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[PDF] Top 20 Cognitive Ant colony optimization: A new framework in swarm intelligence

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Cognitive Ant colony optimization: A new framework in swarm intelligence

Cognitive Ant colony optimization: A new framework in swarm intelligence

... The pheromone is always in the gain region, so the reference point is zero. With this frame setting, the ants are always in the gains domain and the behaviour towards the risks is only influenced by the probability ... See full document

157

Machine Vision Optimization using Nature-Inspired Algorithms to Model Sunagoke Moss Water Status

Machine Vision Optimization using Nature-Inspired Algorithms to Model Sunagoke Moss Water Status

... developing new intelligent systems. In this study, artificial intelligence approaches using nature-inspired algorithms for optimizing image feature-subset to predict water status of Sunagoke moss were ... See full document

13

Swarm Intelligence based efficient routing algorithm for platooning in VANET through Ant Colony Optimization

Swarm Intelligence based efficient routing algorithm for platooning in VANET through Ant Colony Optimization

... devised swarm based efficient routing platooning algorithm using ACO techniques is able to reduce the travel time in VANET through two factors: first is by providing green time at traffic signals and hence, ... See full document

7

Validation of Hybridized Particle Swarm Optimization (PSO) Algorithm with the Pheromone Mechanism of Ant Colony Optimization (ACO) using Standard Benchmark Function

Validation of Hybridized Particle Swarm Optimization (PSO) Algorithm with the Pheromone Mechanism of Ant Colony Optimization (ACO) using Standard Benchmark Function

... of new artificial intelligence science known as Swarm Intelligence (SI) ...A swarm is a large number of homogenous, simple agents interacting locally among themselves, and their ... See full document

8

Survey on Novel Optimization Algorithm Using ACOSIO

Survey on Novel Optimization Algorithm Using ACOSIO

... The ant-colony algorithm is a probabilistic technique for solving large data sets problems which can be reduced time to find particular data ...paper new algorithm i.e Ant Colony ... See full document

5

Optimization of Fairhurst Cook Model for 2 D Wing Cracks Using Ant Colony Optimization (ACO), Particle Swarm Intelligence (PSO), and Genetic Algorithm (GA)

Optimization of Fairhurst Cook Model for 2 D Wing Cracks Using Ant Colony Optimization (ACO), Particle Swarm Intelligence (PSO), and Genetic Algorithm (GA)

... DOI: 10.4236/jamp.2018.68134 1590 Journal of Applied Mathematics and Physics generation change. Some of the individuals in the population are replaced by new ones, since the size of the population has to remain ... See full document

15

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

... Ant Colony Optimization (ACO) was first introduced as a powerful search and a heuristic approach for the solution of combinatorial optimization prob- lems by ...applied Ant System to ... See full document

27

Model Analysis on Job Shop Scheduling in Automobile Industry using Ant Colony Optimization and Particle Swarm Optimization

Model Analysis on Job Shop Scheduling in Automobile Industry using Ant Colony Optimization and Particle Swarm Optimization

... discovering new and different methods to solve ...used Ant Colony Optimization and Particle Swarm Optimization, techniques which are probabilistic and iterative respectively to ... See full document

5

Comparative Analysis of Meta-Heuristic Algorithms based on their Application Areas in SRGM

Comparative Analysis of Meta-Heuristic Algorithms based on their Application Areas in SRGM

... SRGMs are the statistical models which can be used to make predictions about a Software System's Failure Rate, given the failure history of the system. As the failure intensity decreases, the reliability increases. ... See full document

7

Comparative Analysis of ACO and PSO over Task Scheduling

Comparative Analysis of ACO and PSO over Task Scheduling

... Particle swarm optimization deals in two major ...Particle swarm optimization [11] is based on the observations of swarms of particles (bees, insects ...Particle swarm ... See full document

6

Population based meta-heuristic techniques for solving optimization problems: A selective survey

Population based meta-heuristic techniques for solving optimization problems: A selective survey

... of optimization implicitly include reducing computational time and ...of optimization have grown over the ...as Ant Colony Optimization (ACO) have found significant applications in ... See full document

6

A Self-Adaptive Discrete PSO Algorithm with Heterogeneous Parameter Values for Dynamic TSP

A Self-Adaptive Discrete PSO Algorithm with Heterogeneous Parameter Values for Dynamic TSP

... Figure 3. Numbers of new (different) edges between X k−1 and X k (the previous and current positions) in the DPSO solving the static kroA200 TSP instance. The blue line indicates the particles with the first set ... See full document

16

Population-based optimization algorithms for solving the travelling salesman problem

Population-based optimization algorithms for solving the travelling salesman problem

... examples of such interactive behavior are bee dancing during the food procurement, ants’ pheromone secretion, and performance of specific acts which signal the other insects to start performing the same actions. Based on ... See full document

36

The Case Study of Swarm Intelligence Optimization Algorithm Performance

The Case Study of Swarm Intelligence Optimization Algorithm Performance

... Particle Swarm Optimization (PSO) was invented by Russell Eberhart and James Kennedy in ...on optimization problems. Particle Swarm Optimization might sound complicated, but it's really ... See full document

8

Comparative Analysis of Clustering by using
          Optimization Algorithms

Comparative Analysis of Clustering by using Optimization Algorithms

... The proposed model focuses on the above objectives which are helpful in improving the classification parameters and are practically implemented using MATLAB 7.11.0 environment. In this proposed work, we used Particle ... See full document

6

Evaluation of the Performance of Ant Colony Optimization over Particle Swarm Optimization

Evaluation of the Performance of Ant Colony Optimization over Particle Swarm Optimization

... In every iteration, each particle is updated by following two “best” values. The first one is best solution it has achieved; its value is called pbest. Another “best” value that is tracked by the particle swarm ... See full document

7

Survey of Metaheuristic Algorithms for Combinatorial Optimization

Survey of Metaheuristic Algorithms for Combinatorial Optimization

... a new crossover operation is introduced for the proposed genetic algorithm ...the new crossover operation, population reformulates operation, multi mutation operation, partial local optimal mutation ... See full document

11

IJCSMC, Vol. 4, Issue. 5, May 2015, pg.271 – 277 REVIEW ARTICLE A Review on Traffic Route Optimizing by Using Different Swarm Intelligence Algorithm

IJCSMC, Vol. 4, Issue. 5, May 2015, pg.271 – 277 REVIEW ARTICLE A Review on Traffic Route Optimizing by Using Different Swarm Intelligence Algorithm

... Yanfang Deng and Hengqing Tong (2010) proposed a PSO particle swarm optimization [24]. In there work, a hybrid PSO algorithm combined fluid neural network (FNN) to search for the shortest path in stochastic ... See full document

7

An Enhanced Cooperative Harmony Search Algorithm for Solving Optimization Problems

An Enhanced Cooperative Harmony Search Algorithm for Solving Optimization Problems

... a new meta-heuristic algorithm which is inspired by a process involving musical ...stochastic optimization technique that is similar to genetic algorithm (GA) and particle swarm optimization ... See full document

9

Incorporating Space Division At Initialization Phase Of Bees Algorithm

Incorporating Space Division At Initialization Phase Of Bees Algorithm

... According to Jebari and Madiafi (2013), evolution begins from a randomly generated individuals population that been selected from search space which is an iterative process. The population is known as generation in every ... See full document

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