Paper Info
Title
Constrained evolutionary optimization by means of (μ + λ)-differential evolution and improved adaptive trade-off model.
Abstract
This paper proposes a (μ + λ)-differential evolution and an improved adaptive trade-off model for solving constrained optimization problems. The proposed (μ + λ)-differential evolution adopts three mutation strategies (i.e., rand/1 strategy, current-to-best/1 strategy, and rand/2 strategy) and binomial crossover to generate the offspring population. Moreover, the current-to-best/1 strategy has been improved in this paper to further enhance the global exploration ability by exploiting the feasibility proportion of the last population. Additionally, the improved adaptive trade-off model includes three main situations: the infeasible situation, the semi-feasible situation, and the feasible situation. In each situation, a constraint-handling mechanism is designed based on the characteristics of the current population. By combining the (μ + λ)-differential evolution with the improved adaptive trade-off model, a generic method named (μ + λ)-constrained differential evolution ((μ + λ)-CDE) is developed. The (μ + λ)-CDE is utilized to solve 24 well-known benchmark test functions provided for the special session on constrained real-parameter optimization of the 2006 IEEE Congress on Evolutionary Computation (CEC2006). Experimental results suggest that the (μ + λ)-CDE is very promising for constrained optimization, since it can reach the best known solutions for 23 test functions and is able to successfully solve 21 test functions in all runs. Moreover, in this paper, a self-adaptive version of (μ + λ)-CDE is proposed which is the most competitive algorithm so far among the CEC2006 entries.
Year
DOI
Venue
2011
10.1162/EVCO_a_00024
Evolutionary Computation
Keywords
Field
DocType
Constrained optimization problems,adaptive trade-off model,constraint-handling technique,differential evolution,evolutionary algorithm
Population,Mathematical optimization,Crossover,Evolutionary algorithm,Binomial,Differential evolution,Artificial intelligence,IEEE Congress on Evolutionary Computation,Constrained optimization problem,Machine learning,Mathematics,Constrained optimization
Journal
Volume
Issue
ISSN
19
2
1530-9304
Citations 
PageRank 
References 
19
0.65
25
Authors
2
Name
Order
Citations
PageRank
Yong Wang159625.79
Zixing Cai2152566.96