating food sources. It can be applied to problems with multiple optimal solutions.
The document summarizes the artificial bee colony (ABC) algorithm, which was introduced in 2005 and is inspired by the foraging behavior of honeybee swarms. The ABC algorithm simulates three groups of bees - employed bees, onlookers, and scouts - to solve optimization problems. It involves phases of employed bee search, onlooker bee choice, and scout bee recruitment to balance exploration and exploitation. The ABC algorithm has few parameters and fast convergence but is limited by its initial solutions. Variations include multi-objective ABC algorithms and parameter studies on swarm size, limit, and dimension.
This document discusses the bee algorithm, which is an optimization technique inspired by the foraging behavior of honey bees. It begins with an introduction and overview of concepts like nature of bees, hill climbing, swarm intelligence, and bee colony optimization. It then describes the key steps of the proposed bee algorithm, including initializing a population of solutions, evaluating their fitness, selecting sites for neighborhood search, recruiting bees to search those sites, and iterating until an optimal solution is found. An example application to a traveling salesperson problem is provided. The document concludes that bee algorithm can help provide an optimal solution for problems with many possible solutions, such as in artificial intelligence applications.
The document discusses the artificial bee colony (ABC) algorithm, which is an optimization algorithm inspired by the foraging behavior of honey bee swarms. It summarizes the ABC algorithm's main components: employed bees, onlooker bees, scout bees, and how they work together to iteratively improve potential solutions. An example application of the ABC algorithm to minimize a 2D function is provided to demonstrate how the algorithm progresses through cycles of the bee phases and updates potential solutions based on their "fitness".
The document describes the artificial bee colony (ABC) algorithm, which is inspired by the foraging behavior of honey bee swarms. It summarizes the behaviors of employed foragers, unemployed foragers, and information exchange through waggle dancing. The ABC algorithm simulates these behaviors to solve optimization problems. It represents solutions as food sources and uses employed bees, onlookers, and scouts to explore and exploit the search space through randomization and selection of high-quality solutions. The algorithm is demonstrated through simulations of the Rosenbrock test function.
This document provides an overview of the Artificial Bee Colony (ABC) algorithm. It describes how ABC was inspired by the foraging behavior of honey bees. The core components of the ABC algorithm are introduced, including the initialization phase, employed bee phase, onlooker bee phase, and scout bee phase. Pseudocode and a flowchart depicting the steps of the ABC algorithm are presented. Applications of ABC in areas such as optimization, bioinformatics, scheduling, clustering, and engineering are discussed. Finally, the advantages of ABC like simplicity and flexibility are contrasted with limitations such as high computational cost.
The document discusses Particle Swarm Optimization (PSO), which is an optimization technique inspired by swarm intelligence and the social behavior of bird flocking. PSO initializes a population of random solutions and searches for optima by updating generations of candidate solutions. Each candidate, or particle, updates its position based on its own experience and the experience of neighboring highly-ranked particles. The algorithm is simple to implement and converges quickly to produce approximate solutions to difficult optimization problems.
The document describes the Bees Algorithm, which is an optimization algorithm inspired by the foraging behavior of honey bees. It begins with randomly placed scout bees that evaluate potential solutions. The best sites are selected and more bees are recruited to explore near those locations. The fittest bees are kept and the process repeats until termination criteria is met. The algorithm aims to efficiently locate good solutions like honey bees locating food sources. It can be applied to problems with multiple optimal solutions.
The document summarizes the artificial bee colony (ABC) algorithm, which was introduced in 2005 and is inspired by the foraging behavior of honeybee swarms. The ABC algorithm simulates three groups of bees - employed bees, onlookers, and scouts - to solve optimization problems. It involves phases of employed bee search, onlooker bee choice, and scout bee recruitment to balance exploration and exploitation. The ABC algorithm has few parameters and fast convergence but is limited by its initial solutions. Variations include multi-objective ABC algorithms and parameter studies on swarm size, limit, and dimension.
This document discusses the bee algorithm, which is an optimization technique inspired by the foraging behavior of honey bees. It begins with an introduction and overview of concepts like nature of bees, hill climbing, swarm intelligence, and bee colony optimization. It then describes the key steps of the proposed bee algorithm, including initializing a population of solutions, evaluating their fitness, selecting sites for neighborhood search, recruiting bees to search those sites, and iterating until an optimal solution is found. An example application to a traveling salesperson problem is provided. The document concludes that bee algorithm can help provide an optimal solution for problems with many possible solutions, such as in artificial intelligence applications.
