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1

Adapting differential evolution algorithms for continuous optimization via greedy adjustment of control parameters

Leon M., Xiong N.

Journal of Artificial Intelligence and Soft Computing Research

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2016

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Vol. 6, No. 2

103--118

EN

Differential evolution (DE) presents a class of evolutionary and meta-heuristic techniques that have been applied successfully to solve many real-world problems. However, the performance of DE is significantly influenced by its control parameters such as scaling factor and crossover probability. This paper proposes a new adaptive DE algorithm by greedy adjustment of the control parameters during the running of DE. The basic idea is to perform greedy search for better parameter assignments in successive learning periods in the whole evolutionary process. Within each learning period, the current parameter assignment and its neighboring assignments are tested (used) in a number of times to acquire a reliable assessment of their suitability in the stochastic environment with DE operations. Subsequently the current assignment is updated with the best candidate identified from the neighborhood and the search then moves on to the next learning period. This greedy parameter adjustment method has been incorporated into basic DE, leading to a new DE algorithm termed as Greedy Adaptive Differential Evolution (GADE). GADE has been tested on 25 benchmark functions in comparison with five other DE variants. The results of evaluation demonstrate that GADE is strongly competitive: it obtained the best rank among the counterparts in terms of the summation of relative errors across the benchmark functions with a high dimensionality.

2

Synthesis and electrical properties of silver nanoplates for electronic applications

Xiong N., Li Z., Xie H., Zhao Y., Li M., Wang Y., Li J.

Materials Science Poland

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2015

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Vol. 33, No. 2

242--250

EN

In this paper, silver nanoplates of 100 to 500 nm size were synthesized by reduction of silver nitrate with N,Ndimethylformamide, using poly(vinylpyrolidone) as a surfactant and ferric chloride as a controlling agent, at 120 to 160 °C for 5 to 24 hours. The influence of the concentration of ferric chloride, the reaction temperature and reaction time on the morphology of the product has been investigated by transmission electron microscopy, scanning electron microscopy and UV-Vis spectroscopy. The results indicated that the products obtained at the low reaction temperature and short reaction time in the presence of FeCl3 in the reaction solution were in the form of silver nanoplates, whose morphology was mainly triangular and hexagonal. In addition, the size and thickness of the nanoplates increased with increasing of the FeCl3 concentration. At a high reaction temperature and long reaction time, the truncated triangle and hexagonal nanoplates were mainly produced. Furthermore, the sintering behavior of nanoplates was studied and the results showed that sintering of the silver nanoplates started at 180 °C, and a typical sintering behavior was observed at higher temperatures. The incorporation of the silver nanoplates into the polymer matrix with micro-sized silver flakes led to an increase in the matrix resistivity in almost all cases, especially at high fractions and low curing temperatures. The curing temperature had an influence on the resistivity of the conductive adhesives filled with micro-sized silver flakes and silver nanoplates due to sintering of the silver nanoplates.

3

Learning rule structures of fuzzy controllers based on genetic algorithm.

Xiong N.

Systems Science

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2000

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Vol. 26, nr 2

69-84

EN

One of the major difficulties in fuzzy control of complex processes is the "curse of dimensionality". For the sake of a reduced size of the knowledge base some rules with incomplete premise structures covering larger regions of input domain are often desirable. The paper presents a genetic algorithm based approach to searching for suitable antecedents under which specific fuzzy actions could be derived. The rule premises are coded in a flexible way allowing the presence as well as absence of an input variable in them, in combination with a certain class of input and output fuzzy sets. On the other hand, a consistency index is introduced to give a numerical evaluation of the coherence among individual rules. This index is incorporated into the fitness function of the genetic algorithm to search for a set of optimal rule premises yielding not only good control performances but also little conflict in the rule base. The effectiveness of our work is demonstrated through experiment results on an inverted pendulum.

4

Fuzzy tuning of PID controller based on genetic algorithm.

Xiong N.

Systems Science

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1999

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Vol. 25, nr 3

65-75

EN

PID controllers have found extensive industrial applications. The idea of online determination of the PID gains using fuzzy logic and reasoning was proposed with the purpose of acquiring better control performances then with using fixed PID parameters during the process. However, the detailed specification for such a fuzzy schedule is rather complicated and time consuming. Genetic algorithms are search algorithms based on natural section and have proved themselves superior to conventional optimization techniques in many cases. This paper develops a genetic-based approach to designing a fuzzy system for online schedule of PID parameters. The effectiveness of the proposed approach in learning to adjust PID parameters for improving control performances has been demonstrated through simulation tests on a fourth-order process.