Using computing algorithms to generate personalized learning resources provide the needs and improve capabilities, preferences, academic performance of diverse learners is creating preferred environments. As more resources, strategies techniques are frequently added these e-learning systems, input data personalize path has been growing exponentially making swift responses learner’s requests dif...

This paper presents a robust hybrid improved dolphin echolocation and ant colony optimization algorithm (IDEACO) for optimization of truss structures with discrete sizing variables. The dolphin echolocation (DE) is inspired by the navigation and hunting behavior of dolphins. An improved version of dolphin echolocation (IDE), as the main engine, is proposed and uses the positive attributes of an...

—The implementation methods of the tasks assignment and tasks scheduling for Wireless Sensor and Actuator Network (WSAN) are proposed in this paper. Firstly, the distributed auction algorithm was used to assign tasks to the optimal actuators. Secondly, the Ant Colony Optimization (ACO) algorithm whose parameters were optimized by Particle Swarm Optimization algorithm (PSO) was proposed for the...

Time constraint is the main factor in real time operating system. Different scheduling algorithm is used to schedule the task. The Earliest Deadline First and Ant Colony Optimization is a dynamic scheduling algorithm used in a real time system and it is most beneficial scheduling algorithm for single processor real-time operating systems when the systems are preemptive and under loaded. The mai...

Ant Colony Optimization (ACO) is a well-known metaheuristic in which a colony of artificial ants cooperates in exploring good solutions to a combinatorial optimization problem. In this paper, an ACO algorithm is presented for the graph coloring problem. This ACO algorithm conforms to Max-Min Ant System structure and exploits a local search heuristic to improve its performance. Experimental resu...

In optimization problem, Genetic Algorithm (GA) and Ant Colony Optimization Algorithm (ACO) have been known as good alternative techniques. GA is designed by adopting the natural evolution process, while ACO is inspired by the foraging behaviour of ant species. This paper presents a hybrid GA-ACO for Travelling Salesman Problem (TSP), called Genetic Ant Colony Optimization (GACO). In this metho...

The crowding population based ant colony optimization algorithm (CP-ACO) uses a different pheromone update in comparison to other ACO algorithms. In this paper, crowding population based ant colony optimization algorithm is proposed to solve service restoration problem. The most notable achievement featured in this paper is run time reduction of algorithm to solve the service restoration task. ...

This paper presents improved Ant Colony Optimization (ACO) algorithms for data mining. The goal of the algorithms is to extract classification rules from data. The traditional Ant Colony Optimization algorithm is enhanced with genetic operators to develop improved ACO algorithms. The genetic operators like crossover, mutation are used to develop Ant Colony Optimization with Crossover (ACOC), AC...

Ant colony optimization (ACO) algorithms are a recently developed, population-based approach which has been successfully applied to optimization problems. However, in the ACO algorithms it is difficult to adjust the balance between intensification and diversification and thus the performance is not always very well. In this work, we propose an improved ACO algorithm in which some of ants can ev...