An Efficient Genetic Fuzzy Approach to UAV Swarm Routing

2016 ◽  
Vol 04 (02) ◽  
pp. 117-127 ◽  
Author(s):  
Anoop Sathyan ◽  
Nicholas D. Ernest ◽  
Kelly Cohen
Keyword(s):  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Search Algorithm ◽  
Task Assignment ◽  
Search Space ◽  
Computational Time ◽  
Rule Base ◽  
Heuristic Search Algorithm ◽  
Multiple Traveling Salesman Problem ◽  
Uav Swarm

Fuzzy logic is used in a variety of applications because of its universal approximator attribute and nonlinear characteristics. But, it takes a lot of trial and error to come up with a set of membership functions and rule-base that will effectively work for a specific application. This process could be simplified by using a heuristic search algorithm like Genetic Algorithm (GA). In this paper, genetic fuzzy is applied to the task assignment for cooperating Unmanned Aerial Vehicles (UAVs) classified as the Polygon Visiting Multiple Traveling Salesman Problem (PVMTSP). The PVMTSP finds a lot of applications including UAV swarm routing. We propose a method of genetic fuzzy clustering that would be specific to PVMTSP problems and hence more efficient compared to k-means and c-means clustering. We developed two different algorithms using genetic fuzzy. One evaluates the distance covered by each UAV to cluster the search-space and the other uses a cost function that approximates the distance covered thus resulting in a reduced computational time. We compare these two approaches to each other as well as to an already benchmarked fuzzy clustering algorithm which is the current state-of-the-art. We also discuss how well our algorithm scales for increasing number of targets. The results are compared for small and large polygon sizes.

scholarly journals A Fuzzy Classifier with Feature Selection Based on the Gravitational Search Algorithm

Symmetry ◽  
2018 ◽  
Vol 10 (11) ◽  
pp. 609 ◽  
Author(s):  
Marina Bardamova ◽  
Anton Konev ◽  
Ilya Hodashinsky ◽  
Alexander Shelupanov
Keyword(s):  
Feature Selection ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Fuzzy Rule ◽  
Search Space ◽  
Fuzzy Rules ◽  
Rule Base ◽  
Rule Based ◽  
Gravitational Search ◽  
Rule Based Classifier

This paper concerns several important topics of the Symmetry journal, namely, pattern recognition, computer-aided design, diversity and similarity. We also take advantage of the symmetric and asymmetric structure of a transfer function, which is responsible to map a continuous search space to a binary search space. A new method for design of a fuzzy-rule-based classifier using metaheuristics called Gravitational Search Algorithm (GSA) is discussed. The paper identifies three basic stages of the classifier construction: feature selection, creating of a fuzzy rule base and optimization of the antecedent parameters of rules. At the first stage, several feature subsets are obtained by using the wrapper scheme on the basis of the binary GSA. Creating fuzzy rules is a serious challenge in designing the fuzzy-rule-based classifier in the presence of high-dimensional data. The classifier structure is formed by the rule base generation algorithm by using minimum and maximum feature values. The optimal fuzzy-rule-based parameters are extracted from the training data using the continuous GSA. The classifier performance is tested on real-world KEEL (Knowledge Extraction based on Evolutionary Learning) datasets. The results demonstrate that highly accurate classifiers could be constructed with relatively few fuzzy rules and features.

scholarly journals Task Resource Allocation in Grid using Swift Scheduler

2009 ◽  
Vol 4 (2) ◽  
pp. 158 ◽  
Author(s):  
K. Somasundaram ◽  
S. Radhakrishnan
Keyword(s):  
Waiting Time ◽  
Heuristic Search ◽  
Dynamic Scheduling ◽  
Weather Forecasting ◽  
Search Algorithm ◽  
Scheduling Algorithm ◽  
Computational Time ◽  
Parallel And Distributed Computing ◽  
Heuristic Search Algorithm ◽  
Computationally Intensive

In nature, Grid computing is the combination of parallel and distributed computing where running computationally intensive applications like sequence alignment, weather forecasting, etc are needed a proficient scheduler to solve the problems awfully fast. Most of the Grid tasks are scheduled based on the First come first served (FCFS) or FCFS with advanced reservation, Shortest Job First (SJF) and etc. But these traditional algorithms seize more computational time due to soar waiting time of jobs in job queue. In Grid scheduling algorithm, the resources selection is NPcomplete. To triumph over the above problem, we proposed a new dynamic scheduling algorithm which is the combination of heuristic search algorithm and traditional SJF algorithm called swift scheduler. The proposed algorithm takes care of Job’s memory and CPU requirements along with the priority of jobs and resources. Our experimental results shows that our scheduler reduces the average waiting time in the job queue and reduces the over all computational time.

