Hybridization of Evolutionary Algorithms

Fister, Iztok; Mernik, Marjan; Brest, Janez

doi:10.5772/15592

Cited by 7 publications

(6 citation statements)

References 38 publications

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“…As shown in Table 3, nature-inspired approaches have been shown to provide adaptive multi-objective mechanisms with fewer complications and demands [85][86][87][88]. As the field of nature-inspired heuristics is large and growing, an overview is outside the scope of this paper.…”

Section: Nature-inspired Heuristics To Improve Voronoi Tessellationsmentioning

confidence: 99%

“…We consider it to be beneficial to implement a GA with our BISON algorithm. This is because evolutionary algorithms, specifically GAs, have the flexibility of generating new candidate solutions inside the confined Voronoi region and to do so on the basis of information available locally to each node [88]. In contrast, PSO often requires distributing the nodes' best local and global solution to all nodes, which means it relies on guaranteed connectivity and heavy communication between all the nodes.…”

Section: Motivating Our Decision To Chose Gas Over Psomentioning

confidence: 99%

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A Hybrid Voronoi Tessellation/Genetic Algorithm Approach for the Deployment of Drone-Based Nodes of a Self-Organizing Wireless Sensor Network (WSN) in Unknown and GPS Denied Environments

Eledlebi

Hildmann

Ruta

et al. 2020

Drones

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Using autonomously operating mobile sensor nodes to form adaptive wireless sensor networks has great potential for monitoring applications in the real world. Especially in, e.g., disaster response scenarios—that is, when the environment is potentially unsafe and unknown—drones can offer fast access and provide crucial intelligence to rescue forces due the fact that they—unlike humans—are expendable and can operate in 3D space, often allowing them to ignore rubble and blocked passages. Among the practical issues faced are the optimizing of device–device communication, the deployment process and the limited power supply for the devices and the hardware they carry. To address these challenges a host of literature is available, proposing, e.g., the use of nature-inspired approaches. In this field, our own work (bio-inspired self-organizing network, BISON, which uses Voronoi tessellations) achieved promising results. In our previous approach the wireless sensors network (WSN) nodes were using knowledge about their coverage areas center of gravity, something which a drone would not automatically know. To address this, we augment BISON with a genetic algorithm (GA), which has the benefit of further improving network deployment time and overall coverage. Our evaluations show, unsurprisingly, an increase in energy cost. Two variations of our proposed GA-BISON deployment strategies are presented and compared, along with the impact of the GA. Counter-intuitively, performance and robustness increase in the presence of noise.

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Section: Nature-inspired Heuristics To Improve Voronoi Tessellationsmentioning

confidence: 99%

Section: Motivating Our Decision To Chose Gas Over Psomentioning

confidence: 99%

A Hybrid Voronoi Tessellation/Genetic Algorithm Approach for the Deployment of Drone-Based Nodes of a Self-Organizing Wireless Sensor Network (WSN) in Unknown and GPS Denied Environments

Eledlebi

Hildmann

Ruta

et al. 2020

Drones

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show abstract

“…The mutation rate "F", crossover rate "C r " and population dimension "N p" maintain balance between exploration and exploitation [6]. Exploration is associated with finding new solutions, and exploitation is associated with searching for new, suitable solutions; the two processes are linked in the evolutionary search [42,43]. Therefore, the mutation and crossover rates influence the convergence rate and the effectiveness of the search space [44].…”

Section: Parameter Controlmentioning

confidence: 99%

Differential Evolution: A Survey and Analysis

2018

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“…First, the DE algorithm applies certain control parameters to the system implementation. the two processes are linked in the evolutionary search [31,32]. Therefore, the mutation and crossover rates influenced the convergence rate and the effectiveness of the search space [33].…”

Section: Parameter Controlmentioning

confidence: 99%

Large-Scale Evolutionary Optimization Using Multi-Layer Strategy Differential Evolution

Eltaeib

Mahmood

2018

Computational Collective Intelligence

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Differential evolution (DE) has been extensively used in optimization studies since its development in 1995 because of its reputation as an effective global optimizer. DE is a population-based metaheuristic technique that develops numerical vectors to solve optimization problems. DE strategies have a significant impact on DE performance and play a vital role in achieving stochastic global optimization. However, DE is highly dependent on the control parameters involved. In practice, the fine-tuning of these parameters is not always easy. Here, we discuss the improvements and developments that have been made to DE algorithms. The Multi-Layer Strategies Differential Evolution (MLSDE) algorithm, which finds optimal solutions for large scale problems. To solve large scale problems were grouped different strategies together and applied them to date set. Furthermore, these strategies were applied to selected vectors to strengthen the exploration ability of the algorithm. Extensive computational analysis was also carried out to evaluate the performance of the proposed algorithm on a set of well-known CEC 2015 benchmark v functions. This benchmark was utilized for the assessment and performance evaluation of the proposed algorithm. vi ACKNOWLEDGEMENTS Special appreciations to my family. Words cannot express how thankful I am to my wife, my daughters, my mother, my mother-in law, and father-in-law for all of the sacrifices and prayers for me that you've made on my behalf. I would like to express my special thanks to my advisor Professor Ausif Mahmood and his efforts, encouraging and guiding me to grow as a research scientist.. vii

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Hybridization of Evolutionary Algorithms

Cited by 7 publications

References 38 publications

A Hybrid Voronoi Tessellation/Genetic Algorithm Approach for the Deployment of Drone-Based Nodes of a Self-Organizing Wireless Sensor Network (WSN) in Unknown and GPS Denied Environments

A Hybrid Voronoi Tessellation/Genetic Algorithm Approach for the Deployment of Drone-Based Nodes of a Self-Organizing Wireless Sensor Network (WSN) in Unknown and GPS Denied Environments

Differential Evolution: A Survey and Analysis

Large-Scale Evolutionary Optimization Using Multi-Layer Strategy Differential Evolution

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