The document discusses the artificial bee colony (ABC) algorithm, which is an optimization algorithm inspired by the foraging behavior of honey bee swarms. It summarizes the ABC algorithm's main components: employed bees, onlooker bees, scout bees, and how they work together to iteratively improve potential solutions. An example application of the ABC algorithm to minimize a 2D function is provided to demonstrate how the algorithm progresses through cycles of the bee phases and updates potential solutions based on their "fitness".
The document describes the artificial bee colony (ABC) algorithm, which is inspired by the foraging behavior of honey bee swarms. It summarizes the behaviors of employed foragers, unemployed foragers, and information exchange through waggle dancing. The ABC algorithm simulates these behaviors to solve optimization problems. It represents solutions as food sources and uses employed bees, onlookers, and scouts to explore and exploit the search space through randomization and selection of high-quality solutions. The algorithm is demonstrated through simulations of the Rosenbrock test function.
This document provides an overview of the Artificial Bee Colony (ABC) algorithm. It describes how ABC was inspired by the foraging behavior of honey bees. The core components of the ABC algorithm are introduced, including the initialization phase, employed bee phase, onlooker bee phase, and scout bee phase. Pseudocode and a flowchart depicting the steps of the ABC algorithm are presented. Applications of ABC in areas such as optimization, bioinformatics, scheduling, clustering, and engineering are discussed. Finally, the advantages of ABC like simplicity and flexibility are contrasted with limitations such as high computational cost.
The document discusses Particle Swarm Optimization (PSO), which is an optimization technique inspired by swarm intelligence and the social behavior of bird flocking. PSO initializes a population of random solutions and searches for optima by updating generations of candidate solutions. Each candidate, or particle, updates its position based on its own experience and the experience of neighboring highly-ranked particles. The algorithm is simple to implement and converges quickly to produce approximate solutions to difficult optimization problems.
Tabu search is a metaheuristic technique that guides a local search procedure to explore the solution space beyond local optimality. It uses flexible memory-based processes to escape the trap of cycling. Particle swarm optimization is a swarm intelligence technique inspired by bird flocking where potential solutions fly through hyperspace to find optimal regions. Ant colony optimization is another swarm intelligence technique inspired by how ants find food, where artificial ants cooperate to find good solutions.
The document summarizes the bat algorithm, which is inspired by the echolocation of bats. It describes how bats use echolocation to detect prey and avoid obstacles. The bat algorithm models this behavior mathematically to solve optimization problems. Key aspects covered include the idealized rules of the bat algorithm, the mathematical equations governing how solutions are generated and updated, examples of its application in image segmentation and other domains, comparisons to other algorithms, and advantages such as automatic zooming and parameter control.
The document discusses the grey wolf optimizer (GWO) algorithm, which is a meta-heuristic algorithm inspired by grey wolves' hunting behavior. It describes the social hierarchy of grey wolves, including alpha, beta, delta, and omega ranks. The algorithm simulates grey wolves' hunting techniques like encircling prey, hunting guided by the alpha/beta/delta ranks, attacking prey through exploitation, and searching for prey through exploration. The GWO algorithm initializes parameters and a population, assigns the best three solutions, updates other solutions, and iterates until termination criteria are met to find the best solution.
This document provides an overview of Markov Decision Processes (MDPs) and related concepts in decision theory and reinforcement learning. It defines MDPs and their components, describes algorithms for solving MDPs like value iteration and policy iteration, and discusses extensions to partially observable MDPs. It also briefly mentions dynamic Bayesian networks, the dopaminergic system, and its role in reinforcement learning and decision making.
The document discusses swarm intelligence and the artificial bee colony (ABC) algorithm. ABC simulates the foraging behavior of honeybee colonies. It includes three groups of bees - employed bees that exploit food sources and share information, unemployed bees called onlookers that choose food sources, and scouts that search for new sources. The algorithm uses this behavior with positive and negative feedback to balance exploration and exploitation to solve optimization problems. It evaluates candidate solutions and replaces poor sources in an iterative process until requirements are met.
The document summarizes the Cuckoo Search algorithm, which is inspired by the brood parasitism behavior of some cuckoo species. It describes three key aspects of cuckoos' behavior that the algorithm is based on: 1) cuckoos lay their eggs in other birds' nests; 2) if the host bird discovers the foreign egg, it will throw it out or abandon the nest; 3) cuckoo eggs often hatch slightly earlier, allowing the cuckoo chick to evict the other eggs. The algorithm represents each solution as an "egg" in a nest - the aim is to use new solutions to replace inferior solutions. It operates according to three rules: each cuckoo lays one egg, the
The document discusses the harmony search algorithm, a population-based metaheuristic optimization method inspired by musical improvisation. It describes how the algorithm works, including initializing the harmony memory randomly, improvising new solutions using memory consideration and pitch adjustment rules, and updating the memory with better solutions. The harmony search algorithm is then applied to various engineering optimization problems.
