Study of logistics distribution route based on improved genetic algorithm and ant colony optimization algorithm

To improve the service quality of cloud computing, and aiming at the characteristics of resource scheduling optimization problems, this paper proposes a hybrid multi-objective bat algorithm. To prevent the algorithm from falling into a local minimum, the bat population is classified. The back-propagation algorithm based on the mean square error and the conjugate gradient method is used to increase the loudness in the search direction and the pulse emission rate. In addition, the random walk based on lévy flight is also used to improve the optimal solution, thereby improving the algorithm’s global search capability. The simulation results prove that the multi-objective bat algorithm proposed in this paper is superior to the multi-objective ant colony optimization algorithm, genetic algorithm, particle swarm algorithm, and cuckoo search algorithm in terms of makespan, degree of imbalance, and throughput. The cost is also slightly better than the multi-objective ant colony optimization algorithm and the multi-objective genetic algorithm.

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Optimization of operation sequencing based on GA-Jaya algorithm

Journal of Physics Conference Series ◽

10.1088/1742-6596/2113/1/012075 ◽

2021 ◽

Vol 2113 (1) ◽

pp. 012075

Author(s):

Kai Zheng ◽

Rui Zhang ◽

Zhen-Wei Zhu ◽

Hua-Dong Zhao

Keyword(s):

Genetic Algorithm ◽

Particle Swarm Optimization ◽

Ant Colony Optimization ◽

Optimization Algorithm ◽

Particle Swarm Optimization Algorithm ◽

Ant Colony ◽

Jaya Algorithm ◽

Ant Colony Optimization Algorithm ◽

Operation Sequencing ◽

Swarm Optimization

Abstract To solve the operation sequencing problem in CAPP that is a difficult problem, combining the idea of genetic algorithm, an GA-Jaya algorithm is proposed to minimize the total cost. In the GA-Jaya, the population is initialized according to the procedure priority adjacency matrix which makes the population all meet the process priority relationship. Mutation iteration operator and two kinds of crossover iteration operator are designed for process sequence and processing resource evolution. The GA-Jaya algorithm is applied to a typical case, and compared with the existing genetic algorithm, particle swarm optimization algorithm and ant colony optimization algorithm. The results show that the average quality of the solution obtained by the GA-Jaya algorithm is better than the existing genetic algorithm, particle swarm optimization algorithm and ant colony optimization algorithm.

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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.

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