scholarly journals Genetic Algorithm – A Sensible Evolutionary Optimization Technique

2019 ◽  
Vol 8 (9) ◽  
pp. 2805-2809
Keyword(s):  
Genetic Algorithm ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Random Search ◽  
Optimization Technique ◽  
Search Space ◽  
Effective Population ◽  
Natural Systems ◽  
Heuristic Search Algorithm ◽  
Feasible Solutions

The study presents a pragmatic outlook of genetic algorithm. Many biological algorithms are inspired for their ability to evolve towards best solutions and of all; genetic algorithm is widely accepted as they well suit evolutionary computing models. Genetic algorithm could generate optimal solutions on random as well as deterministic problems. Genetic algorithm is a mathematical approach to imitate the processes studied in natural evolution. The methodology of genetic algorithm is intensively experimented in order to use the power of evolution to solve optimization problems. Genetic algorithm is an adaptive heuristic search algorithm based on the evolutionary ideas of genetics and natural selection. Genetic algorithm exploits random search approach to solve optimization problems. Genetic algorithm takes benefits of historical information to direct the search into the convergence of better performance within the search space. The basic techniques of evolutionary algorithms are observed to be simulating the processes in natural systems. These techniques are aimed to carry effective population to the next generation and ensure the survival of the fittest. Nature supports the domination of stronger over the weaker ones in any kind. In this study, we proposed the arithmetic views of the behavior and operators of genetic algorithm that support the evolution of feasible solutions to optimized solutions.

scholarly journals Smart Root Search (SRS): A Novel Nature-Inspired Search Algorithm

Symmetry ◽  
2020 ◽  
Vol 12 (12) ◽  
pp. 2025
Author(s):  
Narjes Khatoon Naseri ◽  
Elankovan A. Sundararajan ◽  
Masri Ayob ◽  
Amin Jula
Keyword(s):  
Particle Swarm Optimization ◽  
Heuristic Search ◽  
Search Algorithm ◽  
Search Space ◽  
Test Functions ◽  
Swarm Optimization ◽  
Search Spaces ◽  
Heuristic Search Algorithm ◽  
Hair Roots ◽  
Competitive Algorithms

In this paper, a novel heuristic search algorithm called Smart Root Search (SRS) is proposed. SRS employs intelligent foraging behavior of immature, mature and hair roots of plants to explore and exploit the problem search space simultaneously. SRS divides the search space into several subspaces. It thereupon utilizes the branching and drought operations to focus on richer areas of promising subspaces while extraneous ones are not thoroughly ignored. To achieve this, the smart reactions of the SRS model are designed to act based on analyzing the heterogeneous conditions of various sections of different search spaces. In order to evaluate the performance of the SRS, it was tested on a set of known unimodal and multimodal test functions. The results were then compared with those obtained using genetic algorithms, particle swarm optimization, differential evolution and imperialist competitive algorithms and then analyzed statistically. The results demonstrated that the SRS outperformed comparative algorithms for 92% and 82% of the investigated unimodal and multimodal test functions, respectively. Therefore, the SRS is a promising nature-inspired optimization algorithm.

scholarly journals An Improved Crow Search Algorithm for Data Clustering

2020 ◽  
Vol 8 (1) ◽  
pp. 86-101 ◽  
Author(s):  
Vivi Nur Wijayaningrum ◽  
Novi Nur Putriwijaya
Keyword(s):  
Data Clustering ◽  
Search Algorithm ◽  
Search Space ◽  
Large Datasets ◽  
The Other ◽  
Computational Time ◽  
Local Optimum ◽  
Exploration And Exploitation ◽  
High Dimensions ◽  
Swarm Optimization

Metaheuristic algorithms are often trapped in local optimum solutions when searching for solutions. This problem often occurs in optimization cases involving high dimensions such as data clustering. Imbalance of the exploration and exploitation process is the cause of this condition because search agents are not able to reach the best solution in the search space. In this study, the problem is overcome by modifying the solution update mechanism so that a search agent not only follows another randomly chosen search agent, but also has the opportunity to follow the best search agent. In addition, the balance of exploration and exploitation is also enhanced by the mechanism of updating the awareness probability of each search agent in accordance with their respective abilities in searching for solutions. The improve mechanism makes the proposed algorithm obtain pretty good solutions with smaller computational time compared to Genetic Algorithm and Particle Swarm Optimization. In large datasets, it is proven that the proposed algorithm is able to provide the best solution among the other algorithms.