The document discusses particle swarm optimization (PSO), a population-based stochastic optimization technique inspired by bird flocking and fish schooling behavior. PSO initializes a population of random particles in search space and updates their positions and velocities based on their own experience and neighboring particles' experience to move toward optimal solutions. Compared to genetic algorithms, PSO does not use genetic operators and particles have memory of their own best solution to guide the search. The document also provides an overview of ant colony optimization, another swarm intelligence technique modeled after ant colony behavior.
Simulated Annealing - A Optimisation TechniqueAUSTIN MOSES
Simulated annealing is a global optimization technique inspired by the physical process of annealing in solids. It can find the global minimum of a cost function by slowly cooling the system. At each temperature, the algorithm accepts random moves to neighboring solutions with a probability based on the change in cost and current temperature. This allows occasionally moving to higher-cost solutions and avoids getting stuck in local minima. While slower than local search methods, simulated annealing is more likely to find the global optimum solution over multiple iterations as the temperature decreases.
A presentation on PSO with videos and animations to illustrate the concept. The ppt throws light on the concept, the algo, the application and comparison of PSO with GA and DE.
Bat algorithm explained. slides ppt pptxMahdi Atawneh
[Important]
Some numbers in the example are not correct ( in iteration 3 and later), I used them to clarify the idea only.
For people who asked me about the random number that appears in the slide:
Overview:
As described in the paper and pseudo code.
We have two important variables ( ri,Ai) for each bat, these variables will be used to evaluate the bats( solutions).
When a bat becomes near the goal, “ri” value will be increased, and “Ai” will be decreased.
*** About the Random variable:
At each iteration,
- The algorithm will have the solutions population ( assume we have 10 bats ), these solutions(bats) values are near each other.
- To prevent the algorithm from falling at local minima, the algorithm at each iteration will generate a random solution (bat) to explore, this could in some cases jump to a new solution that is near the goal.
- So in the slides, the “rand” means the random solution. We will compare it to all other solutions. If the random solution “ri” value is the height we will put this bat in the best solutions array.
Bat algorithm
download the Powerpoint file pptx with animations
https://docs.google.com/presentation/d/0Bxij58M-C_RgY2gxOEFHSlZzWHM/edit?usp=sharing&ouid=117863559816378751483&resourcekey=0-94EJhpYOuJtlSGiJlRH3jQ&rtpof=true&sd=true
The original paper: https://www.researchgate.net/publication/45913690_A_New_Metaheuristic_Bat-Inspired_Algorithm
Nature-Inspired Optimization Algorithms Xin-She Yang
This document discusses nature-inspired optimization algorithms. It begins with an overview of the essence of optimization algorithms and their goal of moving to better solutions. It then discusses some issues with traditional algorithms and how nature-inspired algorithms aim to address these. Several nature-inspired algorithms are described in detail, including particle swarm optimization, firefly algorithm, cuckoo search, and bat algorithm. These are inspired by behaviors in swarms, fireflies, cuckoos, and bats respectively. Examples of applications to engineering design problems are also provided.
Ant colony optimization (ACO) is a heuristic optimization algorithm inspired by the foraging behavior of ants. It is used to find optimal paths in graph problems. The algorithm operates by simulating ants walking around the problem space, depositing and following pheromone trails. Over time, as ants discover short paths, the pheromone density increases on those paths, making them more desirable for future ants. This positive feedback eventually leads all ants to converge on the shortest path. ACO has been applied successfully to problems like the traveling salesman problem.
The bat algorithm is a nature-inspired metaheuristic algorithm proposed in 2010 based on echolocation behavior of bats. Bats use echolocation to detect prey and obstacles, emitting sound pulses and analyzing echo signals to determine distance. In the bat algorithm, bats fly randomly searching for prey, adjusting pulse emission rate and wavelength based on target proximity. New solutions are generated by moving virtual bats according to equations involving the best current solution. The algorithm has been applied to problems in engineering design, classification, and image processing.
The document discusses the cuckoo search algorithm, which is a metaheuristic algorithm for global optimization inspired by the breeding behavior of some cuckoo species. It describes how cuckoos lay their eggs in other birds' nests, sometimes ejecting the host birds' eggs. The algorithm uses three rules - cuckoos lay one egg at a time in randomly chosen nests, the best nests carry over to future generations, and hosts can discover alien eggs with some probability. It also discusses Levy flights for random walks and the steps of the cuckoo search algorithm which involves generating nests, replacing eggs based on fitness, and abandoning nests to avoid local optimization. Finally, it lists some applications of the c
The document summarizes the bat algorithm (BA), a swarm intelligence technique inspired by bat echolocation behavior. It describes how BA mimics how real bats use echolocation to locate prey, varying the loudness and rate of their ultrasonic calls. The basic steps of the BA are outlined, including initializing a population of solutions, adjusting their position and velocity based on the best solutions, and updating parameters like loudness and pulse rate between iterations. Applications of BA include optimization problems in areas like engineering, scheduling, data mining, and image processing.
