617 research outputs found
Attraction and diffusion in nature-inspired optimization algorithms
Nature-inspired algorithms usually use some form of attraction and diffusion as a mechanism for exploitation and exploration. In this paper, we investigate the role of attraction and diffusion in algorithms and their ways in controlling the behaviour and performance of nature-inspired algorithms. We highlight different ways of the implementations of attraction in algorithms such as the firefly algorithm, charged system search, and the gravitational search algorithm. We also analyze diffusion mechanisms such as random walks for exploration in algorithms. It is clear that attraction can be an effective way for enhancing exploitation, while diffusion is a common way for exploration. Furthermore, we also discuss the role of parameter tuning and parameter control in modern metaheuristic algorithms, and then point out some key topics for further research
Attraction and diffusion in nature-inspired optimization algorithms
Nature-inspired algorithms usually use some form of attraction and diffusion as a mechanism for exploitation and exploration. In this paper, we investigate the role of attraction and diffusion in algorithms and their ways in controlling the behaviour and performance of nature-inspired algorithms. We highlight different ways of the implementations of attraction in algorithms such as the firefly algorithm, charged system search, and the gravitational search algorithm. We also analyze diffusion mechanisms such as random walks for exploration in algorithms. It is clear that attraction can be an effective way for enhancing exploitation, while diffusion is a common way for exploration. Furthermore, we also discuss the role of parameter tuning and parameter control in modern metaheuristic algorithms, and then point out some key topics for further research
Nature Inspired Evolutionary Swarm Optimizers for Biomedical Image and Signal Processing -- A Systematic Review
The challenge of finding a global optimum in a solution search space with
limited resources and higher accuracy has given rise to several optimization
algorithms. Generally, the gradient-based optimizers converge to the global
solution very accurately, but they often require a large number of iterations
to find the solution. Researchers took inspiration from different natural
phenomena and behaviours of many living organisms to develop algorithms that
can solve optimization problems much quicker with high accuracy. These
algorithms are called nature-inspired meta-heuristic optimization algorithms.
These can be used for denoising signals, updating weights in a deep neural
network, and many other cases. In the state-of-the-art, there are no systematic
reviews available that have discussed the applications of nature-inspired
algorithms on biomedical signal processing. The paper solves that gap by
discussing the applications of such algorithms in biomedical signal processing
and also provides an updated survey of the application of these algorithms in
biomedical image processing. The paper reviews 28 latest peer-reviewed relevant
articles and 26 nature-inspired algorithms and segregates them into thoroughly
explored, lesser explored and unexplored categories intending to help readers
understand the reliability and exploration stage of each of these algorithms
Investigation of Evolutionary Computation Techniques for Enhancing Solar Photovoltaic Cell Performance
The pursuit of optimized solar photovoltaic (PV) cell parameters is critical for advancing renewable energy technologies amidst global energy security and climate change challenges. This research investigates the efficacy of particle swarm optimization (PSO) and gray wolf optimization (GWO) in fine-tuning PV cell behavior parameters. Leveraging evolutionary computation, the study aims to maximize energy output, minimize costs, and enhance system reliability by optimizing material properties, structural configurations, and operating conditions. Through iterative optimization, PSO and GWO navigate the parameter space with precision, yielding solutions that maximize energy yield and system efficiency
An optimised cuckoo-based discrete symbiotic organisms search strategy for tasks scheduling in cloud computing environment
Currently, the cloud computing paradigm is experiencing rapid growth as there
is a shift from other distributed computing methods and traditional IT
infrastructure towards it. Consequently, optimised task scheduling techniques
have become crucial in managing the expanding cloud computing environment. In
cloud computing, numerous tasks need to be scheduled on a limited number of
diverse virtual machines to minimise the imbalance between the local and global
search space; and optimise system utilisation. Task scheduling is a challenging
problem known as NP-complete, which means that there is no exact solution, and
we can only achieve near-optimal results, particularly when using large-scale
tasks in the context of cloud computing. This paper proposes an optimised
strategy, Cuckoo-based Discrete Symbiotic Organisms Search (C-DSOS) that
incorporated with Levy-Flight for optimal task scheduling in the cloud
computing environment to minimise degree of imbalance. The strategy is based on
the Standard Symbiotic Organism Search (SOS), which is a nature-inspired
metaheuristic optimisation algorithm designed for numerical optimisation
problems. SOS simulates the symbiotic relationships observed in ecosystems,
such as mutualism, commensalism, and parasitism. To evaluate the proposed
technique, the CloudSim toolkit simulator was used to conduct experiments. The
results demonstrated that C-DSOS outperforms the Simulated Annealing Symbiotic
Organism Search (SASOS) algorithm, which is a benchmarked algorithm commonly
used in task scheduling problems. C-DSOS exhibits a favourable convergence
rate, especially when using larger search spaces, making it suitable for task
scheduling problems in the cloud. For the analysis, a t-test was employed,
reveals that C-DSOS is statistically significant compared to the benchmarked
SASOS algorithm, particularly for scenarios involving a large search space.Comment: 21 pages, 5 figures, 2 algorithms, 6 table
Cloud Service Selection System Approach based on QoS Model: A Systematic Review
The Internet of Things (IoT) has received a lot of interest from researchers recently. IoT is seen as a component of the Internet of Things, which will include billions of intelligent, talkative "things" in the coming decades. IoT is a diverse, multi-layer, wide-area network composed of a number of network links. The detection of services and on-demand supply are difficult in such networks, which are comprised of a variety of resource-limited devices. The growth of service computing-related fields will be aided by the development of new IoT services. Therefore, Cloud service composition provides significant services by integrating the single services. Because of the fast spread of cloud services and their different Quality of Service (QoS), identifying necessary tasks and putting together a service model that includes specific performance assurances has become a major technological problem that has caused widespread concern. Various strategies are used in the composition of services i.e., Clustering, Fuzzy, Deep Learning, Particle Swarm Optimization, Cuckoo Search Algorithm and so on. Researchers have made significant efforts in this field, and computational intelligence approaches are thought to be useful in tackling such challenges. Even though, no systematic research on this topic has been done with specific attention to computational intelligence. Therefore, this publication provides a thorough overview of QoS-aware web service composition, with QoS models and approaches to finding future aspects
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