A Thermodynamical Selection-Based Discrete Differential Evolution for the 0-1 Knapsack Problem

Guo, Zitao; Yue, Xuezhi; Zhang, Kejun; Wang, Shenwen; Wu, Zhijian

doi:10.3390/e16126263

Cited by 10 publications

(3 citation statements)

References 50 publications

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“…Also, many researchers work on modifications of EAs to improve their computational performance. In many recently published papers, we can find modifications of GA [22,23,115,116], GP [31,32,117,118], DE [48,49,119,120], ES [64,65], and EP [74,75]. An interesting domain of future research in EAs is also memetic algorithms.…”

Section: Summary and Future Trendsmentioning

confidence: 99%

Evolutionary algorithms and their applications to engineering problems

Słowik

Kwaśnicka

2020

Neural Comput & Applic

442

107

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The main focus of this paper is on the family of evolutionary algorithms and their real-life applications. We present the following algorithms: genetic algorithms, genetic programming, differential evolution, evolution strategies, and evolutionary programming. Each technique is presented in the pseudo-code form, which can be used for its easy implementation in any programming language. We present the main properties of each algorithm described in this paper. We also show many state-of-the-art practical applications and modifications of the early evolutionary methods. The open research issues are indicated for the family of evolutionary algorithms.

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Section: Summary and Future Trendsmentioning

confidence: 99%

Evolutionary algorithms and their applications to engineering problems

Słowik

Kwaśnicka

2020

Neural Comput & Applic

442

107

View full text Add to dashboard Cite

show abstract

“…Entropy measures uncertainty in a series of information units . [16][17][18] Solteiro et al 19 remark the use of Shannon entropy in several studies, and the capability to provide useful information about the diversity of the population and the entropy's strategy to preserve diversity. The state of the algorithms is revealed by the distance among populations and can be measured by the Shannon entropy; a smaller entropy represents a saturated space and that the diversity be reduced.…”

Section: Introductionmentioning

confidence: 99%

Chaotic genetic algorithm and the effects of entropy in performance optimization

Fuertes

Vargas

Alfaro

et al. 2019

Chaos: An Interdisciplinary Journal of Nonlinear Science

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This work proposes a new edge about the Chaotic Genetic Algorithm (CGA) and the importance of the entropy in the initial population. Inspired by chaos theory the CGA uses chaotic maps to modify the stochastic parameters of Genetic Algorithm (GA). The algorithm modifies the parameters of the initial population using chaotic series and then analyzes the entropy of such population. This strategy exhibits the relationship between entropy and performance optimization in complex search spaces. Our study includes the optimization of nine benchmark functions using eight different chaotic maps for each of the benchmark functions. The numerical experiment demonstrates a direct relation between entropy and performance of the algorithm.

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“…In [33], a soccer league competition algorithm was proposed. In [34], a thermondynamical selection-based discrete differential evolution method was presented. However, they still cost the high time complexity especially in [27], [28], [29], and [31].…”

Section: Introductionmentioning

confidence: 99%

Solving 0-1 knapsack problem by greedy degree and expectation efficiency

Wang

Huang

et al. 2016

Applied Soft Computing

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It is well known that 0-1 knapsack problem (KP01) plays an important role in both computing theory and many real life applications. Due to its NP-hardness, lots of impressive research work has been performed on many variants of the problem. Inspired by region partition of items, an effective hybrid algorithm based on greedy degree and expectation efficiency (GDEE) is presented in this paper. In the proposed algorithm, initially determinate items region, candidate items region and unknown items region are generated to direct the selection of items. A greedy degree model inspired by greedy strategy is devised to select some items as initially determinate region. Dynamic expectation efficiency strategy is designed and used to select some other items as candidate region, and the remaining items are regarded as unknown region. To obtain the final items to which the best profit corresponds, static expectation efficiency strategy is proposed whilst the parallel computing method is adopted to update the objective function value. Extensive numerical investigations based on a large number of instances are conducted. The proposed GDEE algorithm is evaluated against chemical reaction optimization algorithm and modified discrete shuffled frog leaping algorithm. The comparative results show that GDEE is much more effective in solving KP01 than other algorithms and that it is a promising tool for solving combinatorial optimization problems such as resource allocation and production scheduling.

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A Thermodynamical Selection-Based Discrete Differential Evolution for the 0-1 Knapsack Problem

Cited by 10 publications

References 50 publications

Evolutionary algorithms and their applications to engineering problems

Evolutionary algorithms and their applications to engineering problems

Chaotic genetic algorithm and the effects of entropy in performance optimization

Solving 0-1 knapsack problem by greedy degree and expectation efficiency

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