This document provides an overview of multilayer perceptrons (MLPs) and the backpropagation algorithm. It defines MLPs as neural networks with multiple hidden layers that can solve nonlinear problems. The backpropagation algorithm is introduced as a method for training MLPs by propagating error signals backward from the output to inner layers. Key steps include calculating the error at each neuron, determining the gradient to update weights, and using this to minimize overall network error through iterative weight adjustment.
The document discusses the concepts of soft computing and artificial neural networks. It defines soft computing as an emerging approach to computing that parallels the human mind in dealing with uncertainty and imprecision. Soft computing consists of fuzzy logic, neural networks, and genetic algorithms. Neural networks are simplified models of biological neurons that can learn from examples to solve problems. They are composed of interconnected processing units, learn via training, and can perform tasks like pattern recognition. The document outlines the basic components and learning methods of artificial neural networks.
Ant colony optimization is a metaheuristic algorithm that is inspired by the behavior of real ant colonies. Real ants deposit pheromone on paths between their nest and food sources, and other ants are more likely to follow paths with higher pheromone densities, allowing the colony to find the shortest path over time without centralized control. The algorithm models this behavior to solve optimization problems, with artificial ants probabilistically building solutions and adjusting pheromone levels to bias toward better solutions. The presentation discusses how ant colony optimization works and its components, including probabilistic solution construction, pheromone updating, and evaporation. It then provides an example application of using ant colony optimization for adaptive routing in communication networks.
Taking AI to the Next Level in Manufacturing.pdfssuserfac0301
Read Taking AI to the Next Level in Manufacturing to gain insights on AI adoption in the manufacturing industry, such as:
1. How quickly AI is being implemented in manufacturing.
2. Which barriers stand in the way of AI adoption.
3. How data quality and governance form the backbone of AI.
4. Organizational processes and structures that may inhibit effective AI adoption.
6. Ideas and approaches to help build your organization's AI strategy.
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slackshyamraj55
Discover the seamless integration of RPA (Robotic Process Automation), COMPOSER, and APM with AWS IDP enhanced with Slack notifications. Explore how these technologies converge to streamline workflows, optimize performance, and ensure secure access, all while leveraging the power of AWS IDP and real-time communication via Slack notifications.
Tabu search is a metaheuristic technique that guides a local search procedure to explore the solution space beyond local optimality. It uses flexible memory-based processes to escape the trap of cycling. Particle swarm optimization is a swarm intelligence technique inspired by bird flocking where potential solutions fly through hyperspace to find optimal regions. Ant colony optimization is another swarm intelligence technique inspired by how ants find food, where artificial ants cooperate to find good solutions.
The document summarizes the bat algorithm, which is inspired by the echolocation of bats. It describes how bats use echolocation to detect prey and avoid obstacles. The bat algorithm models this behavior mathematically to solve optimization problems. Key aspects covered include the idealized rules of the bat algorithm, the mathematical equations governing how solutions are generated and updated, examples of its application in image segmentation and other domains, comparisons to other algorithms, and advantages such as automatic zooming and parameter control.
The document discusses the grey wolf optimizer (GWO) algorithm, which is a meta-heuristic algorithm inspired by grey wolves' hunting behavior. It describes the social hierarchy of grey wolves, including alpha, beta, delta, and omega ranks. The algorithm simulates grey wolves' hunting techniques like encircling prey, hunting guided by the alpha/beta/delta ranks, attacking prey through exploitation, and searching for prey through exploration. The GWO algorithm initializes parameters and a population, assigns the best three solutions, updates other solutions, and iterates until termination criteria are met to find the best solution.
This document provides an overview of Markov Decision Processes (MDPs) and related concepts in decision theory and reinforcement learning. It defines MDPs and their components, describes algorithms for solving MDPs like value iteration and policy iteration, and discusses extensions to partially observable MDPs. It also briefly mentions dynamic Bayesian networks, the dopaminergic system, and its role in reinforcement learning and decision making.
The document discusses swarm intelligence and the artificial bee colony (ABC) algorithm. ABC simulates the foraging behavior of honeybee colonies. It includes three groups of bees - employed bees that exploit food sources and share information, unemployed bees called onlookers that choose food sources, and scouts that search for new sources. The algorithm uses this behavior with positive and negative feedback to balance exploration and exploitation to solve optimization problems. It evaluates candidate solutions and replaces poor sources in an iterative process until requirements are met.
