1462 Words6 Pages

Artificial Bee Colony (ABC) optimization was invented by Karaboga. ABC is a Swarm Intelligence approach which is being inspired from the foraging behaviour of real time honey bees. Bee Colony Optimization consists of three types of bees namely employed bees, onlooker bees and scout bees. ABC algorithm randomly generates initial population of bees P (I = 0) with their m food source locations throughout the iterations (I =1, 2…Imax) of searching processes of the three types of bees, where m denotes no of onlooker or employed bees which is equal to the food source locations. These food source positions are also called solutions that are having different amount of nectar which is known as the fitness function. Each solution i.e si (i=1, 2, 3,…m) is a d-dimensional vector, Where d is no of parameters of optimization.

Depending upon the visual information of the food source the employed bees updates the solutions in its memory and evaluates the nectar amount of the newly*…show more content…*

The rate of recruitment measures the speed at which the artificial bees explore and exploits newly found feasible food locations. The overall performance of the ABC technique is dependent upon the prompt discovery and efficient utilization of the optimal food sources. In the same way; the effective solution of complex problems is also dependant upon the quick discovery of better solutions specially for the real time optimization problems in which both exploration and exploitation processes took place simultaneously. In Artificial Bee Colony, where exploitation process is performed by onlooker and employed bees in the search space, the scout bees manages the exploration of food resources. Detailed pseudo-code of the Artificial Bee Colony Optimization is given

Depending upon the visual information of the food source the employed bees updates the solutions in its memory and evaluates the nectar amount of the newly

The rate of recruitment measures the speed at which the artificial bees explore and exploits newly found feasible food locations. The overall performance of the ABC technique is dependent upon the prompt discovery and efficient utilization of the optimal food sources. In the same way; the effective solution of complex problems is also dependant upon the quick discovery of better solutions specially for the real time optimization problems in which both exploration and exploitation processes took place simultaneously. In Artificial Bee Colony, where exploitation process is performed by onlooker and employed bees in the search space, the scout bees manages the exploration of food resources. Detailed pseudo-code of the Artificial Bee Colony Optimization is given

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## Bee Colony Algorithm Report

1387 Words | 6 Pages2.5.2.5. Artificial Bee Colony Optimization It is a meta-heuristic algorithm developed by D. Karaboga in 2005. This algorithm simulates the foraging behavior of honey bees. The ABC algorithm has three phases: employed bee phase, onlooker bee phase and scout bee phase. In the first two phases, bees exploit the sources by making local search in the neighborhood of the solutions which are generated randomly.

## Artificial Bee Colony Model: A Case Study

1868 Words | 8 Pages4.2.1.2.5 Artificial Bee Colony algorithm (ABC). Artificial Bee Colony (ABC) model is proposed by Karaboga [263]. In this collective intelligence search model, the honey bees are categorized as employed, onlooker and scout. The employed and unemployed bees search for the rich food sources, which are close to the bee's hive. The employed bees store the food source information and share the information with onlooker bees.

## Ant Colony Optimization Lab Report

1839 Words | 8 PagesANT COLONY OPTIMIZATION Introduction Ant Colony Optimization (ACO) is a metaheuristic approach which uses the technique to find the shortest path for a given problem. Dorigo, Maniezzo and Colorni were the first ones who proposed ACO algorithm called Ant System. Ant Colony Optimization algorithms are used to solve large number of hard combinatorial optimization problems such as traveling salesman problem, routing, quadratic assignment problem and in telecommunication networks. Whenever we are solving optimization problems, often the approach is to find all the possible solution and choose a best solution out of it. But some problems have such a large solution that it is impossible to arrive at an accurate solution in a reasonable time.

