A Robust Conjugate-Gradient-Penalty Function Algorithm for Solving General Constrained Optimization Problems with Application to Computer-Aided Design

1978 ◽  
Vol 11 (1) ◽  
pp. 1049-1056 ◽  
Author(s):  
W. Gesing ◽  
E.J. Davison
Keyword(s):  
Constrained Optimization ◽  
Penalty Function ◽  
Conjugate Gradient ◽  
Computer Aided Design ◽  
Optimization Problems ◽  
Constrained Optimization Problems ◽  
Computer Aided ◽  
Aided Design

scholarly journals Composite Differential Evolution with Modified Oracle Penalty Method for Constrained Optimization Problems

2014 ◽  
Vol 2014 ◽  
pp. 1-15 ◽  
Author(s):  
Minggang Dong ◽  
Ning Wang ◽  
Xiaohui Cheng ◽  
Chuanxian Jiang
Keyword(s):  
Differential Evolution ◽  
Constrained Optimization ◽  
Penalty Function ◽  
Optimization Problems ◽  
Real Life ◽  
Optimization Criterion ◽  
Constrained Optimization Problems ◽  
Constraints Handling ◽  
Efficient Alternative ◽  
Handling Methods

Motivated by recent advancements in differential evolution and constraints handling methods, this paper presents a novel modified oracle penalty function-based composite differential evolution (MOCoDE) for constrained optimization problems (COPs). More specifically, the original oracle penalty function approach is modified so as to satisfy the optimization criterion of COPs; then the modified oracle penalty function is incorporated in composite DE. Furthermore, in order to solve more complex COPs with discrete, integer, or binary variables, a discrete variable handling technique is introduced into MOCoDE to solve complex COPs with mix variables. This method is assessed on eleven constrained optimization benchmark functions and seven well-studied engineering problems in real life. Experimental results demonstrate that MOCoDE achieves competitive performance with respect to some other state-of-the-art approaches in constrained optimization evolutionary algorithms. Moreover, the strengths of the proposed method include few parameters and its ease of implementation, rendering it applicable to real life. Therefore, MOCoDE can be an efficient alternative to solving constrained optimization problems.

Methods and algorithms of synthesis and analysis of design and technological solutions in the computer-aided design of engineering structures

2018 ◽  
Author(s):  
Сабир Якубов ◽  
S. Yakubov
Keyword(s):  
Feedback Control ◽  
Doctoral Students ◽  
Process Improvement ◽  
Wide Class ◽  
Computer Aided Design ◽  
Optimization Problems ◽  
Control Process ◽  
Engineering Structures ◽  
Computer Aided ◽  
Aided Design

The monograph presents the results of scientific research on the optimization of engineering structures, assuming the formalization and algorithmization of design and technological solutions in the computer-aided design (CAD). The process of designing optimal engineering structures is considered as a feedback control process. Improvement of methods and means of automation for solving optimization problems, as well as problems of calculation and design of engineering structures led to the synthesis of these methods and the creation of a wide class of objects based on CAD. The monograph is intended for scientists, researchers, doctoral students, undergraduates and students.

HYBRID-FITNESS FUNCTION EVOLUTIONARY ALGORITHM BASED ON SIMPLEX CROSSOVER AND PSO MUTATION FOR CONSTRAINED OPTIMIZATION PROBLEMS

2009 ◽  
Vol 23 (01) ◽  
pp. 115-127 ◽  
Author(s):  
YIBO HU
Keyword(s):  
Constrained Optimization ◽  
Evolutionary Algorithm ◽  
Penalty Function ◽  
Feasible Solution ◽  
Optimization Problems ◽  
Fitness Function ◽  
Benchmark Problems ◽  
Crossover Operator ◽  
Hybrid Fitness ◽  
Constrained Optimization Problems

For constrained optimization problems, evolutionary algorithms often utilize a penalty function to deal with constraints, even if it is difficult to control the penalty parameters. To overcome this shortcoming, this paper presents a new penalty function which has no parameter and can effectively handle constraint first, after which a hybrid-fitness function integrating this penalty function into the objective function is designed. The new fitness function can properly evaluate not only feasible solution, but also infeasible one, and distinguish any feasible one from an infeasible one. Meanwhile, a new crossover operator based on simplex crossover operator and a new PSO mutation operator are also proposed, which can produce high quality offspring. Based on these, a new evolutionary algorithm for constrained optimization problems is proposed. The simulations are made on ten widely used benchmark problems, and the results indicate the proposed algorithm is effective.

Improved Hybrid Taboo Search with its Application in Computer-Aided Optimization Problem

2012 ◽  
Vol 488-489 ◽  
pp. 1293-1297
Author(s):  
Jia Yang Wang ◽  
Bi Zhang ◽  
Zuo Yong Li ◽  
Lei Xu
Keyword(s):  
Simulated Annealing ◽  
Computer Aided Design ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Test Functions ◽  
Local Optima ◽  
Taboo Search ◽  
Computer Aided ◽  
Aided Design ◽  
Improved Algorithm

A new improved algorithm of Taboo Search (TS), namely, Hybrid Taboo Search (HTS) is first introduced and tried for several test functions having multiple local optima. Here, Taboo Search was improved by combining Immune Arithmetic (IA) and Simulated Annealing (SA). Several strategies to improve the TS have been presented before, but the focus here is on the novelty, availability and precision of algorithm. There are several optimization problems in computer-aided design, so the article used the improved HTS in computer-aided optimization problems, the performance of which is compared with the performance of conventional TS (TS). Results show that HTS plays an important role in solving computer-aided optimization problems with the effectiveness and higher accuracy.

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