Optimum Dynamic Design of Planar Linkage Using Genetic Algorithms.

Guo, Gang; Morita, Nobuyoshi; Torii, Takao

doi:10.1299/kikaic.65.2515

Cited by 2 publications

(2 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Also, the average value is reduced from 11.84 to 5.30. Guo et al (2000) used GA to solve an optimization problem formulated to balance planar mechanism based on three different approaches -(1) mass redistribution, (2) counterweight addition and (3) mixed mass redistribution. The results obtained using genetic algorithm was found better as compared to the traditional nonlinear optimization technique.…”

Section: ) Learner Phase :Learning Through Interactionmentioning

confidence: 99%

Optimization Techniques for Engineering Design

Chaudhary¹

2017

IJRASET

View full text Add to dashboard Cite

This paper discusses the popular evolutionary optimization technique, Genetic Algorithm (GA)and Teaching-learningbased Optimization (TLBO) algorithm. It also covers the definitions of various parameters used by these algorithms. I.INTRODUCTION Most of the engineering design problems are competing multi-objective problems for which the optimal values of the design variables are searched that optimize several objectivesfor a given set of constraints. The different methods available to formulate a multi-objective problem as a single objective problem are weighted global criterion method, weighted sum method, lexicographic method, weighted min-max method, exponential weighted criterion, weighted product method, goal programming methods, bounded objective function method, and physical programming (Marled and Arora, 2004).The weighted sum approach is more widely used in which a normalized objective function is formulated by assigning proper weighting factors to all the objectives. By selecting different values of the weighting factorsto objectives, the results are obtained as a set of optimum solutions and each solution in this set is a trade-off between the different objectives (Marled and Arora, 2010). A constrained optimization problem is considered more complex than that of an unconstrained problem. It finds a feasible solution that optimizes one or more mathematical functions in a constrained search space. The constrained optimization problem is transformed into an unconstrained optimization problem by modifying the objective function on the basis of the constraint violations After formulating the optimization problem, it can be solved by using either traditional or evolutionary optimization algorithms. The traditional or classical optimization algorithms are based on deterministic approach, i.e., they use gradient information of objective function with respect to the design variables and move from one solution to other following the specific rules. Depending on the starting solution these algorithmsmay end up with a local optimum solution. Therefore, one has to explore all local solutions; one of them is the global optimum solution. To improve the chances of getting the global optimum solution, a large set of randomly generated initial solutions is required for these algorithms. The global optimum solution is then found as the best of all local optimum solution provided by different instances of the algorithm.The popular methods in this category are quadratic programming, steepest descent method, linear programming, nonlinear programming, dynamic programming and geometric programming, etc. For the complex optimization problem having a large number of design variables and multiple local minimum solutions, these methods converge on the optimum solution near to the initial solution provided and thus produce local optimum solution (Marler and Arora, 2004;Mariappan and Krishnamurty, 1996).These techniques are generally not suitable for the optimization problems with (1) large number of constraints (2) large...

show abstract

Section: ) Learner Phase :Learning Through Interactionmentioning

confidence: 99%

Optimization Techniques for Engineering Design

Chaudhary¹

2017

IJRASET

View full text Add to dashboard Cite

show abstract

“…The mixed mass redistribution method using genetic algorithm is applied for reducing shaking force and shaking moment in mechanisms [23].…”

Section: Introductionmentioning

confidence: 99%

Dynamic balancing of planar mechanisms using genetic algorithm

Chaudhary

2014

J MECH SCI TECHNOL

View full text Add to dashboard Cite

This paper presents an optimization technique to dynamically balance the planar mechanisms in which the shaking forces and shaking moments are minimized using the genetic algorithm (GA). A dynamically equivalent system of point-masses that represents each rigid link of a mechanism is developed to represent link's inertial properties. The shaking force and shaking moment are then expressed in terms of the point-mass parameters which are taken as the design variables. These design variables are brought into the optimization scheme to reduce the shaking force and shaking moment. This formulates the objective function which optimizes the mass distribution of each link. First, the problem is formulated as a single objective optimization problem for which the genetic algorithm produces better results as compared to the conventional optimization algorithm. The same problem is then formulated as a multi-objective optimization problem and multiple optimal solutions are created as a Pareto front by using the genetic algorithm. The masses and inertias of the optimized links are computed from the optimized design variables. The effectiveness of the proposed methodology is shown by applying it to a standard problem of four-bar planar mechanism available in the literature.

show abstract

Optimum Dynamic Design of Planar Linkage Using Genetic Algorithms.

Cited by 2 publications

References 0 publications

Optimization Techniques for Engineering Design

Optimization Techniques for Engineering Design

Dynamic balancing of planar mechanisms using genetic algorithm

Contact Info

Product

Resources

About