The document summarizes the Cuckoo Search algorithm, which is inspired by the brood parasitism behavior of some cuckoo species. It describes three key aspects of cuckoos' behavior that the algorithm is based on: 1) cuckoos lay their eggs in other birds' nests; 2) if the host bird discovers the foreign egg, it will throw it out or abandon the nest; 3) cuckoo eggs often hatch slightly earlier, allowing the cuckoo chick to evict the other eggs. The algorithm represents each solution as an "egg" in a nest - the aim is to use new solutions to replace inferior solutions. It operates according to three rules: each cuckoo lays one egg, the
The document discusses the harmony search algorithm, a population-based metaheuristic optimization method inspired by musical improvisation. It describes how the algorithm works, including initializing the harmony memory randomly, improvising new solutions using memory consideration and pitch adjustment rules, and updating the memory with better solutions. The harmony search algorithm is then applied to various engineering optimization problems.
The document discusses particle swarm optimization (PSO), a population-based stochastic optimization technique inspired by bird flocking and fish schooling behavior. PSO initializes a population of random particles in search space and updates their positions and velocities based on their own experience and neighboring particles' experience to move toward optimal solutions. Compared to genetic algorithms, PSO does not use genetic operators and particles have memory of their own best solution to guide the search. The document also provides an overview of ant colony optimization, another swarm intelligence technique modeled after ant colony behavior.
Simulated Annealing - A Optimisation TechniqueAUSTIN MOSES
Simulated annealing is a global optimization technique inspired by the physical process of annealing in solids. It can find the global minimum of a cost function by slowly cooling the system. At each temperature, the algorithm accepts random moves to neighboring solutions with a probability based on the change in cost and current temperature. This allows occasionally moving to higher-cost solutions and avoids getting stuck in local minima. While slower than local search methods, simulated annealing is more likely to find the global optimum solution over multiple iterations as the temperature decreases.
A presentation on PSO with videos and animations to illustrate the concept. The ppt throws light on the concept, the algo, the application and comparison of PSO with GA and DE.
Bat algorithm explained. slides ppt pptxMahdi Atawneh
[Important]
Some numbers in the example are not correct ( in iteration 3 and later), I used them to clarify the idea only.
For people who asked me about the random number that appears in the slide:
Overview:
As described in the paper and pseudo code.
We have two important variables ( ri,Ai) for each bat, these variables will be used to evaluate the bats( solutions).
When a bat becomes near the goal, “ri” value will be increased, and “Ai” will be decreased.
*** About the Random variable:
At each iteration,
- The algorithm will have the solutions population ( assume we have 10 bats ), these solutions(bats) values are near each other.
- To prevent the algorithm from falling at local minima, the algorithm at each iteration will generate a random solution (bat) to explore, this could in some cases jump to a new solution that is near the goal.
- So in the slides, the “rand” means the random solution. We will compare it to all other solutions. If the random solution “ri” value is the height we will put this bat in the best solutions array.
Bat algorithm
download the Powerpoint file pptx with animations
https://docs.google.com/presentation/d/0Bxij58M-C_RgY2gxOEFHSlZzWHM/edit?usp=sharing&ouid=117863559816378751483&resourcekey=0-94EJhpYOuJtlSGiJlRH3jQ&rtpof=true&sd=true
The original paper: https://www.researchgate.net/publication/45913690_A_New_Metaheuristic_Bat-Inspired_Algorithm
Nature-Inspired Optimization Algorithms Xin-She Yang
This document discusses nature-inspired optimization algorithms. It begins with an overview of the essence of optimization algorithms and their goal of moving to better solutions. It then discusses some issues with traditional algorithms and how nature-inspired algorithms aim to address these. Several nature-inspired algorithms are described in detail, including particle swarm optimization, firefly algorithm, cuckoo search, and bat algorithm. These are inspired by behaviors in swarms, fireflies, cuckoos, and bats respectively. Examples of applications to engineering design problems are also provided.
Ant colony optimization (ACO) is a heuristic optimization algorithm inspired by the foraging behavior of ants. It is used to find optimal paths in graph problems. The algorithm operates by simulating ants walking around the problem space, depositing and following pheromone trails. Over time, as ants discover short paths, the pheromone density increases on those paths, making them more desirable for future ants. This positive feedback eventually leads all ants to converge on the shortest path. ACO has been applied successfully to problems like the traveling salesman problem.
