A SEMI-GENERAL METHOD TO SOLVE THE COMBINATORIAL OPTIMIZATION PROBLEMS BASED ON NANOCOMPUTING

Nanocomputing describes computing that uses nanoscale devices. It is reasonable to search for nanoscale particles, such as molecules, that do not require difficult fabrication steps. DNA is recognized as a nanomaterial, not as a biological material, in the research field of nanotechnology. This paper proposes a semi-general method to solve combinatorial optimization problems based on DNA computing. It is obvious that the DNA molecule is one of the most promising functional nanomaterials. However, the application of DNA molecules is still under study because of the big gap that exists between theory and practice.

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A novel methodology for optimal land allocation for agricultural crops using Social Spider Algorithm

PeerJ ◽

10.7717/peerj.7559 ◽

2019 ◽

Vol 7 ◽

pp. e7559

Author(s):

N Thilagavathi ◽

T Amudha

Keyword(s):

Combinatorial Optimization ◽

Optimization Problems ◽

Crop Productivity ◽

Research Field ◽

Land Allocation ◽

Net Income ◽

Social Spider ◽

Combinatorial Optimization Problems ◽

Social Spider Algorithm ◽

Land Holdings

In the current agricultural scenario, availability of suitable land for cultivation is less and profitable allocation of the land for cultivating crops seems to be a cumbersome task. Crop planning optimization is a major research field in agriculture, in which land optimization is a significant challenge, which falls under the category of combinatorial optimization problems. The main objective of the present research is to maximize the net income from agriculture through optimal land allocation. Bio-inspired algorithms are quite popular in solving combinatorial optimization problems. Social Spider Algorithm (SSA), a new bio-inspired algorithm, is used to solve land optimization problem in this research based on the simulation of cooperative behaviour of social spiders. The agricultural area chosen for case study is the Coimbatore region, located in Tamilnadu state, India and the relevant data for the crops are collected from Tamilnadu Agricultural University Coimbatore, India. The optimal planting area, crop productivity for various land holdings and the water requirements are computed by SSA and the results have shown better directions for agricultural planning to improve the profit with constrained land area and water limitations.

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Combinatorial Optimization Problems and Metaheuristics: Review, Challenges, Design, and Development

Applied Sciences ◽

10.3390/app11146449 ◽

2021 ◽

Vol 11 (14) ◽

pp. 6449

Author(s):

Fernando Peres ◽

Mauro Castelli

Keyword(s):

Combinatorial Optimization ◽

Scientific Community ◽

Optimization Problems ◽

Research Field ◽

Machine Learning Algorithms ◽

Combinatorial Optimization Problems ◽

The Past ◽

Definition Of ◽

Problem Solvers ◽

New Algorithms

In the past few decades, metaheuristics have demonstrated their suitability in addressing complex problems over different domains. This success drives the scientific community towards the definition of new and better-performing heuristics and results in an increased interest in this research field. Nevertheless, new studies have been focused on developing new algorithms without providing consolidation of the existing knowledge. Furthermore, the absence of rigor and formalism to classify, design, and develop combinatorial optimization problems and metaheuristics represents a challenge to the field’s progress. This study discusses the main concepts and challenges in this area and proposes a formalism to classify, design, and code combinatorial optimization problems and metaheuristics. We believe these contributions may support the progress of the field and increase the maturity of metaheuristics as problem solvers analogous to other machine learning algorithms.

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