Gear Design Optimization Algorithms: A Review

2020 ◽  
Vol 27 (1) ◽  
Author(s):  
YO Usman ◽  
PO Odion ◽  
EO Onibere ◽  
AY Egwoh
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Design Optimization ◽  
Ant Colony Optimization ◽  
Optimization Algorithms ◽  
Optimization Techniques ◽  
Ant Colony ◽  
Design Parameters ◽  
Gear Design ◽  
Complex Design

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

Ant Colony Optimization for Factory Layout

2012 ◽  
Vol 591-593 ◽  
pp. 758-761
Author(s):  
Xiu Zeng ◽  
Qian Li Ma
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Ant Colony Optimization ◽  
Heuristic Algorithms ◽  
Mean Value ◽  
Ant Colony ◽  
Logistics Cost ◽  
The Mean ◽  
Factory Layout ◽  
Np Problem

Factory layout is NP problem[1]. There are many methods to solve it ,such as engineering diagram, flow chart method, various heuristic algorithms, SA( simulated annealing) and GA(genetic algorithm) [2].ACO (ant colony optimization) is used to solve it in this paper. The logistics costs exist between two workshops that are treated as pheromone that guides ants to search the best solution. Smaller logistics cost is, stronger the two workshops of relation is. In the process of optimization theworkshop with low logistics cost is more likely to be chosen, which minimizes the system logistics cost. Compared with GA, ACO has the advantage in speed. The mean value of the solution, the best solution, the worst solution is better too. More the number of workshop is, more obvious the superiority is.

scholarly journals Ant Colony Optimization for Resolving Unit Commitment Issues by Considering Reliability Constraints

2019 ◽  
Vol 2 (4) ◽  
pp. 120
Author(s):  
Alan Abdu Robbi Afifi ◽  
Sarjiya Sarjiya ◽  
Yusuf Susilo Wijoyo
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Ant Colony Optimization ◽  
Reliability Index ◽  
Unit Commitment ◽  
Ant Colony ◽  
Complex Combination ◽  
Generator Scheduling ◽  
Index Combination

Unit Commitment or generator scheduling is one of complex combination issues aiming to obtain the cheapest generating power total costs. Ant Colony Optimization is proposed as a method to solve Unit Commitment issues because it has a better result convergence according to one of journals that reviews methods to solve Unit Commitment issues. Ant Colony Optimization modification into Nodal Ant Colony Optimization as well as addition of several elements are also conducted to overcome Ant Colony Optimization limitations in resolving Unit Commitment issues. Nodal Ant Colony Optimization simulations are then compared with Genetic Algorithm and Simulated Annealing methods which previously has similar simulations. Reliability index combination in a form of Loss of Load Probability and Expected Unserved Energy are also added as reliability constraints in the system. Comparison of three methods shows that Nodal Ant Colony Optimization is able to provide better results up to 0.08% cheaper than Genetic Algorithm or Simulated Annealing methods.

scholarly journals Application of Nontraditional Optimization Techniques for Airfoil Shape Optimization

2012 ◽  
Vol 2012 ◽  
pp. 1-6 ◽  
Author(s):  
R. Mukesh ◽  
K. Lingadurai ◽  
U. Selvakumar
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Shape Optimization ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Optimization Algorithms ◽  
Optimization Techniques ◽  
Aerodynamic Shape Optimization ◽  
Shape Optimization Problem ◽  
Aerodynamic Shape

The method of optimization algorithms is one of the most important parameters which will strongly influence the fidelity of the solution during an aerodynamic shape optimization problem. Nowadays, various optimization methods, such as genetic algorithm (GA), simulated annealing (SA), and particle swarm optimization (PSO), are more widely employed to solve the aerodynamic shape optimization problems. In addition to the optimization method, the geometry parameterization becomes an important factor to be considered during the aerodynamic shape optimization process. The objective of this work is to introduce the knowledge of describing general airfoil geometry using twelve parameters by representing its shape as a polynomial function and coupling this approach with flow solution and optimization algorithms. An aerodynamic shape optimization problem is formulated for NACA 0012 airfoil and solved using the methods of simulated annealing and genetic algorithm for 5.0 deg angle of attack. The results show that the simulated annealing optimization scheme is more effective in finding the optimum solution among the various possible solutions. It is also found that the SA shows more exploitation characteristics as compared to the GA which is considered to be more effective explorer.

Applications of Cultural Ant Colony Optimization in Optimal Excavator Mechanisms Design

2012 ◽  
Vol 479-481 ◽  
pp. 1857-1862
Author(s):  
Pei Qing Xie ◽  
Shu Wen Lin
Keyword(s):  
Design Optimization ◽  
Ant Colony Optimization ◽  
Engineering Design ◽  
Optimization Algorithms ◽  
Ant Colony ◽  
Optimization Process ◽  
Unsatisfactory Result ◽  
Engineering Design Optimization ◽  
Low Efficiency

In allusion to the low efficiency and unsatisfactory result of the tradional optimization algorithms in existence for engineering design optimization,this paper proposes a cultural ant colony optimization(CACO) algorithm for application in design optimization of excavator’s mechanisms to improve the excavator’s performance efficiently. Through testing and verifying experiments,it is concluded that CACO can discovery knowledge during optimization process and use the knowledge to guide the heuristic searching process,furthermore,it is an appropriate algorithm for the optimization of excavator mechanisms. CACO costs less time and can get better quality solution to improve excavator’s main porformances.

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

2016 ◽  
Vol 3 (2) ◽  
pp. 149-158 ◽  
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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