A Novel Decomposition-Based Evolutionary Algorithm for Engineering Design Optimization

2017 ◽  
Vol 139 (4) ◽  
Author(s):  
Kalyan Shankar Bhattacharjee ◽  
Hemant Kumar Singh ◽  
Tapabrata Ray
Keyword(s):  
Evolutionary Algorithm ◽  
Design Optimization ◽  
Wind Farm ◽  
Optimization Problems ◽  
Constrained Optimization Problems ◽  
Wind Farm Layout Optimization ◽  
Wide Range ◽  
Replacement Scheme ◽  
Assignment Scheme ◽  
Significant Attention

In recent years, evolutionary algorithms based on the concept of “decomposition” have gained significant attention for solving multi-objective optimization problems. They have been particularly instrumental in solving problems with four or more objectives, which are further classified as many-objective optimization problems. In this paper, we first review the cause-effect relationships introduced by commonly adopted schemes in such algorithms. Thereafter, we introduce a decomposition-based evolutionary algorithm with a novel assignment scheme. The scheme eliminates the need for any additional replacement scheme, while ensuring diversity among the population of candidate solutions. Furthermore, to deal with constrained optimization problems efficiently, marginally infeasible solutions are preserved to aid search in promising regions of interest. The performance of the algorithm is objectively evaluated using a number of benchmark and practical problems, and compared with a number of recent algorithms. Finally, we also formulate a practical many-objective problem related to wind-farm layout optimization and illustrate the performance of the proposed approach on it. The numerical experiments clearly highlight the ability of the proposed algorithm to deliver the competitive results across a wide range of multi-/many-objective design optimization problems.

Composite Evolutionary Algorithm for Constrained Optimization

2012 ◽  
Vol 220-223 ◽  
pp. 2846-2851
Author(s):  
Si Lian Xie ◽  
Tie Bin Wu ◽  
Shui Ping Wu ◽  
Yun Lian Liu
Keyword(s):  
Evolutionary Algorithms ◽  
Constrained Optimization ◽  
Evolutionary Algorithm ◽  
Selection Criteria ◽  
Optimization Problems ◽  
Experimental Results ◽  
Constrained Optimization Problems ◽  
Mutation Operators ◽  
Single Operator ◽  
Algorithm Framework

Evolutionary algorithms are amongst the best known methods of solving difficult constrained optimization problems, for which traditional methods are not applicable. Due to the variability of characteristics in different constrained optimization problems, no single evolutionary with single operator performs consistently over a range of problems. We introduce an algorithm framework that uses multiple search operators in each generation. A composite evolutionary algorithm is proposed in this paper and combined feasibility rule to solve constrained optimization problems. The proposed evolutionary algorithm combines three crossover operators with two mutation operators. The selection criteria based on feasibility of individual is used to deal with the constraints. The proposed method is tested on five well-known benchmark constrained optimization problems, and the experimental results show that it is effective and robust

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.

scholarly journals Improved Quantum-Inspired Evolutionary Algorithm for Engineering Design Optimization

2012 ◽  
Vol 2012 ◽  
pp. 1-27 ◽  
Author(s):  
Jinn-Tsong Tsai ◽  
Jyh-Horng Chou ◽  
Wen-Hsien Ho
Keyword(s):  
Evolutionary Algorithm ◽  
Design Optimization ◽  
Engineering Design ◽  
Optimization Problems ◽  
Latin Square ◽  
Latin Squares ◽  
Feasible Region ◽  
Continuous Variables ◽  
Nonlinear Design ◽  
Discrete Values

An improved quantum-inspired evolutionary algorithm is proposed for solving mixed discrete-continuous nonlinear problems in engineering design. The proposed Latin square quantum-inspired evolutionary algorithm (LSQEA) combines Latin squares and quantum-inspired genetic algorithm (QGA). The novel contribution of the proposed LSQEA is the use of a QGA to explore the optimal feasible region in macrospace and the use of a systematic reasoning mechanism of the Latin square to exploit the better solution in microspace. By combining the advantages of exploration and exploitation, the LSQEA provides higher computational efficiency and robustness compared to QGA and real-coded GA when solving global numerical optimization problems with continuous variables. Additionally, the proposed LSQEA approach effectively solves mixed discrete-continuous nonlinear design optimization problems in which the design variables are integers, discrete values, and continuous values. The computational experiments show that the proposed LSQEA approach obtains better results compared to existing methods reported in the literature.

Parallel Evolutionary Algorithm for Computationally Expensive Single Criteria Design Optimization

2014 ◽  
Vol 555 ◽  
pp. 586-592
Author(s):  
Stanisław Krenich
Keyword(s):  
Evolutionary Algorithms ◽  
Evolutionary Algorithm ◽  
Design Optimization ◽  
Optimization Problems ◽  
Optimal Solution ◽  
Computation Method ◽  
Mechanism Optimization ◽  
Parallel Evolutionary Algorithms ◽  
Parallel Evolutionary Algorithm ◽  
Method Analysis

The paper presents an approach to design optimization using parallel evolutionary algorithms. The only use of a simple evolutionary algorithm in order to generate the optimal solution for complex problems can be ineffective due to long calculation time. Thus a tournament evolutionary algorithm (EA) and a parallel computation method are proposed and used. The proposed EA does not require an analysis of the optimization model for each potential solution from evolutionary populations. The second element of the method consists in parallel running of evolutionary algorithms using multi-threads approach. The experiments were carried out for many different single design optimization problems and two of them are presented in the paper. The first problem considers a task of robot gripper mechanism optimization and the second one deals with the optimization of a shaft based on Finite Element Method analysis. From the generated results it is clear that proposed approach is a very effective tool for solving fairly complicated tasks considering both the accuracy and the time of calculation.

Sign in / Sign up

Export Citation Format

Share Document