Different optimization methods are available for optimum design of structures including; classical optimization techniques and meta-heuristic optimization algorithms. However, engineers do not generally use optimization techniques to design a structure. They attempt to decrease the structural weight and increase its performance and efficiency, empirically, by changing the variables and controlling the constraints. Based on this professional engineering design philosophy, in this paper, a simple algorithm, termed the constraint control method (CCM), is developed and presented whereby optimum design is achieved gradually by controlling the problem constraints. Starting with oversized sections, the design is gradually improved by changing sections based on a ‘ control function’ and controlling the constraints to be below the target values. As the constraints move towards their targets, the design moves towards an optimum. The general functionality of the proposed algorithm is first demonstrated by solving several linear and nonlinear mathematical problems which have exact answers. The performance of the algorithm is then evaluated through comparing design optimization results of three, 2D steel frame benchmark problems with those from other, metaheuristic optimization solutions. the proposed method leads to the minimum structural weight while performing much smaller number of structural analyses, compared to other optimization methods.

In recent decades optimal control problems with partial differential equation constraints have been studied extensively. These issues are very complex and the numerical solution of such problems is of great importance. In this article we will discuss the solution of elliptic optimal control problem. First, by using the finite element method we obtain to gain the discrete form of the problem. The obtained discrete problem is actually a large scale constrained optimization problem. Solving this optimization problem with traditional methods is difficult and requires a lot of CPU time and memory.But split Bergman method converts the constrained problem to an unconstrained problem, and hence it saves time and memory requirement. We then use the split Bergman iterative methods for solving this problem, and examples show the speed and accuracy of split Bergman iterative methods for solving this type of problems. We also use the SQP method for solving the problem and compare with split Bergman method.

Many Important problems In Artificial Intelligence can be defined as Constralnt Satisfaction Problems (CSP). These types of problems are defined by a limited set of variables, each having a limited domain and a number of Constralnts on the values of those variables (these problems are also called Consistent Labelling Problems (CLP), in which "Labeling" nuans assigning a value to a variable.) Solution to these problems is a set of unique values for variables such that all the problem constralnts are satisfied. Several search algorithms have been proposed for solving these problems, som of which reduce the need for bacJctracklng by doing some sort of looking to future, and produce more efficient solutions. These are the so-called Forward Checking (FC), Partialiy Lookahead (PL), and Fully Lookahead (FL) algorithms. They are different In terms of the amount of looking to the future, number of backtracks thaJ are performed, and the quality of the solution that they find. In this paper, wepropose a new search algorithm we call Modified Fully Lookahead (MFL) which is Shown to be more efficient than the original Fully Lookahead algorithm

The goal of theory of constraints (TOC) is to maximize output, which is achieved by identifying and managing the critically constrained resources. To manage the constraints, Goldratt proposed five focusing steps (5FS). If we increase constrained output, the output of system will be increased. In this paper, we focus on step four of the 5FS and use the remained capacity of nonconstraint to elevate the system’s constraint.

In this paper, the time-optimal control for point-to-point motion of robotic manipulators, with fixed initial and final states, bounded control inputs and jerk constraint for entire path is presented. First, the jerk constraint with respect to state variables is derived. Afterwards the dynamic equations of robotic manipulator in state space form and jerk constraints discretized by forward difference technique. Thus the time-optimal control problem is formulated as a constrained parameter optimization problem. The MATLAB optimization toolbox is used to obtain the solution of the constrained parameter optimization problem. The proposed method can help us to implement the time-optimal control problem with bounded inputs and jerk constraint. Finally, the above-mentioned method is implemented for a 2R planar robotic manipulator. Simulation results show a smooth trajectory for robot motion when the jerk is limited, which can effectively reduce mechanical wear at joints.

In this paper, the time-optimal control problem for point-to-point motion of robot manipulators with elastic joints, bounded control inputs and jerk constraint on entire path is studied. First, The dynamic equations and jerk constraint are derived for a elastic joint manipulator. Then, the jerk constraint and equations of motion in the state space form are discredited by forward difference technique. Thus the time optimal control problem is formulated as a constrained parameters optimization problem. The MATLAB optimization toolbox is used to obtain the solution of the optimization problem. This method helps to implement the jerk constraint, easily. The above mentioned method is also implemented for a single link with elastic joint, and some of results are presented. The results show a smooth trajectory for robot motion when the jerk is limited, which reduce the vibration and mechanical wear at the joints.

This paper presents a novel method for path planning of redundant manipulators. The idea is to use load distribution as an objective function for resolving kinematic redundancy. The method is based on imposing a set of convenient relation among joint torques as dynamic constraints to system. Two algorithms for path planning of redundant manipulators subject to dynamic constraints are proposed. The first algorithm takes advantage of all necessary dynamic constraint equations which can be approximately solved using a pseudo-inverse technique, whereas the second algorithm is based on finding a good starting point with fair load distribution and generate the path by imposing as many dynamic constraint equations as the number of degrees of redundancy. The results for path planning of a 3-DOF planar manipulator using this method are presenteded.

In this paper an uncertain multi objective closed-loop supply chain is developed. The first objective function is maximizing the total profit. The second objective function is minimizing the use of row materials. In the other word, the second objective function is maximizing the amount of remanufacturing and recycling. Genetic algorithm is used for optimization; and for finding the pareto optimal line, Epsilon-constraint method is used. Finally a numerical example is solved with proposed approach and performance of the model is evaluated in different sizes. The results show that this approach is effective and useful for managerial decisions.