Harmony Search Algorithm

2018 ◽  
pp. 1-30 ◽  
Author(s):  
Alireza Askarzadeh ◽  
Esmat Rashedi
Keyword(s):  
Optimization Problems ◽  
Search Algorithm ◽  
Harmony Search ◽  
Optimal Solution ◽  
Harmony Search Algorithm ◽  
Computational Time ◽  
Engineering Applications ◽  
Heuristic Search Algorithm ◽  
And Performance ◽  
Significant Attention

Harmony search (HS) is a meta-heuristic search algorithm which tries to mimic the improvisation process of musicians in finding a pleasing harmony. In recent years, due to some advantages, HS has received a significant attention. HS is easy to implement, converges quickly to the optimal solution and finds a good enough solution in a reasonable amount of computational time. The merits of HS algorithm have led to its application to optimization problems of different engineering areas. In this chapter, the concepts and performance of HS algorithm are shown and some engineering applications are reviewed. It is observed that HS has shown promising performance in solving difficult optimization problems and different versions of this algorithm have been developed. In the next years, it is expected that HS is applied to more real optimization problems.

Harmony Search Algorithm

2017 ◽  
pp. 1-36 ◽  
Author(s):  
Alireza Askarzadeh ◽  
Esmat Rashedi
Keyword(s):  
Optimization Problems ◽  
Search Algorithm ◽  
Harmony Search ◽  
Optimal Solution ◽  
Harmony Search Algorithm ◽  
Computational Time ◽  
Engineering Applications ◽  
Heuristic Search Algorithm ◽  
And Performance ◽  
Significant Attention

Harmony search (HS) is a meta-heuristic search algorithm which tries to mimic the improvisation process of musicians in finding a pleasing harmony. In recent years, due to some advantages, HS has received a significant attention. HS is easy to implement, converges quickly to the optimal solution and finds a good enough solution in a reasonable amount of computational time. The merits of HS algorithm have led to its application to optimization problems of different engineering areas. In this chapter, the concepts and performance of HS algorithm are shown and some engineering applications are reviewed. It is observed that HS has shown promising performance in solving difficult optimization problems and different versions of this algorithm have been developed. In the next years, it is expected that HS is applied to more real optimization problems.

Multi-objective analysis of clusters using gravitational density based model with PSO

2019 ◽  
Vol 12 ◽  
Author(s):  
Naveen Trivedi ◽  
Suvendu Kanungo
Keyword(s):  
Clustering Algorithm ◽  
Local Density ◽  
Search Algorithm ◽  
Clustering Algorithms ◽  
Gravitational Search Algorithm ◽  
Vital Role ◽  
Computational Time ◽  
Data Sets ◽  
Multi Objective ◽  
Cluster Distance

Background: Clustering analysis plays a vital role in obtaining knowledgeable data from the huge amount of data sets in knowledge discovery. Most of the traditional clustering algorithms do not work well with high dimensional data. The objective of effective clustering is to obtain well connected, compact and separated clusters. Density-based clustering (DBSCAN) is one of the popular clustering algorithms uses local density information of data points to detect clusters with arbitrary shapes. The Gravitational search algorithm (GSA) is one of the effective approaches inspired by Newton’s law of gravitational force where every particle in the universe attracts every other particle with force. Objectives: In this paper, a novel multi-objective clustering is proposed to produce the desired number of valid clusters, further in a part of the paper we have also optimized the algorithm to obtain optimal solutions. Method: In the proposed approach a hybrid clustering algorithm based on GSA along with DBSCAN is recommended to group the data into the desired number of clusters, and in the next phase of the algorithm PSO is applied in order to optimize the solutions using the fitness functions. Results: In the analysis of the result, we used two objectives function namely quantization error and inter-cluster distance to evaluate the performance of our algorithm. Conclusion: The algorithm has been compared with some well- known traditional heuristics based method in terms of accuracy and computational time.

scholarly journals A Novel Fuzzy Clustering Recommendation Algorithm Based On Pso

2014 ◽  
Vol 14 (5) ◽  
pp. 108-117 ◽  
Author(s):  
Zhang Hui ◽  
You Fei
Keyword(s):  
Particle Swarm Optimization ◽  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Solution Space ◽  
Pso Algorithm ◽  
Search Space ◽  
Improved Method ◽  
Recommendation Algorithm ◽  
Swarm Optimization ◽  
Fuzzy Clustering Algorithm

Abstract Aiming at the problem of recommendation systems, this paper proposes a fuzzy clustering algorithm based on particle swarm optimization. This algorithm can find the best solution, using the capacity of global search in PSO algorithm with a powerful global and defining a proportion factor, which can adjust the position and reduce the search space automatically. Then using mutation particles it replaces the particles flying out the solution space by new particles during the searching process. In order to check the performance of the proposed algorithm, by testing with typical ZDT1, ZDT2, ZDT3 functions, the experimental results show that the improved method not only has a better ability to converge to the global point, but can also efficiently avoid premature convergence.

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