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

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.

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

PeerJ ◽  
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.

Chapter 23. MaxSAT, Hard and Soft Constraints

2021 ◽  
Author(s):  
Chu Min Li ◽  
Felip Manyà
Keyword(s):  
Combinatorial Optimization ◽  
Lower Bounds ◽  
Branch And Bound ◽  
Data Structures ◽  
Optimization Problems ◽  
Soft Constraints ◽  
Inference Rules ◽  
Combinatorial Optimization Problems ◽  
Definition Of ◽  
Hard And Soft Constraints

MaxSAT solving is becoming a competitive generic approach for solving combinatorial optimization problems, partly due to the development of new solving techniques that have been recently incorporated into modern MaxSAT solvers, and to the challenge problems posed at the MaxSAT Evaluations. In this chapter we present the most relevant results on both approximate and exact MaxSAT solving, and survey in more detail the techniques that have proven to be useful in branch and bound MaxSAT and Weighted MaxSAT solvers. Among such techniques, we pay special attention to the definition of good quality lower bounds, powerful inference rules, clever variable selection heuristics and suitable data structures. Moreover, we discuss the advantages of dealing with hard and soft constraints in the Partial MaxSAT formalims, and present a summary of the MaxSAT Evaluations that have been organized so far as affiliated events of the International Conference on Theory and Applications of Satisfiability Testing.

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

2010 ◽  
Vol 09 (05) ◽  
pp. 391-398 ◽  
Author(s):  
MAJID DAREHMIRAKI
Keyword(s):  
Combinatorial Optimization ◽  
Dna Computing ◽  
Optimization Problems ◽  
Research Field ◽  
Theory And Practice ◽  
Functional Nanomaterials ◽  
Nanoscale Particles ◽  
Combinatorial Optimization Problems ◽  
Dna Molecule ◽  
General Method

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.

Computational Metrology for the Design and Manufacture of Product Geometry: A Classification and Synthesis

2006 ◽  
Vol 7 (1) ◽  
pp. 3-9 ◽  
Author(s):  
Vijay Srinivasan
Keyword(s):  
Combinatorial Optimization ◽  
Optimization Problems ◽  
Design Specification ◽  
Combinatorial Optimization Problems ◽  
The Past ◽  
Manufactured Products ◽  
Measurement Tools ◽  
The Status ◽  
Computational Metrology ◽  
Design And Manufacture

The increasing use of advanced measurement tools and technology in industry over the past 30 years has ushered in a new set of challenging computational problems. These problems can be broadly classified as fitting and filtering of discrete geometric data collected by measurements made on manufactured products. Collectively, they define the field of computational metrology for the design specification, production, and verification of product geometry. The fitting problems can be posed and solved as optimization problems; they involve both continuous and combinatorial optimization problems. The filtering problems can be unified under convolution problems, which include convolutions of functions as well as convolutions of sets. This paper presents the status of research and standardization efforts in computational metrology, with an emphasis on its classification and synthesis.

scholarly journals Stability concepts in bargaining games

2021 ◽  
Author(s):  
Somnath Kundu
Keyword(s):  
Combinatorial Optimization ◽  
Cost Sharing ◽  
Optimization Problems ◽  
Real Life ◽  
Profit Sharing ◽  
Bargaining Games ◽  
Bargaining Process ◽  
Combinatorial Optimization Problems ◽  
Definition Of ◽  
Stability Concepts

In this thesis we discuss some novel concepts of stability in bargaining games, over a network setting. So far, the studies on bargaining games were done as profit sharing problems, whose underlying combinatorial optimization problems are of packing type. In our work, we study bargaining games from a cost sharing perspective, where the underlying combinatorial optimization problems are covering type problems. Unlike previous studies, where bargaining processes are restricted to only two players, we study bargaining games over a more generic hypergraph setting, which allows any bargaining process to be formed among any number of players. In previous studies of bargaining games, the objects that are being negotiated are assumed to be uniform and only the outcomes of the negotiations are allowed to be different. However, in our study, we accommodate possibilities of non-uniform weights of the objects that are being negotiated, which is closer to any real life scenario. Finally we extend our study to incorporate socially aware players by introducing a relaxed and innovative definition of stability.

