Enhancement of Genetic Algorithm and Ant Colony Optimization Techniques using Fuzzy Systems

Gearing is one of the most efficient methods of transmitting power from a source to its application with or without change of speed or direction. Gears are round mechanical components with teeth arranged in their perimeter. Gear design is complex design that involves many design parameters and tables, finding an optimal or near optimal solution to this complex design is still a major challenge. Different optimization algorithms such as Genetic Algorithm (GA), Simulated Annealing, Ant-Colony Optimization, and Neural Network etc., have been used for design optimization of the gear design problems. This paper focuses on the review of the optimization techniques used for gear design optimization with a view to identifying the best of them. Nowadays, the method used for the design optimization of gears is the evolutionary algorithm specifically the genetic algorithm which is based on the evolution idea of natural selection. The study revealed that GA. has the ability to find optimal solutions in a short time of computation by making a global search in a large search space. Keywords: Firefly Algorithm, Ant-Colony Optimization, Simulated Annealing, Genetic Algorithm, Gear design, Optimization, Particle Swarm Optimization Algorithm

Download Full-text

Comparison Performance of Genetic Algorithm and Ant Colony Optimization in Course Scheduling Optimizing

Scientific Journal of Informatics ◽

10.15294/sji.v3i2.7911 ◽

2016 ◽

Vol 3 (2) ◽

pp. 149-158 ◽

Cited By ~ 4

Author(s):

Imam Ahmad Ashari ◽

Much Aziz Muslim ◽

Alamsyah Alamsyah

Keyword(s):

Genetic Algorithm ◽

Ant Colony Optimization ◽

Optimization Algorithm ◽

Optimal Solution ◽

Optimization Techniques ◽

Ant Colony ◽

Scheduling Problems ◽

Ant Colony Optimization Algorithm ◽

Course Scheduling ◽

The University

Scheduling problems at the university is a complex type of scheduling problems. The scheduling process should be carried out at every turn of the semester's. The core of the problem of scheduling courses at the university is that the number of components that need to be considered in making the schedule, some of the components was made up of students, lecturers, time and a room with due regard to the limits and certain conditions so that no collision in the schedule such as mashed room, mashed lecturer and others. To resolve a scheduling problem most appropriate technique used is the technique of optimization. Optimization techniques can give the best results desired. Metaheuristic algorithm is an algorithm that has a lot of ways to solve the problems to the very limit the optimal solution. In this paper, we use a genetic algorithm and ant colony optimization algorithm is an algorithm metaheuristic to solve the problem of course scheduling. The two algorithm will be tested and compared to get performance is the best. The algorithm was tested using data schedule courses of the university in Semarang. From the experimental results we conclude that the genetic algorithm has better performance than the ant colony optimization algorithm in solving the case of course scheduling.

Download Full-text