Structural Optimization With Nonlinear Goal Programming Using Powell’s Method

1991 ◽  
Author(s):  
Mohamed E. M. El-Sayed ◽  
T. S. Jang
Keyword(s):  
Structural Optimization ◽  
Goal Programming ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Numerical Test ◽  
Design Tool ◽  
Test Cases ◽  
Programming Techniques ◽  
Ordered Design

Abstract This paper presents a method for solving structural optimization problems using nonlinear goal programming techniques. The developed method removes the difficulty of having to define an objective function and constraints. It also has the capacity of handling rank ordered design objectives or goals. The formulation of the structural optimization problem into a goal programming form is discussed. The resulting optimization problem is solved using Powell’s conjugate direction search algorithm. To demonstrate the effectiveness of the method, as a design tool, the solutions of some numerical test cases are included.

Large Scale Structural Optimization With Linear Goal Programming

1990 ◽  
Author(s):  
Mohamed E. M. El-Sayed ◽  
T. S. Jang
Keyword(s):  
Structural Optimization ◽  
Goal Programming ◽  
Large Scale ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Minimum Weight ◽  
Design Tool ◽  
Linear Goal Programming ◽  
Programming Techniques ◽  
Structural Optimization Problem

Abstract This paper presents a method for solving large scale structural optimization problems using linear goal programming techniques. The method can be used as a multicriteria optimization tool since goal programming removes the difficulty of having to define an objective function and constraints. It also has the capacity of handling rank ordered design objectives or goals. The method uses finite element analysis, linear goal programming techniques and successive linearization to obtain the solution for the nonlinear goal optimization problems. The general formulation of the structural optimization problem into a nonlinear goal programming form is presented. The successive linearization method for the nonlinear goal optimization problem is discussed. To demonstrate the validity of the method, as a design tool, the solution of the minimum weight structural optimization problem with stress constraints for 10, 25 and 200 truss problems are included.

Two Meta-Heuristics for Solving Unconstrained Optimization Problems and Machinery Problems

2014 ◽  
Vol 1044-1045 ◽  
pp. 1418-1423
Author(s):  
Pasura Aungkulanon
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Problem ◽  
Heuristic Algorithms ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Harmony Search ◽  
Particle Swarm ◽  
Finite Sequence ◽  
Swarm Optimization ◽  
Machining Optimization

Machining optimization problem aims to optimize machinery conditions which are important for economic settings. The effective methods for solving these problems using a finite sequence of instructions can be categorized into two groups; exact optimization algorithm and meta-heuristic algorithms. A well-known meta-heuristic approach called Harmony Search Algorithm was used to compare with Particle Swarm Optimization. We implemented and analysed algorithms using unconstrained problems under different conditions included single, multi-peak, curved ridge optimization, and machinery optimization problem. The computational outputs demonstrated the proposed Particle Swarm Optimization resulted in the better outcomes in term of mean and variance of process yields.

Study on Free Search for Combinatorial Optimization Problem

2013 ◽  
Vol 411-414 ◽  
pp. 1904-1910
Author(s):  
Kai Zhong Jiang ◽  
Tian Bo Wang ◽  
Zhong Tuan Zheng ◽  
Yu Zhou
Keyword(s):  
Combinatorial Optimization ◽  
Optimization Problem ◽  
Feasible Solution ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Search Process ◽  
Standard Data ◽  
Combinatorial Optimization Problems ◽  
Free Search

An algorithm based on free search is proposed for the combinatorial optimization problems. In this algorithm, a feasible solution is converted into a full permutation of all the elements and a transformation of one solution into another solution can be interpreted the transformation of one permutation into another permutation. Then, the algorithm is combined with intersection elimination. The discrete free search algorithm greatly improves the convergence rate of the search process and enhances the quality of the results. The experiment results on TSP standard data show that the performance of the proposed algorithm is increased by about 2.7% than that of the genetic algorithm.

