Comparative Study of Privacy Preservation of Data in Hybrid Cloud of Combined Clustering and Geometric Data Perturbation Approach and Hybrid Ant Colony Optimization and Gravitational Search Algorithm

2020 ◽  
pp. 25-37
Author(s):  
Sridhar Reddy Vulapula ◽  
Srinivas Malladi
Keyword(s):  
Comparative Study ◽  
Ant Colony Optimization ◽  
Privacy Preservation ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Ant Colony ◽  
Perturbation Approach ◽  
Hybrid Cloud ◽  
Data Perturbation ◽  
Gravitational Search

Population Based Techniques for Solving the Student Project Allocation Problem

2020 ◽  
Vol 11 (2) ◽  
pp. 192-207 ◽  
Author(s):  
Patrick Kenekayoro ◽  
Promise Mebine ◽  
Bodouowei Godswill Zipamone
Keyword(s):  
Ant Colony Optimization ◽  
Constraint Satisfaction ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Population Based ◽  
Constraint Satisfaction Problems ◽  
Allocation Problem ◽  
Ant Colony ◽  
Optimal Solutions ◽  
Gravitational Search

The student project allocation problem is a well-known constraint satisfaction problem that involves assigning students to projects or supervisors based on a number of criteria. This study investigates the use of population-based strategies inspired from physical phenomena (gravitational search algorithm), evolutionary strategies (genetic algorithm), and swarm intelligence (ant colony optimization) to solve the Student Project Allocation problem for a case study from a real university. A population of solutions to the Student Project Allocation problem is represented as lists of integers, and the individuals in the population share information through population-based heuristics to find more optimal solutions. All three techniques produced satisfactory results and the adapted gravitational search algorithm for discrete variables will be useful for other constraint satisfaction problems. However, the ant colony optimization algorithm outperformed the genetic and gravitational search algorithms for finding optimal solutions to the student project allocation problem in this study.

scholarly journals Comparative study between elephant herding optimization (EHO) and U-turning ant colony optimization (U-TACO) in solving symmetric traveling salesman problem (STSP)

2019 ◽  
Vol 8 (2) ◽  
pp. 32 ◽  
Author(s):  
Saman M. Almufti ◽  
Ridwan Boya Marqas ◽  
Renas R. Asaad
Keyword(s):  
Comparative Study ◽  
Ant Colony Optimization ◽  
Traveling Salesman Problem ◽  
Ant Colony Algorithm ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Traveling Salesman ◽  
Ant Colony ◽  
Initial State ◽  
Symmetric Traveling Salesman Problem

Swarm Intelligence is an active area of researches and one of the most well-known high-level techniques intended to generat, select or find a heuristic that optimize solutions of optimization problems.Elephant Herding optimization algorithm (EHO) is a metaheuristic swarm based search algorithm, which is used to solve various optimi-zation problems. The algorithm is deducted from the behavior of elephant groups in the wild. Were elephants live in a clan with a leader matriarch, while the male elephants separate from the group when they reach adulthood. This is used in the algorithm in two parts. First, the clan updating mechanism. Second, the separation mechanism.U-Turning Ant colony optimization (U-TACO) is a swarm-based algorithm uses the behavior of real ant in finding the shortest way be-tween its current location and a source of food for solving optimization problems. U-Turning Ant colony Optimization based on making partial tour as an initial state for the basic Ant Colony algorithm (ACO).In this paper, a Comparative study has been done between the previous mentioned algorithms (EHO, U-TACO) in solving Symmetric Traveling Salesman Problem (STSP) which is one of the most well-known NP-Hard problems in the optimization field. The paper pro-vides tables for the results obtained by EHO and U-TACO for various STSP problems from the TSPLIB95.

Analysis of Optimal Power Flow Using Gravitational Search Algorithm

2016 ◽  
Vol 3 (4) ◽  
pp. 1-11
Author(s):  
M. Lakshmikantha Reddy ◽  
◽  
M. Ramprasad Reddy ◽  
V.C. Veera Reddy ◽  
◽  
...  
Keyword(s):  
Power Flow ◽  
Optimal Power Flow ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Optimal Power ◽  
Gravitational Search

Analysis on improvement of particle memory in gravitational search algorithm

2013 ◽  
Vol 32 (10) ◽  
pp. 2732-2735
Author(s):  
Chun-long LI ◽  
Juan DAI ◽  
Feng PAN
Keyword(s):  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Gravitational Search

Automatic Mining of Quantitative Association Rules with Gravitational Search Algorithm

2017 ◽  
Vol 27 (03) ◽  
pp. 343-372 ◽  
Author(s):  
Umit Can ◽  
Bilal Alatas
Keyword(s):  
Association Rules ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Fitness Function ◽  
Optimization Algorithms ◽  
Gravitational Search Algorithm ◽  
Search Space ◽  
Search Method ◽  
Quantitative Association Rules ◽  
Gravitational Search

The classical optimization algorithms are not efficient in solving complex search and optimization problems. Thus, some heuristic optimization algorithms have been proposed. In this paper, exploration of association rules within numerical databases with Gravitational Search Algorithm (GSA) has been firstly performed. GSA has been designed as search method for quantitative association rules from the databases which can be regarded as search space. Furthermore, determining the minimum values of confidence and support for every database which is a hard job has been eliminated by GSA. Apart from this, the fitness function used for GSA is very flexible. According to the interested problem, some parameters can be removed from or added to the fitness function. The range values of the attributes have been automatically adjusted during the time of mining of the rules. That is why there is not any requirements for the pre-processing of the data. Attributes interaction problem has also been eliminated with the designed GSA. GSA has been tested with four real databases and promising results have been obtained. GSA seems an effective search method for complex numerical sequential patterns mining, numerical classification rules mining, and clustering rules mining tasks of data mining.

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