scholarly journals KGSA: A Gravitational Search Algorithm for Multimodal Optimization based on K-Means Niching Technique and a Novel Elitism Strategy

2018 ◽  
Vol 16 (1) ◽  
pp. 1582-1606 ◽  
Author(s):  
Shahram Golzari ◽  
Mohammad Nourmohammadi Zardehsavar ◽  
Amin Mousavi ◽  
Mahmoud Reza Saybani ◽  
Abdullah Khalili ◽  
...  
Keyword(s):  
State Of The Art ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Search Space ◽  
Initial Population ◽  
Multimodal Optimization ◽  
Global Optima ◽  
Loop Technique ◽  
Gravitational Search ◽  
Optimum Direction

AbstractGravitational Search Algorithm (GSA) is a metaheuristic for solving unimodal problems. In this paper, a K-means based GSA (KGSA) for multimodal optimization is proposed. This algorithm incorporates K-means and a new elitism strategy called “loop in loop” into the GSA. First in KGSA, the members of the initial population are clustered by K-means. Afterwards, new population is created and divided in different niches (or clusters) to expand the search space. The “loop in loop” technique guides the members of each niche to the optimum direction according to their clusters. This means that lighter members move faster towards the optimum direction of each cluster than the heavier members. For evaluations, KGSA is benchmarked on well-known functions and is compared with some of the state-of-the-art algorithms. Experiments show that KGSA provides better results than the other algorithms in finding local and global optima of constrained and unconstrained multimodal functions.

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.

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.

Kinetic Gas Molecule Optimization (KGMO)

2018 ◽  
pp. 114-149
Keyword(s):  
High Performance ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Search Space ◽  
Kinetic Theory Of Gases ◽  
Swarm Optimization ◽  
Gas Molecule ◽  
Gas Molecules ◽  
Gravitational Search ◽  
Molecule Interactions

In this chapter, an optimization algorithm that is based on the kinetic energy of gas molecules, namely kinetic gas molecule optimization (KGMO), is introduced. This algorithm has some agents that are gas molecules, which move in the search space; these agents are subject to the kinetic theory of gases, which defines the rules for gas molecule interactions in the model. This algorithm has a good performance in terms of finding the global minima in 23 nonlinear benchmark functions, and the performance is compared with two other benchmark algorithms, namely particle swarm optimization (PSO) and the recently developed high-performance gravitational search algorithm (GSA).

scholarly journals Performance evaluation of black hole algorithm, gravitational search algorithm and particle swarm optimization

2015 ◽  
Vol 11 (1) ◽  
Author(s):  
Mohamad Nizam Aliman ◽  
Zuwairie Ibrahim ◽  
Fardila Naim ◽  
Sophan Wahyudi Nawawi ◽  
Shahdan Sudin
Keyword(s):  
Black Hole ◽  
Particle Swarm Optimization ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Particle Swarm ◽  
Search Space ◽  
Benchmark Dataset ◽  
Swarm Optimization ◽  
Gravitational Search ◽  
Black Hole Algorithm

Particle Swarm Optimization (PSO) and Gravitational Search Algorithm are a well known population-based heuristic optimization techniques. PSO is inspired from a motion flock of birds searching for a food. In PSO, a bird adjusts its position according to its own ‘‘experience’’ as well as the experience of other birds. Tracking and memorizing the best position encountered build bird’s experience which will leads to optimal solution. GSA is based on the Newtonian gravity and motion laws between several masses. In GSA, the heaviest mass presents an optimum solution in the search space. Other agents inside the population are attracted to heaviest mass and will finally converge to produce best solution. Black Hole Algorithm (BH) is one of the optimization technique recently proposed for data clustering problem. BH algorithm is inspired by the natural universe phenomenon called "black hole”. In BH algorithm, the best solution is selected to be the black hole and the rest of candidates which are called stars will be drawn towards the black hole. In this paper, performance of BH algorithm will be analyzed and reviewed for continuous search space using CEC2014 benchmark dataset against Gravitational Search Algorithm (GSA) and Particle Swarm Optimization (PSO). CEC2014 benchmark dataset contains 4 unimodal, 7 multimodal and 6 hybrid functions. Several common parameters has been chosen to make an equal comparison between these algorithm such as size of population is 30, 1000 iteration, 30 dimension and 30 times of experiment. 

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
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