A micro-genetic algorithm for multi-objective scheduling of a real world pipeline network

Ribas, Paulo Cesar; Yamamoto, Lia; Polli, Helton Luis; Arruda, Lúcia Valéria Ramos de; Neves-Jr, Flávio

doi:10.1016/j.engappai.2012.09.020

Cited by 26 publications

(16 citation statements)

References 16 publications

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“…Over the years, other authors have adopted micro-genetic algorithms for solving a variety of problems (see for example [5,30,46,47,61,72,76,77]). Additionally, the use of very small population sizes has also been attempted with other bio-inspired metaheuristics, such as particle swarm optimization (see [22]).…”

Section: Use Of Very Small Population Sizesmentioning

confidence: 99%

Multi-objective Evolutionary Algorithms in Real-World Applications: Some Recent Results and Current Challenges

Coello

2014

Computational Methods in Applied Sciences

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This chapter provides a short overview of the most significant research work that has been conducted regarding the solution of computationally expensive multi-objective optimization problems. The approaches that are briefly discussed include problem approximation, function approximation (i.e., surrogates) and evolutionary approximation (i.e., clustering and fitness inheritance). Additionally, the use of alternative approaches such as cultural algorithms, small population sizes and hybrids that use a few solutions (generated with optimizers that sacrifice diversity for the sake of a faster convergence) to reconstruct the Pareto front with powerful local search engines are also briefly discussed. In the final part of the chapter, some topics that (from the author's perspective) deserve more research, are provided.

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Section: Use Of Very Small Population Sizesmentioning

confidence: 99%

Multi-objective Evolutionary Algorithms in Real-World Applications: Some Recent Results and Current Challenges

Coello

2014

Computational Methods in Applied Sciences

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“…Figure 4 presents the general structure of the GA which was developed to address this task. The GA is characterized by small populations which can lead to achieving faster convergence with less storing memory [35,36]. In this case, the individuals of the population of the GA consist only of pairs of values ( , ) that can define the lower and upper limits of a range that may contain the value that can lead to a total minimum distance on a single execution of the CKMC algorithm.…”

Section: Gamentioning

confidence: 99%

Extended GRASP-Capacitated K-Means Clustering Algorithm to Establish Humanitarian Support Centers in Large Regions at Risk in Mexico

Caballero‐Morales

Barojas-Payán

Sánchez‐Partida

et al. 2018

Journal of Optimization

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Mexico is located within the so-called Fire Belt which makes it susceptible to earthquakes. In fact, two-thirds of the Mexican territory have a significant seismic risk. On the other hand, the country’s location in the tropical zone makes it susceptible to hurricanes which are generated in both the Pacific and Atlantic Oceans. Due to these situations, each year many communities are affected by diverse natural disasters in Mexico and efficient logistic systems are required to provide prompt support. This work is aimed at providing an efficient metaheuristic to determine the most appropriate location for support centers in the State of Veracruz, which is one of the most affected regions in Mexico. The metaheuristic is based on the K-Means Clustering (KMC) algorithm which is extended to integrate (a) the associated capacity restrictions of the support centers, (b) a micro Genetic Algorithm μGA to estimate a search interval for the most suitable number of support centers, (c) variable number of assigned elements to centers in order to add flexibility to the assignation task, and (d) random-based decision model to further improve the final assignments. These extensions on the KMC algorithm led to the GRASP-Capacitated K-Means Clustering (GRASP-CKMC) algorithm which was able to provide very suitable solutions for the establishment of 260 support centers for 3837 communities at risk in Veracruz, Mexico. Validation of the GRASP-CKMC algorithm was performed with well-known test instances and metaheuristics. The validation supported its suitability as alternative to standard metaheuristics such as Capacitated K-Means (CKM), Genetic Algorithms (GA), and Variable Neighborhood Search (VNS).

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“…Under no external pressure, the author judged whether the pressure of a node could satisfy the pressure required for the other node. If the result was positive, the reliability of each connection was calculated and the cost was computed by equation (5). Then, the connection mode with higher reliability and lower cost was identified.…”

Section: Model Optimizationmentioning

confidence: 99%

“…The research focus of fluid pipe networks lies in reducing cost and enhancing efficiency through structural optimization. For instance, Afshar, and Ribas et al, optimized the pipe layout of water supply pipe network by the genetic algorithm [4][5]. Sinha and Pandey, and Carrión et al, put forward reliability evaluation methods after examining the structural performance of different fluid pipe networks [6][7].…”

Section: Introductionmentioning

confidence: 99%

Establishment and optimization of fluid pipe network models based on topological analysis algorithm

Cheng¹,

Zhang²,

Dan³

2018

IJHT

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Recent years has seen the proliferation of various fluid pipe networks in our living and working environments. With the growth in size, complexity and scale, fluid pipe networks are faced with numerous problems like pipe collision and bursting. To prevent these problems, ensure the operation efficiency and save cost, this paper probes into the structure and performance of two fluid pipe networks that backs up each other. Based on topological analysis algorithm, the two fluid pipe networks were converted into topological graphs, and created topological models involving pipe elements and nodes. Through optimization of the models, the author proposed the two-pipe connection mode for the two pipe networks. The optimization strategy was then verified through a simulation application in a tree-shaped water injection system. The results show that our strategy can effectively optimize the two-pipe connection, ensure the operation efficiency and save the network cost. The research findings provide a good reference to the design and optimization of fluid pipe networks in China.

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A micro-genetic algorithm for multi-objective scheduling of a real world pipeline network

Cited by 26 publications

References 16 publications

Multi-objective Evolutionary Algorithms in Real-World Applications: Some Recent Results and Current Challenges

Multi-objective Evolutionary Algorithms in Real-World Applications: Some Recent Results and Current Challenges

Extended GRASP-Capacitated K-Means Clustering Algorithm to Establish Humanitarian Support Centers in Large Regions at Risk in Mexico

Establishment and optimization of fluid pipe network models based on topological analysis algorithm

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