Variation Operators for Grouping Genetic Algorithms: A Review

Ramos-Figueroa, Octavio; Quiróz-Castellanos, Marcela; Mezura-Montes, Efrén; Kharel, Rupak

doi:10.1016/j.swevo.2020.100796

Cited by 36 publications

(24 citation statements)

References 67 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Grouping problems are problems that group or assign items, considering their objective and constraints (Ramos-Figueroa et al, 2021). More formally, they are to partition a set 𝑉𝑉 of 𝑛𝑛 items into 𝐷𝐷 mutually disjoint subsets 𝐺𝐺 𝑖𝑖 (𝑖𝑖 = 1,2, … , 𝐷𝐷) in such a way that their objective function is maximized or minimized.…”

Section: Grouping Genetic Algorithmmentioning

confidence: 99%

Genetic algorithm for scheduling of parcel delivery by drones

Hazama

Iima

Karuno

et al. 2021

JAMDSM

View full text Add to dashboard Cite

In recent years, efficient logistics has become indispensable, and using unmanned aerial vehicles (UAVs) or drones is promising for considerably reducing the cost and time required for parcel delivery. This paper addresses a parcel delivery scheduling problem. In this problem, a truck loaded with drones and parcels leaves a distribution center and stops at some points on a fixed route. At each point, the drones take off and deliver parcels to customers. We define this problem as finding the assignment of customers to both the drones and their takeoff points. Then, we propose a genetic algorithm (GA) for finding a near-optimal solution in a short time. In the proposed GA, a solution is represented using sets of customers assigned to the takeoff points, and a heuristic rule determines the assignment to the drones. The crossover operation enables offspring to inherit the customer sets. Experimental results show that the proposed GA can successfully find an optimal or a near-optimal solution faster than an integer programming solver for almost all instances. In addition, it significantly outperforms other GAs using a different crossover.

show abstract

Section: Grouping Genetic Algorithmmentioning

confidence: 99%

Genetic algorithm for scheduling of parcel delivery by drones

Hazama

Iima

Karuno

et al. 2021

JAMDSM

View full text Add to dashboard Cite

show abstract

“…The GGA was designed in 1992 by Falkenauer [35] and is an extension to the traditional GA with the difference of using a group-based solutions representation scheme and variation operators working together with such solution encoding. Ramos-Figueroa et al [36] remark that the encoding of a grouping problem solution into a chromosome is a key issue for obtaining good GGA performance; the authors also comment on the importance of integrating crossover and mutation operators adapted to work at the group level. They present a survey of different variation operators designed to work with GGAs that use different types of encoding, as well as their advantages to solve grouping problems.…”

Section: Grouping Genetic Algorithmsmentioning

confidence: 99%

Variable Decomposition for Large-Scale Constrained Optimization Problems Using a Grouping Genetic Algorithm

Carmona-Arroyo

Quiróz-Castellanos

Mezura-Montes

2022

MCA

Self Cite

View full text Add to dashboard Cite

Several real optimization problems are very difficult, and their optimal solutions cannot be found with a traditional method. Moreover, for some of these problems, the large number of decision variables is a major contributing factor to their complexity; they are known as Large-Scale Optimization Problems, and various strategies have been proposed to deal with them. One of the most popular tools is called Cooperative Co-Evolution, which works through a decomposition of the decision variables into smaller subproblems or variables subgroups, which are optimized separately and cooperate to finally create a complete solution of the original problem. This kind of decomposition can be handled as a combinatorial optimization problem where we want to group variables that interact with each other. In this work, we propose a Grouping Genetic Algorithm to optimize the variable decomposition by reducing their interaction. Although the Cooperative Co-Evolution approach is widely used to deal with unconstrained optimization problems, there are few works related to constrained problems. Therefore, our experiments were performed on a test benchmark of 18 constrained functions under 100, 500, and 1000 variables. The results obtained indicate that a Grouping Genetic Algorithm is an appropriate tool to optimize the variable decomposition for Large-Scale Constrained Optimization Problems, outperforming the decomposition obtained by a state-of-the-art genetic algorithm.

show abstract

“… 37 The main characteristic of the genetic algorithm is to operate the structure object directly, 38 and it has certain advantages in solving combinatorial optimization problems. 39 …”

Section: Introductionmentioning

confidence: 99%

“…In a recent article, the authors mentioned that it is necessary to analyze the correlation between the ECU signal and the DCU signal to derive a factor in the future, which also shows the importance of close cooperation between EGR and SCR to the engine . The main characteristic of the genetic algorithm is to operate the structure object directly, and it has certain advantages in solving combinatorial optimization problems …”

Section: Introductionmentioning

confidence: 99%

Effect of Exhaust Gas Recirculation Combined with Selective Catalytic Reduction on NO_x Emission Characteristics and Their Matching Optimization of a Heavy-Duty Diesel Engine

et al. 2022

View full text Add to dashboard Cite

Exhaust gas recirculation (EGR) and selective catalytic reduction (SCR) have become important technologies to reduce the NO x emission of heavy-duty diesel engines and meet the increasingly stringent emission regulations. This paper studied the effect of EGR combined with SCR on the NO x emission characteristics of a heavy-duty diesel engine based on the engine bench test. The results showed that the NO reduction rate of EGR-coupled SCR increased with the increase of engine load, and the effect was no longer significant when the NO reduction rate exceeded a certain limit under the same working conditions. EGR combined with SCR has little effect on NO 2 emission reduction, and the increase of engine speed can significantly improve the efficiency of the NO 2 reduction rate at 75 and 100% load. 25% opening of the EGR valve (OEV) and 50% OEV have very similar effects on the NO x reduction rate when the engine speed is at a low level. Compared with low engine speeds, increased OEV or ammonia NO x molar ratio (ANR) had a more obvious effect on the NO x reduction rate at high engine speeds. SCR combined with low valve-opening EGR had a more significant effect on the NO x reduction rate. The increase of OEV led to the increase of fuel consumption rate, but the effect on the fuel consumption rate decreased gradually with the increase of diesel engine speed. Meanwhile, this study optimized the matching relationship between OEV and ANR based on the data of the genetic algorithm, which provides a theoretical research method and application basis for diesel engine-matching of EGR and SCR.

show abstract

Variation Operators for Grouping Genetic Algorithms: A Review

Cited by 36 publications

References 67 publications

Genetic algorithm for scheduling of parcel delivery by drones

Genetic algorithm for scheduling of parcel delivery by drones

Variable Decomposition for Large-Scale Constrained Optimization Problems Using a Grouping Genetic Algorithm

Effect of Exhaust Gas Recirculation Combined with Selective Catalytic Reduction on NO_x Emission Characteristics and Their Matching Optimization of a Heavy-Duty Diesel Engine

Contact Info

Product

Resources

About

Variation Operators for Grouping Genetic Algorithms: A Review

Cited by 36 publications

References 67 publications

Genetic algorithm for scheduling of parcel delivery by drones

Genetic algorithm for scheduling of parcel delivery by drones

Variable Decomposition for Large-Scale Constrained Optimization Problems Using a Grouping Genetic Algorithm

Effect of Exhaust Gas Recirculation Combined with Selective Catalytic Reduction on NOx Emission Characteristics and Their Matching Optimization of a Heavy-Duty Diesel Engine

Contact Info

Product

Resources

About

Effect of Exhaust Gas Recirculation Combined with Selective Catalytic Reduction on NO_x Emission Characteristics and Their Matching Optimization of a Heavy-Duty Diesel Engine