## Computational Complexity Theory

1172 Words | 5 PagesThere exists a one-to-one relationship between the pickup and delivery vertices. 1.3 RESEARCH OBJECTIVES OBJECTIVE 1: To study the Artificial Bee Colony Algorithm This objective is carried out to understand the concepts, the advantages, disadvantages and applications of ABC algorithm. To understand what kind of problems can be implemented through this algorithm and how they can be implemented so that the new problem can be identified for the thesis. OBJECTIVE 2: To implement Intractable Problem using Artificial Bee Colony Algorithm This objective is carried out to implement the TSPPD, which is an intractable problem, using the ABC algorithm as ABC. The intractable problems can’t be solved by the classical algorithms hence TSPPD has been solved by the ABC algorithm.

## Ant Based Control Case Study

2852 Words | 12 Pages3.1 Ant Based Control (ABC) Ant Based Control model [29] is one of the ant Colony Optimization (ACO) based algorithm used for the telephone network. ABC uses fixed shortest-path routes, and also uses an alternative algorithmically-based type of mobile agent for use with network management. In ABC algorithm a number of agents called ants are continuously exploring the network from the sources to random destinations. Arriving at a node, they update the pheromones to their source node for its entire neighbor node, which corresponds to the probabilities for ants to select each neighbor node. ABC uses two ants 1) Exploratory ant 2) Actual ant.

## Bee Colony Theory

831 Words | 4 PagesThe method comprises the steps that firstly, the artificial bee colony algorithm is utilized for conducting weight value optimization on the neural network; secondly, the optimized neural network is utilized for predicting building energy consumption. The artificial bee colony algorithm is an optimizing algorithm simulating a bee colony and has the advantages that control parameters are fewer, implementation is easy, and calculation is convenient; compared with a particle swarm algorithm, a genetic algorithm and other intelligent computing methods, the artificial bee colony algorithm has the prominent advantages that in each iterative process, global search and local search are both performed, the probability of finding an optimal solution is greatly increased, local optimum is avoided to a great extent, and global convergence is enhanced. Thus, when the artificial bee colony algorithm is adopted to optimize the initial weight value of the neutral network, the accuracy of the neutral network predicting the building energy consumption is improved, and meanwhile the defects existing in weight value optimization of the neutral network at present can be overcome

## Particle Swarm Optimification Case Study

767 Words | 4 PagesBut they know how far the food is in each iteration performed. So the best strategy to find the food is to keep an eye on the bird which is nearest to the food. PSO acquired the knowledge from the scenario and used it in finding the solutions for optimization problems. In Particle Swarm Optimization each single solution is a "bird" in the search space called "particle". Each particle has a fitness value associated with it which is evaluated by the fitness function to be optimized which direct the flying of the particles and have velocities.

## Optimization Algorithm Essay

1034 Words | 5 PagesO.) • Genetic Algorithm Genetic Algorithm is give by Charles Darwin and based on the concept of Natural selection and evolution. This Algorithm is started with the set of solutions also known as chromosomes or population. The solution from the one population is taken and used to make a new population from the previous solution, the purpose of doing this is that we hope that the new generated population is better than the older one. The solutions which are selected from the first population is

## Examples Of Swarm Intelligence

1179 Words | 5 PagesThe common examples of swarm intelligence are Ant colony, Bird flocks, Bees colony etc. This type of group work can be incorporated to build any artificial intelligent system which will be flexible and robust with direct or indirect interactions. In this paper we are discussing about some of the Swarm Intelligence models such as, Ant colony Optimization

## Essay On Artificial Selection

787 Words | 4 PagesArtificial selection is also commonly referred as selective breading. It is when you take 2 living orgasms and make them breed to get the specific outcome like a cow that can fly and a cow that can swim and he breed them together to get a flying swimming cow and he did that on propose. That is Artificial selection. and usaly the strogest get to repudose and survive. These are the steps in Artificial selection : 1 Decide which characteristics are important 2 Choose parents that show these characteristics 3 Select the best offspring from parents to breed the next generation 4 Repeat the process continuously Artificial selection happens in farms for like cows and Wheat Wheat Producing disease-resistant wheat by crossbreeding wheat plants

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