The bat algorithm is a nature-inspired metaheuristic algorithm proposed in 2010 based on echolocation behavior of bats. Bats use echolocation to detect prey and obstacles, emitting sound pulses and analyzing echo signals to determine distance. In the bat algorithm, bats fly randomly searching for prey, adjusting pulse emission rate and wavelength based on target proximity. New solutions are generated by moving virtual bats according to equations involving the best current solution. The algorithm has been applied to problems in engineering design, classification, and image processing.
The document discusses the cuckoo search algorithm, which is a metaheuristic algorithm for global optimization inspired by the breeding behavior of some cuckoo species. It describes how cuckoos lay their eggs in other birds' nests, sometimes ejecting the host birds' eggs. The algorithm uses three rules - cuckoos lay one egg at a time in randomly chosen nests, the best nests carry over to future generations, and hosts can discover alien eggs with some probability. It also discusses Levy flights for random walks and the steps of the cuckoo search algorithm which involves generating nests, replacing eggs based on fitness, and abandoning nests to avoid local optimization. Finally, it lists some applications of the c
The document summarizes the bat algorithm (BA), a swarm intelligence technique inspired by bat echolocation behavior. It describes how BA mimics how real bats use echolocation to locate prey, varying the loudness and rate of their ultrasonic calls. The basic steps of the BA are outlined, including initializing a population of solutions, adjusting their position and velocity based on the best solutions, and updating parameters like loudness and pulse rate between iterations. Applications of BA include optimization problems in areas like engineering, scheduling, data mining, and image processing.
This document provides an overview of multilayer perceptrons (MLPs) and the backpropagation algorithm. It defines MLPs as neural networks with multiple hidden layers that can solve nonlinear problems. The backpropagation algorithm is introduced as a method for training MLPs by propagating error signals backward from the output to inner layers. Key steps include calculating the error at each neuron, determining the gradient to update weights, and using this to minimize overall network error through iterative weight adjustment.
The document discusses the concepts of soft computing and artificial neural networks. It defines soft computing as an emerging approach to computing that parallels the human mind in dealing with uncertainty and imprecision. Soft computing consists of fuzzy logic, neural networks, and genetic algorithms. Neural networks are simplified models of biological neurons that can learn from examples to solve problems. They are composed of interconnected processing units, learn via training, and can perform tasks like pattern recognition. The document outlines the basic components and learning methods of artificial neural networks.
Ant colony optimization is a metaheuristic algorithm that is inspired by the behavior of real ant colonies. Real ants deposit pheromone on paths between their nest and food sources, and other ants are more likely to follow paths with higher pheromone densities, allowing the colony to find the shortest path over time without centralized control. The algorithm models this behavior to solve optimization problems, with artificial ants probabilistically building solutions and adjusting pheromone levels to bias toward better solutions. The presentation discusses how ant colony optimization works and its components, including probabilistic solution construction, pheromone updating, and evaporation. It then provides an example application of using ant colony optimization for adaptive routing in communication networks.
Taking AI to the Next Level in Manufacturing.pdfssuserfac0301
Read Taking AI to the Next Level in Manufacturing to gain insights on AI adoption in the manufacturing industry, such as:
1. How quickly AI is being implemented in manufacturing.
2. Which barriers stand in the way of AI adoption.
3. How data quality and governance form the backbone of AI.
4. Organizational processes and structures that may inhibit effective AI adoption.
6. Ideas and approaches to help build your organization's AI strategy.
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slackshyamraj55
Discover the seamless integration of RPA (Robotic Process Automation), COMPOSER, and APM with AWS IDP enhanced with Slack notifications. Explore how these technologies converge to streamline workflows, optimize performance, and ensure secure access, all while leveraging the power of AWS IDP and real-time communication via Slack notifications.
Main news related to the CCS TSI 2023 (2023/1695)Jakub Marek
An English 🇬🇧 translation of a presentation to the speech I gave about the main changes brought by CCS TSI 2023 at the biggest Czech conference on Communications and signalling systems on Railways, which was held in Clarion Hotel Olomouc from 7th to 9th November 2023 (konferenceszt.cz). Attended by around 500 participants and 200 on-line followers.
The original Czech 🇨🇿 version of the presentation can be found here: https://www.slideshare.net/slideshow/hlavni-novinky-souvisejici-s-ccs-tsi-2023-2023-1695/269688092 .
The videorecording (in Czech) from the presentation is available here: https://youtu.be/WzjJWm4IyPk?si=SImb06tuXGb30BEH .
Letter and Document Automation for Bonterra Impact Management (fka Social Sol...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on automated letter generation for Bonterra Impact Management using Google Workspace or Microsoft 365.
