A Smoothing Penalty Function Algorithm for Two-Cardinality Sparse Constrained Optimization Problems

2018 ◽  
Author(s):  
Jiang Min ◽  
Zhiqing Meng ◽  
Gengui Zhou ◽  
Rui Shen
Keyword(s):  
Constrained Optimization ◽  
Penalty Function ◽  
Optimization Problems ◽  
Constrained Optimization Problems

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.

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.

Dynamic Sub-Population Genetic Algorithm Combined with Dynamic Penalty Function to Solve Constrained Optimization Problems

2010 ◽  
Vol 450 ◽  
pp. 560-563
Author(s):  
Dong Mei Cheng ◽  
Jian Huang ◽  
Hong Jiang Li ◽  
Jing Sun
Keyword(s):  
Genetic Algorithm ◽  
Constrained Optimization ◽  
Penalty Function ◽  
Local Convergence ◽  
Population Genetic ◽  
Optimization Problems ◽  
Optimal Solution ◽  
New Method ◽  
Constrained Optimization Problems ◽  
Population Genetic Algorithm

This paper presents a new method of dynamic sub-population genetic algorithm combined with modified dynamic penalty function to solve constrained optimization problems. The new method ensures the final optimal solution yields all constraints through re-organizing all individuals of each generation into two sub-populations according to the feasibility of individuals. And the modified dynamic penalty function gradually increases the punishment to bad individuals with the development of the evolution. With the help of the penalty function and other improvements, the new algorithm prevents local convergence and iteration wandering fluctuations. Typical instances are used to evaluate the optimizing performance of this new method; and the result shows that it can deal with constrained optimization problems well.

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