A novel ODV crossover operator-based genetic algorithms for traveling salesman problem

Paul, Peter V.; Ganeshkumar, C.; Dhavachelvan, P.; Baskaran, R.

doi:10.1007/s00500-020-04712-2

Cited by 14 publications

(3 citation statements)

References 28 publications

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Section: Literature Review On Agricultural Value Chain Actorsmentioning

confidence: 99%

“…They found immense advantages in resource sharing, cost minimizing and high yield with good quality. TongKe (2013) studied cloud computing and IoT in agriculture for automated control production systems and felt it could solve farmers' problems (Paul et al, 2020). Borah et al (2007) studied the texture feature estimation experiments for segregating images captured of eight distinct categories of cutting, tearing and curling tea using neural networks of multi-layer perceptron network for texture classifications.…”

Section: Farm Productionmentioning

confidence: 99%

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Artificial intelligence in agricultural value chain: review and future directions

Ganeshkumar

Jena

Sivakumar

et al. 2021

JADEE

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PurposeThis paper is a literature review on use of artificial intelligence (AI) among agricultural value chain (AVC) actors, and it brings out gaps in research in this area and provides directions for future research.Design/methodology/approachThe authors systematically collected literature from several databases covering 25 years (1994–2020). They classified literature based on AVC actors present in different stages of AVC. The literature was analysed using Nvivo 12 (qualitative software) for descriptive and content analysis.FindingsFifty percent of the reviewed studies were empirical, and 35% were conceptual. The review showed that AI adoption in AVC could increase agriculture income, enhance competitiveness and reduce cost. Among the AVC stages, AI research related to agricultural processing and consumer sector was very low compared to input, production and quality testing. Most AVC actors widely used deep learning algorithm of artificial neural networks in various aspects such as water resource management, yield prediction, price/demand forecasting, energy efficiency, optimalization of fertilizer/pesticide usage, crop planning, personalized advisement and predicting consumer behaviour.Research limitations/implicationsThe authors have considered only AI in the AVC, AI use in any other sector and not related to value chain actors were not included in the study.Originality/valueEarlier studies focussed on AI use in specific areas and actors in the AVC such as inputs, farming, processing, distribution and so on. There were no studies focussed on the entire AVC and the use of AI. This review has filled that literature gap.

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Section: Literature Review On Agricultural Value Chain Actorsmentioning

confidence: 99%

Section: Farm Productionmentioning

confidence: 99%

Artificial intelligence in agricultural value chain: review and future directions

Ganeshkumar

Jena

Sivakumar

et al. 2021

JADEE

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“…Another swarm intelligence approach called a discrete symbiotic organisms search (DSOS) algorithm for finding a near optimal solution for the TSP was proposed in [21]. A very good performance in solving TSP instances achieves the approach proposed in [22]. The authors propose a genetic algorithm with an effective ordered distance vector (ODV) crossover operator, based on the ODV matrix used at the population seeding stage.…”

Section: Introductionmentioning

confidence: 99%

Parallelized Swarm Intelligence Approach for Solving TSP and JSSP Problems

Jędrzejowicz

Wierzbowska

2020

Algorithms

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One of the possible approaches to solving difficult optimization problems is applying population-based metaheuristics. Among such metaheuristics, there is a special class where searching for the best solution is based on the collective behavior of decentralized, self-organized agents. This study proposes an approach in which a swarm of agents tries to improve solutions from the population of solutions. The process is carried out in parallel threads. The proposed algorithm—based on the mushroom-picking metaphor—was implemented using Scala in an Apache Spark environment. An extended computational experiment shows how introducing a combination of simple optimization agents and increasing the number of threads may improve the results obtained by the model in the case of TSP and JSSP problems.

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Hybrid Genetic Algorithms and Tour Construction and Improvement Algorithms Used for Optimizing the Traveling Salesman Problem

Ильин

Simić

et al. 2020

Advances in Intelligent Systems and Computing

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A novel ODV crossover operator-based genetic algorithms for traveling salesman problem

Cited by 14 publications

References 28 publications

Artificial intelligence in agricultural value chain: review and future directions

Artificial intelligence in agricultural value chain: review and future directions

Parallelized Swarm Intelligence Approach for Solving TSP and JSSP Problems

Hybrid Genetic Algorithms and Tour Construction and Improvement Algorithms Used for Optimizing the Traveling Salesman Problem

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