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

Peres, Fernando; Castelli, Mauro

doi:10.3390/app11146449

Cited by 66 publications

(40 citation statements)

References 88 publications

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“…For each problem discussed, the objective function used, the nature of the objective as well as the constraints considered have also been elaborated on. As has been observed by [7], combinatorial optimization problems in the real-world are known to be difficult to formulate and generally are hard to solve. Moreover, choosing the right algorithm is also a tedious task as each comes with a different set of characterizations.…”

Section: Introductionmentioning

confidence: 99%

“…In order to organize the review, this paper takes guidance from the work of [7]. They note that the most popular algorithms for use in combinatorial optimization problems are the Ant Colony Optimization (ACO), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Differential Evolution (DE) and Artificial Bee Colony (ABC).…”

Section: Introductionmentioning

confidence: 99%

“…They note that the most popular algorithms for use in combinatorial optimization problems are the Ant Colony Optimization (ACO), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Differential Evolution (DE) and Artificial Bee Colony (ABC). Moreover, following from the discussion in [7], this paper considers six factors for each application identified. These are:…”

Section: Introductionmentioning

confidence: 99%

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Making Cities Smarter—Optimization Problems for the IoT Enabled Smart City Development: A Mapping of Applications, Objectives, Constraints

Syed

Sierra-Sosa

Kumar

et al. 2022

Sensors

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One of the prime aims of smart cities has been to optimally manage the available resources and systems that are used in the city. With an increase in urban population that is set to grow even faster in the future, smart city development has been the main goal for governments worldwide. In this regard, while the useage of Artificial Intelligence (AI) techniques covering the areas of Machine and Deep Learning have garnered much attention for Smart Cities, less attention has focused towards the use of combinatorial optimization schemes. To help with this, the current review presents a coverage of optimization methods and applications from a smart city perspective enabled by the Internet of Things (IoT). A mapping is provided for the most encountered applications of computational optimization within IoT smart cities for five popular optimization methods, ant colony optimization, genetic algorithm, particle swarm optimization, artificial bee colony optimization and differential evolution. For each application identified, the algorithms used, objectives considered, the nature of the formulation and constraints taken in to account have been specified and discussed. Lastly, the data setup used by each covered work is also mentioned and directions for future work have been identified. This review will help researchers by providing them a consolidated starting point for research in the domain of smart city application optimization.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Making Cities Smarter—Optimization Problems for the IoT Enabled Smart City Development: A Mapping of Applications, Objectives, Constraints

Syed

Sierra-Sosa

Kumar

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…There are many different optimization methods which can be classified into: analytical, stochastic, and enumerative [1][2][3][4][5][6][7][8][9][10]. A characteristic feature of analytical methods is using the gradient of objective function while searching the optimal solution.…”

Section: Introductionmentioning

confidence: 99%

Computational Scale-Up for Flood Fed/Starve Fed Single Screw Extrusion of Polymers

Nastaj

Wilczyński

2022

Polymers

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A novel scaling-up computer system for single screw extrusion of polymers has been developed. This system makes it possible to scale-up extrusion process with both starve feeding and flood feeding. Each of the scale-up criteria can be an objective function to be minimized, represented by single values or functional dependencies over the screw length. The basis of scaling-up is process simulation made with the use of the GSEM program (Global Screw Extrusion Model). Scaling-up is performed using the GASES program (Genetic Algorithms Screw Extrusion Scaling) based on Genetic Algorithms. Scaling-up the extrusion process has been performed to increase extrusion output according to the scaling-up criteria defined by the single parameters of unit energy consumption, polymer plasticating rate and polymer temperature, as well as by the process parameters profiles of the temperature and plasticating. The global objective function reached the lowest value for the selected process parameters, and extrusion throughput was significantly increased.

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“…The Ising problem is to find a binary spin configuration that minimizes the total energy function (the number of frustrated edges) for a given set of edges. A variety of combinatorial optimization problems, such as sequencing and ordering problems, resource allocation problems, and clustering problems [5,6], can be mapped to the Ising problem [7].…”

Section: Introductionmentioning

confidence: 99%

Investigation of Eigenmode-Based Coupled Oscillator Solver Applied to Ising Spin Problems

et al. 2021

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We evaluate a coupled oscillator solver by applying it to square lattice (N × N) Ising spin problems for N values up to 50. The Ising problems are converted to a classical coupled oscillator model that includes both positive (ferromagnetic-like) and negative (antiferromagnetic-like) coupling between neighboring oscillators (i.e., they are reduced to eigenmode problems). A map of the oscillation amplitudes of lower-frequency eigenmodes enables us to visualize oscillator clusters with a low frustration density (unfrustrated clusters). We found that frustration tends to localize at the boundary between unfrustrated clusters due to the symmetric and asymmetric nature of the eigenmodes. This allows us to reduce frustration simply by flipping the sign of the amplitude of oscillators around which frustrated couplings are highly localized. For problems with N = 20 to 50, the best solutions with an accuracy of 96% (with respect to the exact ground state) can be obtained by simply checking the lowest ~N/2 candidate eigenmodes.

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

Cited by 66 publications

References 88 publications

Making Cities Smarter—Optimization Problems for the IoT Enabled Smart City Development: A Mapping of Applications, Objectives, Constraints

Making Cities Smarter—Optimization Problems for the IoT Enabled Smart City Development: A Mapping of Applications, Objectives, Constraints

Computational Scale-Up for Flood Fed/Starve Fed Single Screw Extrusion of Polymers

Investigation of Eigenmode-Based Coupled Oscillator Solver Applied to Ising Spin Problems

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