NDLI: An agent-based memetic algorithm (AMA) for solving constrained optimazation problems

Content Provider	IEEE Xplore Digital Library
Author	Ullah, A.S.S. Sarker, R. Cornforth, D. Lokan, C.
Copyright Year	2007
Description	Author affiliation: Univ. of New South Wales at the Australian Defence Force Acad., Canberra (Ullah, A.S.S.; Sarker, R.; Cornforth, D.; Lokan, C.)
Abstract	In recent years, memetic algorithms (MAs) have been proposed to enhance the performance of evolutionary algorithms by incorporating local search techniques with evolutionary algorithms' global search ability, and applied successfully to solve different type of optimization problems. This paper proposes a new memetic algorithm and then introduces an agent-based memetic algorithm (AMA), for the first time, to further enhance the ability of MA in solving constrained optimization problems. In a lattice-like environment, each of the agents represents a candidate solution of the problem. The agents are able to sense and act on the society, and their performances i.e. fitness of the solution improves through co-evolutionary adaptation of society with the individual learning of the agents. The proposed algorithm is tested on 13 benchmark problems and the experimental results show promising performance.
Starting Page	999
Ending Page	1006
File Size	251797
Page Count	8
File Format	PDF
ISBN	9781424413393
DOI	10.1109/CEC.2007.4424579
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-09-25
Publisher Place	Singapore
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Evolutionary computation constrained optimization Memetic algorithms evolutionary algorithms genetic algorithms agent-based systems nonlinear programming
Content Type	Text
Resource Type	Article

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