A symbiosis between population based incremental learning and LP-relaxation based parallel genetic algorithm for solving integer linear programming models

Fallah, Mohammad K; Fazlali, Mahmood; Daneshtalab, Masoud

doi:10.1007/s00607-021-01004-x

Cited by 9 publications

(5 citation statements)

References 23 publications

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“…The genetic algorithm (GA) is an effective domain-independent search technique that was motivated by Darwin’s idea . Because the GA is population-based, a different answer is produced in each iteration . The main concept of this natural selection algorithm is that stronger people survive and pass on their strong characteristics to their children .…”

Section: Methodsmentioning

confidence: 99%

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Determination of the Rate of Penetration by Robust Machine Learning Algorithms Based on Drilling Parameters

Alavi Nezhad Khalil Abad,

Hazbeh,

Rajabi

et al. 2023

ACS Omega

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Underground resources, particularly hydrocarbons, are critical assets that promote economic development on a global scale. Drilling activities are necessary for the extraction and recovery of subsurface energy resources, and the rate of penetration (ROP) is one of the most important drilling parameters. This study forecasts the ROP using drilling data from three Iranian wells and hybrid LSSVM-GA/PSO algorithms. These algorithms were chosen due to their ability to reduce noise and increase accuracy despite the high level of noise present in the data. The study results revealed that the LSSVM-PSO method has an accuracy of roughly 97% and is more precise than the LSSVM-GA technique. The LSSVM-PSO algorithm also demonstrated improved accuracy in test data, with RMSE = 1.92 and R 2 = 0.9516. Furthermore, it was observed that the accuracy of the LSSVM-PSO model improves and degrades after the 50th iteration, whereas the accuracy of the LSSVM-GA algorithm remains constant after the 10th iteration. Notably, these algorithms are advantageous in decreasing data noise for drilling data.

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Section: Methodsmentioning

confidence: 99%

“… 53 Because the GA is population-based, a different answer is produced in each iteration. 54 The main concept of this natural selection algorithm is that stronger people survive and pass on their strong characteristics to their children. 55 This algorithm has two genetic operators: integration and mutation.…”

Section: Methodsmentioning

confidence: 99%

Determination of the Rate of Penetration by Robust Machine Learning Algorithms Based on Drilling Parameters

Alavi Nezhad Khalil Abad,

Hazbeh,

Rajabi

et al. 2023

ACS Omega

View full text Add to dashboard Cite

show abstract

“…Step 1. For each combination of even numbers λ 1 , λ 2 , λ 3 ∈ N + with λ 1 + λ 2 + λ 3 = 100 (1176 ILP combinations), solve (with Mathematica v12.1) the ILP problem with Equation ( 9) as the objective function, using the restrictions of Equations ( 2)- (8). Solve the same problem but with λ 1 = λ 2 = λ 3 = 100/3 (the same weight for all three objective functions).…”

Section: Mathematical Modelmentioning

confidence: 99%

“…To solve a MOILP problem, scalarization methods, which, as their name indicates, turn such a problem into solving single-objective integer linear programming problems, play a crucial role in finding all or a subset of the non-dominated objective vectors. Note that a single-objective integer linear programming problem is known in the literature as an integer linear programming (ILP) problem [6][7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Optimization of the Cognitive Processes in a Virtual Classroom: A Multi-objective Integer Linear Programming Approach

et al. 2022

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A fundamental problem in the design of a classroom is to identify what characteristics it should have in order to optimize learning. This is a complex problem because learning is a construct related to several cognitive processes. The aim of this study is to maximize learning, represented by the processes of attention, memory, and preference, depending on six classroom parameters: height, width, color hue, color saturation, color temperature, and illuminance. Multi-objective integer linear programming with three objective functions and 56 binary variables was used to solve this optimization problem. Virtual reality tools were used to gather the data; novel software was used to create variations of virtual classrooms for a sample of 112 students. Using an interactive method, more than 4700 integer linear programming problems were optimally solved to obtain 13 efficient solutions to the multi-objective problem, which allowed the decision maker to analyze all the information and make a final choice. The results showed that achieving the best cognitive processing performance involves using different classroom configurations. The use of a multi-objective interactive approach is interesting because in human behavioral studies, it is important to consider the judgement of an expert in order to make decisions.

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“…In this paper, a more efficient optimisation strategy named artificial bee colony algorithm which is developed by Karaboga [24,25] is introduced. It is based on simulating the foraging behaviour of honey bee swarm, and the numerical comparisons demonstrated that the performance of ABC algorithm is competitive to other population-based algorithms with an advantage of employing fewer control parameters [26,27]. For overcoming the disadvantage of easily getting trapped in the local optimal when solving complex multimodal problems [28], different from combining it with other algorithms [29,30], an improved bee colony algorithm where the search is guided with the important indices of sub-region is proposed.…”

Section: Introductionmentioning

confidence: 99%

An improved bearing fault detection strategy based on artificial bee colony algorithm

Wang

Yue

et al. 2022

CAAI Trans on Intel Tech

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The operating state of bearing affects the performance of rotating machinery; thus, how to accurately extract features from the original vibration signals and recognise the faulty parts as early as possible is very critical. In this study, the one‐dimensional ternary model which has been proved to be an effective statistical method in feature selection is introduced and shapelet transformation is proposed to calculate the parameter of one‐dimensional ternary model that is usually selected by trial and error. Then XGBoost is used to recognise the faults from the obtained features, and artificial bee colony algorithm (ABC) is introduced to optimise the parameters of XGBoost. Moreover, for improving the performance of intelligent algorithm, an improved strategy where the evolution is guided by the probability that the optimal solution appears in certain solution space is proposed. The experimental results based on the failure vibration signal samples show that the average accuracy of fault signal recognition can reach 97%, which is much higher than the ones corresponding to traditional extraction strategies. And with the help of improved ABC algorithm, the performance of XGBoost classifier could be optimised; the accuracy could be improved from 97.02% to 98.60% compared with the traditional classification strategy.

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A symbiosis between population based incremental learning and LP-relaxation based parallel genetic algorithm for solving integer linear programming models

Cited by 9 publications

References 23 publications

Determination of the Rate of Penetration by Robust Machine Learning Algorithms Based on Drilling Parameters

Determination of the Rate of Penetration by Robust Machine Learning Algorithms Based on Drilling Parameters

Optimization of the Cognitive Processes in a Virtual Classroom: A Multi-objective Integer Linear Programming Approach

An improved bearing fault detection strategy based on artificial bee colony algorithm

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