Improved Discrete Differential Evolution Algorithm in Solving Quadratic Assignment Problem for best Solutions

Hameed, Asaad Shakir; Aboobaider, Burhanuddin Mohd; Choon, Ngo Hea; Mutar, Modhi Lafta

doi:10.14569/ijacsa.2018.091261

Cited by 5 publications

(5 citation statements)

References 15 publications

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“…In this context, crossover operator plays an efcient role in the algorithm to achieve the balance between the convergence speed and population diversity [15]. Recently, an efcient crossover operator named uniform like crossover (ULX) applied by the study [16] for the DDE algorithm to solve the QAP.…”

Section: Related Workmentioning

confidence: 99%

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Appling the Roulette Wheel Selection Approach to Address the Issues of Premature Convergence and Stagnation in the Discrete Differential Evolution Algorithm

Hameed

Alrikabi

Abdul-Razaq

et al. 2023

Applied Computational Intelligence and Soft Computing

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The discrete differential evolution (DDE) algorithm is an evolutionary algorithm (EA) that has effectively solved challenging optimization problems. However, like many other EAs, it still faces problems such as premature convergence and stagnation during the iterative process. To address these concerns in the DDE algorithm, this work aims to achieve the following objectives: (i) investigate the causes of premature convergence and stagnation in the DDE algorithm; (ii) propose techniques to prevent premature convergence and stagnation in DDE, including a quantitative measurement of premature convergence based on the level of mismatching between the population solutions and then divide the population into individual groups based on the level of mismatching between the population solutions and the best solution; and applying the roulette wheel selection (RWS) approach to determine whether a higher degree of nonmatching is more suitable for choosing a population of separate groups to be able to produce a new solution with more options to prevent the occurrence of premature convergence; (iii) evaluate the effectiveness of the proposed techniques through employing the DDE algorithm to solve the quadratic assignment problem (QAP) as a standard to evaluate our results and their effect on avoiding premature convergence and stagnation issues, which led to the enhancement of the algorithm’s accuracy. Our comparative study based on the statistical analysis shows that the DDE algorithm that uses the proposed techniques is more efficient than the traditional DDE algorithm and the state-of-the-art methods.

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Section: Related Workmentioning

confidence: 99%

“…Recently, the authors in the study [16] utilized ULX for the DDE algorithm to solve the QAP. However, there are many crossover operators suggested for the QAP in other algorithms such as the genetic algorithm [38].…”

Section: Limitations Of the Studymentioning

confidence: 99%

Appling the Roulette Wheel Selection Approach to Address the Issues of Premature Convergence and Stagnation in the Discrete Differential Evolution Algorithm

Hameed

Alrikabi

Abdul-Razaq

et al. 2023

Applied Computational Intelligence and Soft Computing

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“…Ketika ukuran masalah QAP meningkat, menjadi tidak mungkin untuk menyelesaikan masalah dalam waktu polinomial. Beberapa masalah praktis seperti tata letak rumah sakit dan kampus, alokasi gerbang ke pesawat di bandara, dan masalah kabel backboard listrik dapat dimodelkan sebagai QAP [3].…”

Section: -3385 (Print)unclassified

Komparasi Genetic Algorithm dan Firefly Algorithm pada Permasalahan Quadratic-Assignment-Problem (QAP)

Novianti,

Anjani

2022

shift

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Quadratic Assignment Problem (QAP) is the problem of determining the location of several facilities to a certain number of places to minimize the cost of moving. This displacement considers the flow of material or people movement and the distance between locations. Many studies have developed several efficient methods for solving QAP, one of which is metaheuristics. In this paper, the metaheuristic methods used are Genetic Algorithm (GA) and Firefly Algorithm (FA). The data used is taken from the Nugent data on QAPLIB with 12x12, 16x16, 20x20, and 30x30 matrices. This study proves that FA is an algorithm that produces the most optimal results with indicators in the test getting the best results (12x12 and 16x16), having better average and deviation values and faster computational time than GA. Meanwhile, for comparing the results obtained by the Nugent, FA also has more optimal results on the 12x12 and 16x16 matrices. However, the 20x20 and 30x30 GA metrics get more optimal results.

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“…both and convex relaxation achieve good sparse coefficients as compared to other optimization algorithms. The resulting BP is more accurate than the greedy technique with a slight increase in processing time [11], [12].…”

Section: Introductionmentioning

confidence: 99%

Time complexity in rejang language stemming

et al. 2022

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Stemming is the process of separating the root word from an affixed word in a sentence by separating the base word and affixes which can consist of prefixes (prefixes), insertions (infixes), and suffixes (suffixes). Between one language and another, there are differences in the algorithm, especially the stemming process, in morphology. The time complexity of the Rejang algorithm is determined based on the affix group. To find out the time complexity of the stemming algorithm in the Rejang language using the method of making a digital word dictionary of the Rejang language, studying and analyzing the morphology of the Rejang language, making the Rejang language stemming algorithm based on the results of the Rejang language morphology analysis, analyzing the algorithm's performance and calculating the time complexity of the stemming results. The result of this research is to produce an efficient and effective Rejang Language stemming algorithm, where efficiency is indicated by the algorithm's time complexity of O(log n), and the effectiveness is shown from the results of accuracy of 99% against the test of 9000 affixed words. This accuracy value indicates that the over stemming and under stemming processes are 1%. Test results on 15 text documents with an average stemming failure rate of 1%.

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Improved Discrete Differential Evolution Algorithm in Solving Quadratic Assignment Problem for best Solutions

Cited by 5 publications

References 15 publications

Appling the Roulette Wheel Selection Approach to Address the Issues of Premature Convergence and Stagnation in the Discrete Differential Evolution Algorithm

Appling the Roulette Wheel Selection Approach to Address the Issues of Premature Convergence and Stagnation in the Discrete Differential Evolution Algorithm

Komparasi Genetic Algorithm dan Firefly Algorithm pada Permasalahan Quadratic-Assignment-Problem (QAP)

Time complexity in rejang language stemming

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