Review and Classification of Bio-inspired Algorithms and Their Applications

Fan, Xumei; Sayers, William; Zhang, Shujun; Han, Zhiwu; Ren, Luquan; Chizari, Hassan

doi:10.1007/s42235-020-0049-9

Cited by 100 publications

(70 citation statements)

References 108 publications

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“…Optimisation algorithms are very varied, due to the extremely varied nature of the problems they are applied too. Small differences in algorithms can mean that they are more suitable for the nuances of different fields or particular specialisms, and therefore many different optimisation algorithms are simultaneously viable in their own right, when applied to particular problems (Fan et al, 2020;Weerasuriya et al, 2021). Because of this, there are several sub-categories of the broader category of optimisation algorithm, including single-objective and multiple objective algorithms, examples of which are discussed below.…”

Section: Optimisation Techniques and Meta-heuristicsmentioning

confidence: 99%

Artificial Intelligence and Optimisation Techniques for Risk Reduction in Civil Engineering

Sayers

2022

Handbook of Research on Digital Transformation, Industry Use Cases, and the Impact of Disruptive Technologies

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Artificial intelligence techniques are at the centre of a major shift in business today. They have a very broad array of applications within businesses, including that of optimisation for risk reduction in civil engineering projects. This is an active area of research, which has started to see real-world applications over the last few decades. It is still hindered by the extreme complexity of civil engineering problems and the computing power necessary to tackle these, but the economic and other benefits of these emerging technologies are too important to ignore. With that in mind, this chapter reviews the current state of research and real-world practice of optimisation techniques and artificial intelligence in risk reduction in this field. It also examines related promising techniques and their future potential.

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Section: Optimisation Techniques and Meta-heuristicsmentioning

confidence: 99%

Artificial Intelligence and Optimisation Techniques for Risk Reduction in Civil Engineering

Sayers

2022

Handbook of Research on Digital Transformation, Industry Use Cases, and the Impact of Disruptive Technologies

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“…Scientists are studying the natural behavior of a flock of birds, how the birds do the tasks and successfully cooperate within a flock. Researchers in [ 4 , 5 , 6 ] took the initiative to design artificial intelligence-based bio-inspired algorithms, i.e., ant colony optimization (ACO), particle swarm optimization (PSO), and pigeon-inspired optimization (PIO) to control robots in formation or swarm.…”

Section: Introductionmentioning

confidence: 99%

Collective Motion and Self-Organization of a Swarm of UAVs: A Cluster-Based Architecture

Ali

Han

Masood³

2021

Sensors

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This study proposes a collective motion and self-organization control of a swarm of 10 UAVs, which are divided into two clusters of five agents each. A cluster is a group of UAVs in a dedicated area and multiple clusters make a swarm. This paper designs the 3D model of the whole environment by applying graph theory. To address the aforesaid issues, this paper designs a hybrid meta-heuristic algorithm by merging the particle swarm optimization (PSO) with the multi-agent system (MAS). First, PSO only provides the best agents of a cluster. Afterward, MAS helps to assign the best agent as the leader of the nth cluster. Moreover, the leader can find the optimal path for each cluster. Initially, each cluster contains agents at random positions. Later, the clusters form a formation by implementing PSO with the MAS model. This helps in coordinating the agents inside the nth cluster. However, when two clusters combine and make a swarm in a dynamic environment, MAS alone is not able to fill the communication gap of n clusters. This study does it by applying the Vicsek-based MAS connectivity and synchronization model along with dynamic leader selection ability. Moreover, this research uses a B-spline curve based on simple waypoint defined graph theory to create the flying formations of each cluster and the swarm. Lastly, this article compares the designed algorithm with the NSGA-II model to show that the proposed model has better convergence and durability, both in the individual clusters and inside the greater swarm.

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“…The primary goal of the bio‐inspired algorithm is to fine‐tune the design variables and find the best possible solution. The benefits of a bio‐inspired algorithm are easy implementation, no need for data inclination; an optimum neighborhood solution can be found and are used in numerous application 23‐26 …”

Section: Introductionmentioning

confidence: 99%

Energy efficient optimal parent selection based routing protocol for Internet of Things using firefly optimization algorithm

Sankar¹,

Ramasubbareddy

Luhach

et al. 2020

Trans Emerging Tel Tech

103

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Energy conservation is a major challenge in the Internet of Things (IoT) as the number of resource‐constrained devices is connected to the network. Routing plays a vital role in IoT to extend the lifespan of the network. Routing protocol for low‐power and lossy networks (RPL) is a standard routing protocol in IoT. The parent selection is a crucial role in the routing process to exchange the data. In RPL, the researchers have introduced a single metric, composite metric, and multiobjective optimization algorithm for parent selection. However, the improper parent selection causes the packet losses, congestion among the network nodes, depletes more energy, and increases the convergence time. To overcome these issues, this article proposes energy efficient optimal parent selection in RPL (EEOPS‐RPL) using firefly optimization algorithm to extend the lifespan of the IoT network. In EEOPS‐RPL, each node in the network is considered to be firefly and also calculates the current location of firefly, attraction of the fireflies, random function, velocity, and the global best values in the network. Residual energy and expected transmission count are attractiveness parameters and distance is a movement parameter to choose the optimum parent in the destination‐oriented directed acyclic graph for data transmission. The simulation is conducted using COOJA. The EEOPS‐RPL provides better performance in comparison to the efficient parent selection for RPL (EPC‐RPL) and the E‐RPL. The EEOPS‐RPL improves the packet transmission ratio and lifespan of the network by 2% to 5%, and 5% to 10%, respectively, compared with EPC‐RPL and E‐RPL. In EEOPS‐RPL, each participant node applies the firefly algorithm over the parent node information such as distance, residual energy, and expected transmission count through DODAG information object control message to pick the best parent node in the DODAG. The firefly algorithm provides the fast convergence that able to choose the optimal parent quickly. Thus, it avoids the packet loss during the route establishment in network. Thus, EEOPS‐RPL extends the lifespan of the network and reduces the convergence time.

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Review and Classification of Bio-inspired Algorithms and Their Applications

Cited by 100 publications

References 108 publications

Artificial Intelligence and Optimisation Techniques for Risk Reduction in Civil Engineering

Artificial Intelligence and Optimisation Techniques for Risk Reduction in Civil Engineering

Collective Motion and Self-Organization of a Swarm of UAVs: A Cluster-Based Architecture

Energy efficient optimal parent selection based routing protocol for Internet of Things using firefly optimization algorithm

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