Interested in deploying letter generation automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
GraphRAG for Life Science to increase LLM accuracyTomaz Bratanic
GraphRAG for life science domain, where you retriever information from biomedical knowledge graphs using LLMs to increase the accuracy and performance of generated answers
Skybuffer AI: Advanced Conversational and Generative AI Solution on SAP Busin...Tatiana Kojar
Skybuffer AI, built on the robust SAP Business Technology Platform (SAP BTP), is the latest and most advanced version of our AI development, reaffirming our commitment to delivering top-tier AI solutions. Skybuffer AI harnesses all the innovative capabilities of the SAP BTP in the AI domain, from Conversational AI to cutting-edge Generative AI and Retrieval-Augmented Generation (RAG). It also helps SAP customers safeguard their investments into SAP Conversational AI and ensure a seamless, one-click transition to SAP Business AI.
With Skybuffer AI, various AI models can be integrated into a single communication channel such as Microsoft Teams. This integration empowers business users with insights drawn from SAP backend systems, enterprise documents, and the expansive knowledge of Generative AI. And the best part of it is that it is all managed through our intuitive no-code Action Server interface, requiring no extensive coding knowledge and making the advanced AI accessible to more users.
Building Production Ready Search Pipelines with Spark and MilvusZilliz
Spark is the widely used ETL tool for processing, indexing and ingesting data to serving stack for search. Milvus is the production-ready open-source vector database. In this talk we will show how to use Spark to process unstructured data to extract vector representations, and push the vectors to Milvus vector database for search serving.
Fueling AI with Great Data with Airbyte WebinarZilliz
This talk will focus on how to collect data from a variety of sources, leveraging this data for RAG and other GenAI use cases, and finally charting your course to productionalization.
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-und-domino-lizenzkostenreduzierung-in-der-welt-von-dlau/
DLAU und die Lizenzen nach dem CCB- und CCX-Modell sind für viele in der HCL-Community seit letztem Jahr ein heißes Thema. Als Notes- oder Domino-Kunde haben Sie vielleicht mit unerwartet hohen Benutzerzahlen und Lizenzgebühren zu kämpfen. Sie fragen sich vielleicht, wie diese neue Art der Lizenzierung funktioniert und welchen Nutzen sie Ihnen bringt. Vor allem wollen Sie sicherlich Ihr Budget einhalten und Kosten sparen, wo immer möglich. Das verstehen wir und wir möchten Ihnen dabei helfen!
Wir erklären Ihnen, wie Sie häufige Konfigurationsprobleme lösen können, die dazu führen können, dass mehr Benutzer gezählt werden als nötig, und wie Sie überflüssige oder ungenutzte Konten identifizieren und entfernen können, um Geld zu sparen. Es gibt auch einige Ansätze, die zu unnötigen Ausgaben führen können, z. B. wenn ein Personendokument anstelle eines Mail-Ins für geteilte Mailboxen verwendet wird. Wir zeigen Ihnen solche Fälle und deren Lösungen. Und natürlich erklären wir Ihnen das neue Lizenzmodell.
Nehmen Sie an diesem Webinar teil, bei dem HCL-Ambassador Marc Thomas und Gastredner Franz Walder Ihnen diese neue Welt näherbringen. Es vermittelt Ihnen die Tools und das Know-how, um den Überblick zu bewahren. Sie werden in der Lage sein, Ihre Kosten durch eine optimierte Domino-Konfiguration zu reduzieren und auch in Zukunft gering zu halten.
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Salesforce Integration for Bonterra Impact Management (fka Social Solutions A...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on integration of Salesforce with Bonterra Impact Management.
Interested in deploying an integration with Salesforce for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
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Introduction
• Honeybee search for the best nest site
between many sites with taking care of both
speed and accuracy .
• This analogues to finding the optimal solution
(optimality) in an optimization process.
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Bee in nature
• The group decision making process used by
bees for searching out the best food resources
among various solutions is a robust example
of swarm-based decision method.
• This group decision-making process can be
mimicked for finding out solutions of
optimization problems.
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Bee in nature cont..
• Bee use a waggle dance to communicate
• What is the waggle dance ?!
It is a dance that performed by scout bees to
inform other foraging bees about nectar site.
• What are the scout and foraging ?!
Scout bee : the navigator
Forging bee : the collector of food from
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Bee in nature cont..
• The waggle dance is showed in the following video .
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Bee in nature >>
• Waggle dance is a communication method used
by bees to inform other bees about food
resources and location of nest site .
• Figure-eight running 8 .
• Number of runs represents the distance .
• The angle of run indicates the direction.
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Bee in nature >>
• Waggle dance in decision-making
• Waggle dance gives precise information about
quality ,distance and direction of flower patch.
