Bio-inspired algorithms: principles, implementation, and applications to wireless communication

Swayamsiddha, Swati

doi:10.1016/b978-0-12-819714-1.00013-0

Cited by 12 publications

(6 citation statements)

References 30 publications

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“…The basic procedures of GA involve four steps, that is, initialization, selection, crossover, and mutation. [ 111 ] The flowchart of GA is shown in Figure 29. GA was employed by some early ML‐assisted polymer discoveries.…”

Section: Steps In Ml‐assisted Polymer Design and Discoverymentioning

confidence: 99%

The Rise of Machine Learning in Polymer Discovery

Yan

2023

Advanced Intelligent Systems

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In the recent decades, with rapid development in computing power and algorithms, machine learning (ML) has exhibited its enormous potential in new polymer discovery. Herein, the history of ML is described and the basic process of ML accelerated polymer discovery is summarized. Next, the four steps in this process are reviewed, that is, dataset selection, fingerprinting, ML framework, and new polymer generation. Finally, a couple of main challenges for ML accelerated polymer discovery is presented and the outlooks in this field are prospected. It is expected that this review can service as a useful tool for the people who just step into this field and deepen the understanding for the people who are already in this field.

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Section: Steps In Ml‐assisted Polymer Design and Discoverymentioning

confidence: 99%

The Rise of Machine Learning in Polymer Discovery

Yan

2023

Advanced Intelligent Systems

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“…This cycle continues until a certain result is achieved or the stop criterion is satisfied [30]. Figure 7 shows the flowchart of GA and the steps of binary GA are discussed below [32].…”

Section: Genetic Algorithmmentioning

confidence: 99%

“…The rand 1 and rand 2 are random numbers that have a constant distribution in the range 0-1. Figure 8 shows the flowchart of PSO and the steps of PSO are discussed below [32]. 1 ) is sorted, the particle having the best fitness value is determined for the current generation, and the best location ( g !…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

Heat Exchanger Design and Optimization

Kharaji¹

2022

Heat Exchangers

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A heat exchanger is a unit operation used to transfer heat between two or more fluids at different temperatures. There are many different types of heat exchangers that are categorized based on different criteria, such as construction, flow arrangement, heat transfer mechanism, etc. Heat exchangers are optimized based on their applications. The most common criteria for optimization of heat exchangers are the minimum initial cost, minimum operation cost, maximum effectiveness, minimum pressure drop, minimum heat transfer area, minimum weight, or material. Using the data modeling, the optimization of a heat exchanger can be transformed into a constrained optimization problem and then solved by modern optimization algorithms. In this chapter, the thermal design and optimization of shell and tube heat exchangers are presented.

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“…The CA problem has been widely tackled in the literature and different models have been proposed to resolve it, including game theory, 4‐6 pricing and auction mechanisms, 7‐9 graph coloring, 10,11 and so forth. More recently, metaheuristics algorithms are recommended to address the CA problem 12‐14 . For instance, GAs are applied in Reference 15, PSO in Reference 16, ant colony system (ACS) in Reference 17, artificial bee colony optimization (ABC) in Reference 18, and differential evolution (DE) in Reference 19.…”

Section: Introductionmentioning

confidence: 99%

“…More recently, metaheuristics algorithms are recommended to address the CA problem. [12][13][14] For instance, GAs are applied in Reference 15, PSO in Reference 16, ant colony system (ACS) in Reference 17, artificial bee colony optimization (ABC) in Reference 18, and differential evolution (DE) in Reference 19. Among all the proposed approaches, bio-inspired algorithms have drawn the most attention by achieving better allocation efficiency within a reasonable time.…”

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confidence: 99%

Binary Jaya‐based channel allocation in cognitive radio networks

Saoucha

2022

Concurrency and Computation

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Summary Cognitive radio is a key solution for wireless networks, as it offers the possibility to cope with spectrum scarcity problem. In this work, we are interested in the channel allocation problem (CA) in cognitive radio networks which is a well‐known NP‐hard optimization problem. CA has been resolved using classic evolutionary approaches such as genetic algorithms (GAs) and particle swarm optimization (PSO). However, the performance of these latters depends strongly on the parameters tuning which is a hard time‐consuming task. Under this umbrella, in this article, we propose a new solution based on Jaya algorithm which is a recent free parameters approach. The performance of our proposed algorithm is compared against GA and PSO under different network topologies, and this in terms of max‐sum‐reward, max‐min‐reward, and max‐proportional‐fair. The obtained simulation results confirm the superiority of our proposed approach under all the tested scenarios.

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Bio-inspired algorithms: principles, implementation, and applications to wireless communication

Cited by 12 publications

References 30 publications

The Rise of Machine Learning in Polymer Discovery

The Rise of Machine Learning in Polymer Discovery

Heat Exchanger Design and Optimization

Binary Jaya‐based channel allocation in cognitive radio networks

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