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    A Self-adaptive Differential Evolution Algorithm for Solving Optimization Problems
    (2022-01-01)
    Farda, Irfan
    ;
    Thammano, Arit
    This research proposes a novel self-adaptive differential evolution algorithm for solving continuous optimization problems. This paper focuses on redesiging the self-adaptive strategy for the mutation parameters. The new mutation parameters adjust themselves to the current situation of the algorithm. When the search is stagnant, the first mutation parameter that scales the difference between the best vector and the target vector will be increased. In contrast, the second mutation parameter that scales the difference between two random target vectors will be decreased. On the other hand, when the search progresses well towards the global optimum, the algorithm will enhance the search of the surrounding space by doing the opposite of the above actions. The performance of the proposed self-adaptive differential evolution algorithm was evaluated and compared with the classic differential evolution algorithm on 7 benchmark functions. The experimental results showed that the proposed algorithm converged much faster than the classic differential evolution algorithm on all benchmark functions.
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    EXIT Chart Analysis of Protograph-based LDPC Codes for Unequal Power Two-User Gaussian Multiple Access Channel
    (2020-07-01)
    Khittiwitchayakul, Sirawit
    ;
    Phakphisut, Watid
    ;
    Supnithi, Pornchai
    ;
    Min Myint, Lin Min
    In this work, we analyze the theoretical performances of protograph-based LDPC codes for unequal power two-user Gaussian multiple access channel (GMAC) by using Extrinsic information transfer (EXIT) chart. We show that the decoding performances of LDPC codes for two-users are improved significantly when the transmitted power of user increases. Moreover, we present the design of protograph structures of the LDPC codes for unequal power two-user GMAC by using the differential evolution (DE). The results show that the designed LDPC codes outperform the traditional LDPC codes.
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    A self-adaptive differential evolution algorithm for continuous optimization problems
    (2014-09-01)
    Jitkongchuen, Duangjai
    ;
    Thammano, Arit
    This paper proposes a new self-adaptive differential evolution algorithm (DE) for continuous optimization problems. The proposed self-adaptive differential evolution algorithm extends the concept of the DE/current-to-best/1 mutation strategy to allow the adaptation of the mutation parameters. The control parameters in the mutation operation are gradually self-adapted according to the feedback from the evolutionary search. Moreover, the proposed differential evolution algorithm also consists of a new local search based on the krill herd algorithm. In this study, the proposed algorithm has been evaluated and compared with the traditional DE algorithm and two other adaptive DE algorithms. The experimental results on 21 benchmark problems show that the proposed algorithm is very effective in solving complex optimization problems.
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    Hybrid PSO-DE for solving the economic dispatch problem with generator constraints
    (2010-05-28)
    Khamsawang, S.
    ;
    Wannakarn, P.
    ;
    Jiriwibhakorn, S.
    This paper proposes an improved approach based on conventional particle swarm optimization (PSO) for solving an economic dispatch(ED) problem with considering the generator constraints. The mutation operators of the differential evolution (DE) are used for improving diversity exploration of PSO, which called hybrid particle swarm optimization - differential evolution (PSO-DE). The mutation operators are activated if velocity values of PSO nearly to zero or violated from the boundaries. Four scenarios of mutation operators are implemented for PSO-DE. The simulation results of all scenarios of the PSO-DE outperform over the PSO and other existing approaches which appeared in literatures. ©2010 IEEE.