Gradient-based optimization method for interference suppression of linear arrays by the amplitude-only and phase-only control

2021 ◽  
pp. 1-7
Author(s):  
Renjing Gao ◽  
Yi Tang ◽  
Qi Wang ◽  
Shutian Liu
Keyword(s):  
Optimization Algorithm ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Interference Suppression ◽  
Computation Time ◽  
Optimization Method ◽  
Main Beam ◽  
Linear Arrays ◽  
Gradient Based

Abstract This paper presents a gradient-based optimization method for interference suppression of linear arrays by controlling the electrical parameters of each array element, including the amplitude-only and phase-only. Gradient-based optimization algorithm (GOA), as an efficient optimization algorithm, is applied to the optimization problem of the anti-interference arrays that is generally solved by the evolutionary algorithms. The goal of this method is to maximize the main beam gain while minimizing the peak sidelobe level (PSLL) together with the null constraint. To control the nulls precisely and synthesize the radiation pattern accurately, the full-wave method of moments is used to consider the mutual coupling among the array elements rigorously. The searching efficiency is improved greatly because the gradient (sensitivity) information is used in the algorithm for solving the optimization problem. The sensitivities of the design objective and the constraint function with respect to the design variables are analytically derived and the optimization problems are solved by using GOA. The results of the GOA can produce the desired null at the specific positions, minimize the PSLL, and greatly shorten the computation time compared with the often-used non-gradient method such as genetic algorithm and cuckoo search algorithm.

A Two Level Parallel Evolution Strategy for Solving Mixed-Discrete Structural Optimization Problems

1995 ◽  
Author(s):  
Georg Thierauf ◽  
Jianbo Cai
Keyword(s):  
Parallel Computing ◽  
Structural Optimization ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Parallel Evolution ◽  
Evolution Strategy ◽  
Parallel Computer ◽  
Computing Architecture ◽  
Design Variables

Abstract A method for the solution of mixed-discrete structural optimization problems based on a two level parallel evolution strategy is presented. On the first level, the optimization problem is divided into two subproblems with discrete and continuous design variables, respectively. The two subproblems are solved simultaneously on a parallel computing architecture. On the second level, each subproblem is further parallelized by means of a parallel sub-evolution-strategy. Periodically, the design variables in the two groups axe exchanged. Examples are included to demonstrate the implementation of this method on a 8 nodes parallel computer.

Robust Structural Design Optimization Under Non-Probabilistic Uncertainties

2011 ◽  
Author(s):  
Jiantao Liu ◽  
Hae Chang Gea ◽  
Ping An Du
Keyword(s):  
Structural Optimization ◽  
Design Optimization ◽  
Structural Design ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Optimal Solution ◽  
Global Optimality ◽  
Worst Case ◽  
Probabilistic Uncertainty ◽  
Structural Design Optimization

Robust structural design optimization with non-probabilistic uncertainties is often formulated as a two-level optimization problem. The top level optimization problem is simply to minimize a specified objective function while the optimized solution at the second level solution is within bounds. The second level optimization problem is to find the worst case design under non-probabilistic uncertainty. Although the second level optimization problem is a non-convex problem, the global optimal solution must be assured in order to guarantee the solution robustness at the first level. In this paper, a new approach is proposed to solve the robust structural optimization problems with non-probabilistic uncertainties. The WCDO problems at the second level are solved directly by the monotonocity analysis and the global optimality is assured. Then, the robust structural optimization problem is reduced to a single level problem and can be easily solved by any gradient based method. To illustrate the proposed approach, truss examples with non-probabilistic uncertainties on stiffness and loading are presented.

scholarly journals Cuckoo Search Algorithm for Test Case Prioritization in Regression Testing

2019 ◽  
Vol 8 (3) ◽  
pp. 6004-6009
Keyword(s):  
Optimization Problems ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Regression Testing ◽  
Test Case ◽  
Test Cases ◽  
Test Case Prioritization ◽  
Aggressive Approach ◽  
Traditional Approaches

There are countless optimization problems that have been accelerated by Nature Inspired Metaheuristic Optimization Algorithms (NIMOA) in the earlier decades. NIMOA have gained huge popularity owing to their effective results. In this study NIMOA namely, Cuckoo Search Algorithm (CSA) is used to prioritize (order) the test cases for Regression Testing (RT). Prioritizations aids in the execution of higher priority test cases to give early fault detection. This research adopts the aggressive approach of reproduction made by Cuckoos to prioritize the test cases for RT. Average Percentage of Fault Detected (APFD) metrics is used in this paper for validations of results. APFD metrics is used to compare the performance of CSA with Flower Pollination Algorithm (FPA) and traditional approaches for Test Case Prioritization (TCP). Two java applications are used for the study. CSA is implemented in Java on eclipse platform. It is learnt from the study that APFD results of CSA outperformed the FPA for both the applications namely Puzzle Game and AreaandPerimeter. It is inferred from the results that prioritized set of test cases given by NIMOA outperformed the APFD results of traditional approaches and also CSA performed better than the FPA for TCP.

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