scholarly journals Stability concepts in bargaining games

2021 ◽  
Author(s):  
Somnath Kundu
Keyword(s):  
Combinatorial Optimization ◽  
Cost Sharing ◽  
Optimization Problems ◽  
Real Life ◽  
Profit Sharing ◽  
Bargaining Games ◽  
Bargaining Process ◽  
Combinatorial Optimization Problems ◽  
Definition Of ◽  
Stability Concepts

In this thesis we discuss some novel concepts of stability in bargaining games, over a network setting. So far, the studies on bargaining games were done as profit sharing problems, whose underlying combinatorial optimization problems are of packing type. In our work, we study bargaining games from a cost sharing perspective, where the underlying combinatorial optimization problems are covering type problems. Unlike previous studies, where bargaining processes are restricted to only two players, we study bargaining games over a more generic hypergraph setting, which allows any bargaining process to be formed among any number of players. In previous studies of bargaining games, the objects that are being negotiated are assumed to be uniform and only the outcomes of the negotiations are allowed to be different. However, in our study, we accommodate possibilities of non-uniform weights of the objects that are being negotiated, which is closer to any real life scenario. Finally we extend our study to incorporate socially aware players by introducing a relaxed and innovative definition of stability.

scholarly journals Bee Colony Optimization - part II: The application survey

2015 ◽  
Vol 25 (2) ◽  
pp. 185-219 ◽  
Author(s):  
Dusan Teodorovic ◽  
Milica Selmic ◽  
Tatjana Davidovic
Keyword(s):  
Combinatorial Optimization ◽  
Scientific Community ◽  
Optimization Problems ◽  
Optimization Algorithms ◽  
Heuristic Method ◽  
Continuous Optimization ◽  
Natural Behavior ◽  
Combinatorial Optimization Problems ◽  
Bee Colony ◽  
Bee Colony Optimization

Bee Colony Optimization (BCO) is a meta-heuristic method based on foraging habits of honeybees. This technique was motivated by the analogy found between the natural behavior of bees searching for food and the behavior of optimization algorithms searching for an optimum in combinatorial optimization problems. BCO has been successfully applied to various hard combinatorial optimization problems, mostly in transportation, location and scheduling fields. There are some applications in the continuous optimization field that have appeared recently. The main purpose of this paper is to introduce the scientific community more closely with BCO by summarizing its existing successful applications.

scholarly journals Crossover versus Mutation: A Comparative Analysis of the Evolutionary Strategy of Genetic Algorithms Applied to Combinatorial Optimization Problems

2014 ◽  
Vol 2014 ◽  
pp. 1-22 ◽  
Author(s):  
E. Osaba ◽  
R. Carballedo ◽  
F. Diaz ◽  
E. Onieva ◽  
I. de la Iglesia ◽  
...  
Keyword(s):  
Genetic Algorithms ◽  
Comparative Analysis ◽  
Combinatorial Optimization ◽  
Scientific Community ◽  
Statistical Study ◽  
Optimization Problems ◽  
Basic Structure ◽  
Evolutionary Strategy ◽  
Mutation Process ◽  
Combinatorial Optimization Problems

Since their first formulation, genetic algorithms (GAs) have been one of the most widely used techniques to solve combinatorial optimization problems. The basic structure of the GAs is known by the scientific community, and thanks to their easy application and good performance, GAs are the focus of a lot of research works annually. Although throughout history there have been many studies analyzing various concepts of GAs, in the literature there are few studies that analyze objectively the influence of using blind crossover operators for combinatorial optimization problems. For this reason, in this paper a deep study on the influence of using them is conducted. The study is based on a comparison of nine techniques applied to four well-known combinatorial optimization problems. Six of the techniques are GAs with different configurations, and the remaining three are evolutionary algorithms that focus exclusively on the mutation process. Finally, to perform a reliable comparison of these results, a statistical study of them is made, performing the normal distributionz-test.

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