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Bee in nature >>
• Decision 1 : Quiescent bees evaluate the patch
and decide to recruit or explore for other
patches. “decision”
If the patch still good ,increase the number of
foraging bees.
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Bee in nature >>
• Decision 2 : decide the number of bees
recruited to the patch based on the quality.
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Bee in nature >>
• Decision 3 : Nest-site selection.
Two activity to reach to the decision :
• Consensus : agreement among the group of
quiescent.
• Quorum : threshold value.
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Bee Algorithm (BA)
• The Bees Algorithm is an optimisation
algorithm inspired by the natural foraging
behaviour of honey bees to find the
optimal solution.
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Bee Algorithm (BA)
1. Initialise population with random solutions.
2. Evaluate fitness of the population.
3. While (stopping criterion not met)
//Forming new population.
4. Select sites for neighbourhood search.
5. Recruit bees for selected sites (more bees for
best e sites) and evaluate fitnesses.
6. Select the fittest bee from each patch.
7. Assign remaining bees to search randomly
and evaluate their fitnesses.
8. End While.
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Initialise a Population of n Scout Bees
Evaluate the Fitness of the Population
Select m Sites for Neighbourhood Search
Neighbourhood Search
Determine the Size of Neighbourhood
(Patch Size ngh)
Recruit Bees for Selected Sites
(more Bees for the Best e Sites)
Select the Fittest Bee from Each Site
Assign the (n–m) Remaining Bees to Random Search
New Population of Scout Bees
Flowchart of the Basic BA
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Simple Example: Function
Optimisation
• Here are a simple example about how Bee
algorithm works
• The example explains the use of bee
algorithm to get the best value representing
a mathematical function (functional optimal)
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Simple Example
• The following figure shows the mathematical
function
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Simple Example
• 1- The first step is to initiate the population
with any 10 scout bees with random search
and evaluate the fitness. (n=10)
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Simple Example
y
*
* *
* *
*
* * * *
x
Graph 1. Initialise a Population of (n=10) Scout Bees
with random Search and evaluate the fitness.
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2- Population evaluation fitness:
• An array of 10 values is constructed and
ordered in ascending way from the highest
value of y to the lowest value of y depending
on the previous mathematical function
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3- The best m site is chosen ( the best evaluation to
m scout bee) from n
m=5, e=2, m-e=3
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y
e
▪
▪ ▫
m
▫ ▫
*
* * * *
x
Graph 2. Select best (m=5) Sites for Neighbourhood Search:
(e=2) elite bees “▪” and (m-e=3) other selected bees“▫”
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4- Select a neighborhood search site upon ngh size:
y
▪
▪ ▫
▫ ▫
x
Graph 3. Determine the Size of Neighbourhood (Patch Size ngh)
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• 5- recruits bees to the selected sites and
evaluate the fitness to the sites:
– Sending bees to e sites (rich sites) and m-e sites
(poor sites).
– More bees will be sent to the e site.
• n2 = 4 (rich)
• n1 = 2 (poor)
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**
**
y **
*
*
▪**
▪
*
*
* *
▫*
*
▫
*
▫
*
* *
x
Graph 4. Recruit Bees for Selected Sites
(more Bees for the e=2 Elite Sites)
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6- Select the best bee from each location (higher
fitness) to form the new bees population.
Choosing the best bee from every m site as follow:
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Simple Example
y
*
* ▪*
*
▪
*
*
* *
▫
*
*
*
▫ ▫
*
* *
x
Graph 5. Select the Fittest Bee * from Each Site
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Simple Example
7- initializes a new population:
Taking the old values (5) and assigning random values
(5) to the remaining values n-m
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Simple Example
y
e
* o
* *
m
o * * o
o
o
x
Graph 6. Assign the (n–m) Remaining Bees to Random Search
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Simple Example
8- the loop counter will be reduced and the steps
from two to seven will be repeated until reaching
the stopping condition (ending the number of
repetitions imax)
• At the end we reach the best solution as shown in
the following figure
• This best value (best bees from m) will represent
the optimum answer to the mathematical function
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Simple Example
y
*
*
*
* *
x
Graph 7. Find The Global Best point
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BA- Applications
Function Optimisation
BA for TSP
Training NN classifiers like MLP, LVQ, RBF and
SNNs
Control Chart Pattern Recognitions
Wood Defect Classification
ECG Classification
Electronic Design
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Honeybee foraging algorithm for load
balancing in cloud computing
• Servers are bees
• Web applications are flower patches
• And an advert board is used to simulate a waggle
dance.
• Each server is either a forager or a scout
• The advert board is where servers, successfully
fulfilling a request or may place adverts
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Flow chart of Honeybee Foraging Algorithm in load
balancing for cloud computing
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