NDLI: Immune-inspired evolutionary algorithm for constrained optimization

Content Provider	IEEE Xplore Digital Library
Author	Weiwei Zhang Yen, G.G.
Copyright Year	2012
Description	Author affiliation: Department of Computer Science, Chongqing University, 40044, CHINA (Weiwei Zhang) \|\| School of Electrical and Computer Engineering, Oklahoma State University, Stillwater, 74078, USA (Yen, G.G.)
Abstract	This paper proposes an artificial immune system based algorithm for solving constrained optimization problems, inspired by the principle of the vertebrate immune system. The analogy between the mechanism of vertebrate immune system and constrained optimization formulation is first given. The population is divided into two groups- feasible individuals and infeasible individuals. The infeasible individuals are viewed as the inactivated immune cells approaching the feasible regions by decreasing the constraint violations whereas the feasible individuals are treated as activated immune cells searching for the optima. The interaction between them through the extracted directional information is facilitated mimicking the functionality of T cells. This mechanism not only encourages infeasible individuals approaching feasibility regions, but facilitates exploring the boundary between the feasible and infeasible regions in which optima are often located. This approach is validated and performance is quantified by the benchmark functions used in related researches through statistical means with those of the state-of-the-art from various branches of evolutionary computation paradigms. The performance obtained is fairly competitive and in some cases even better.
Starting Page	1
Ending Page	8
File Size	895342
Page Count	8
File Format	PDF
ISBN	9781467315104
e-ISBN	9781467315098
e-ISBN	9781467315081
DOI	10.1109/CEC.2012.6256421
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-06-10
Publisher Place	Australia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Immune system Optimization Algorithm design and analysis Chemicals Cloning Benchmark testing Adaptive systems constraint handling Artificial immune system constrained optimization
Content Type	Text
Resource Type	Article

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