NDLI: A New Evolutionary Algorithm Based on Simplex Crossover and PSO Mutation for Constrained Optimization Problems

Content Provider	IEEE Xplore Digital Library
Author	Yibo Hu
Copyright Year	2010
Abstract	A new approach is presented to handle constraints optimization using evolutionary algorithms in this paper. First, we present a specific varying fitness function technique, this technique incorporates the problem’s constraints into the fitness function in a dynamic way. The resulting varying fitness function facilitates the EA search. On one hand, The new fitness function without any parameters can properly evaluate not only feasible solution, but also infeasible one, on other hand, the information of the best solution in the current population is also concerned in fitness function, which make search more efficient. Meanwhile, a new crossover operator based on simplex crossover operator and a new PSO mutation operator is also proposed, and both the operators utilize the information of good individuals in the current populations so they can produce high quality offspring. Based on these, a new evolutionary algorithm for constrained optimization problems is proposed. The simulations are made on five widely used benchmark problems, and the results indicate the proposed algorithm is effective
Starting Page	142
Ending Page	146
File Size	415157
Page Count	5
File Format	PDF
ISBN	9781424491148
DOI	10.1109/CIS.2010.38
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-12-11
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Algorithm design and analysis PSO mutation simplex crossover Evolutionary computation Transforms varying fitness function Benchmark testing Search problems Approximation algorithms Optimization
Content Type	Text
Resource Type	Article

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