Novel hybrid discrete differential evolution algorithm for the multi-stage multi-purpose batch plant scheduling problem

Han, Yuxin; Yan, Xueli; Gu, Xingsheng

doi:10.1016/j.asoc.2021.108262

Cited by 5 publications

(2 citation statements)

References 48 publications

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“…Additionally, evaluation of the method's applicability in various settings and problem domains is also under process. Han et al (Yuxin et al, 2022) designed a hybrid discretecontinuous evolutionary strategy using a two-line encoding system and mutation techniques. Applying this method to scheduling concerns in massive multistage batch facilities increased efficiency by a significant margin (96.23%).…”

Section: Literature Surveymentioning

confidence: 99%

Improvement of Computer Adaptive Multistage Testing Algorithm Based on Adaptive Genetic Algorithm

Zhang

2024

International Journal of Intelligent Information Technologies

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Multistage testing (MST) is a portion of computational adaptive testing that adapts assessment structure at the sublevel rather than the component level. The goal of the MST algorithm is to identify bugs in computer programming, and there is a significant cost to utilising MST due to its decreased versatility during software development and maintenance. The efficiency of most algorithms drastically reduces for adaptive MST with complex feasible regions, while some modern algorithms function well while tackling computerised MST with a basic practicable range. The study offers an automated Adaptive Multistage Testing algorithm based on Adaptive Genetic Algorithm (AMST-AGA) for optimisation and scalability problems, in which constraints are successively introduced and dealt with at various evolutionary phases. In this paper, many test cases will aid in finding bugs and meeting completeness goals. Each time test cases are created, these testing scenarios must continue to pass.

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Section: Literature Surveymentioning

confidence: 99%

Improvement of Computer Adaptive Multistage Testing Algorithm Based on Adaptive Genetic Algorithm

Zhang

2024

International Journal of Intelligent Information Technologies

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“…As pointed out in [22], among the existing intelligent approaches, differential evolution (DE) [14] is one of the most popular population-based stochastic optimizers, and has been proven to be more efficient and robust on various problems. Due to its simplicity and simple implementation, DE always attracts more attention from researchers, and numerous DE variants have been put forward to strengthen its performance [29][30][31][32][33][34] and/or solve special practical problems [35][36][37][38][39]. For example, by dividing a population into multiple swarms and randomly selecting the solutions with better fitness values in each swarm to conduct mutations, Wang et al [29] developed a novel adaptive DE algorithm.…”

Section: Introductionmentioning

confidence: 99%

A Novel Discrete Differential Evolution with Varying Variables for the Deficiency Number of Mahjong Hand

Yan

2023

Mathematics

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The deficiency number of one hand, i.e., the number of tiles needed to change in order to win, is an important factor in the game Mahjong, and plays a significant role in the development of artificial intelligence (AI) for Mahjong. However, it is often difficult to compute due to the large amount of possible combinations of tiles. In this paper, a novel discrete differential evolution (DE) algorithm is presented to calculate the deficiency number of the tiles. In detail, to decrease the difficulty of computing the deficiency number, some pretreatment mechanisms are first put forward to convert it into a simple combinatorial optimization problem with varying variables by changing its search space. Subsequently, by means of the superior framework of DE, a novel discrete DE algorithm is specially developed for the simplified problem through devising proper initialization, a mapping solution method, a repairing solution technique, a fitness evaluation approach, and mutation and crossover operations. Finally, several experiments are designed and conducted to evaluate the performance of the proposed algorithm by comparing it with the tree search algorithm and three other kinds of metaheuristic methods on a large number of various test cases. Experimental results indicate that the proposed algorithm is efficient and promising.

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Multi-UAV task allocation based on GCN-inspired binary stochastic L-BFGS

Zhang,

et al. 2023

Computer Communications

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Novel hybrid discrete differential evolution algorithm for the multi-stage multi-purpose batch plant scheduling problem

Cited by 5 publications

References 48 publications

Improvement of Computer Adaptive Multistage Testing Algorithm Based on Adaptive Genetic Algorithm

Improvement of Computer Adaptive Multistage Testing Algorithm Based on Adaptive Genetic Algorithm

A Novel Discrete Differential Evolution with Varying Variables for the Deficiency Number of Mahjong Hand

Multi-UAV task allocation based on GCN-inspired binary stochastic L-BFGS

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