A Meta-Heuristic Search Algorithm based on Infrasonic Mating Displays in Peafowls

Solution Space ◽

Search Algorithms ◽

Heuristic Search Algorithm ◽

Intractable Problems ◽

Gravitational Search ◽

Optimization Search ◽

Meta-heuristic techniques are important as they are used to find solutions to computationally intractable problems. Simplistic methods such as exhaustive search become computationally expensive and unreliable as the solution space for search algorithms increase. As no method is guaranteed to perform better than all others in all classes of optimization search problems, there is a need to constantly find new and/or adapt old search algorithms. This research proposes an Infrasonic Search Algorithm, inspired from the Gravitational Search Algorithm and the mating behaviour in peafowls. The Infrasonic Search Algorithm identified competitive solutions to 23 benchmark unimodal and multimodal test functions compared to the Genetic Algorithm, Particle Swarm Optimization Algorithm and the Gravitational Search Algorithm.

Improving Accuracy of River Flow Forecasting Using LSSVR with Gravitational Search Algorithm

Advances in Meteorology ◽

10.1155/2017/2391621 ◽

2017 ◽

Vol 2017 ◽

pp. 1-23 ◽

Cited By ~ 21

Author(s):

Rana Muhammad Adnan ◽

Xiaohui Yuan ◽

Ozgur Kisi ◽

Rabia Anam

Keyword(s):

Prediction Accuracy ◽

River Flow ◽

Search Algorithm ◽

Flow Data ◽

Log Transformation ◽

Regression Methods ◽

Flow Prediction ◽

Gravitational Search ◽

River flow prediction is essential in many applications of water resources planning and management. In this paper, the accuracy of multivariate adaptive regression splines (MARS), model 5 regression tree (M5RT), and conventional multiple linear regression (CMLR) is compared with a hybrid least square support vector regression-gravitational search algorithm (HLGSA) in predicting monthly river flows. In the first part of the study, all three regression methods were compared with each other in predicting river flows of each basin. It was found that the HLGSA method performed better than the MARS, M5RT, and CMLR in river flow prediction. The effect of log transformation on prediction accuracy of the regression methods was also examined in the second part of the study. Log transformation of the river flow data significantly increased the prediction accuracy of all regression methods. It was also found that log HLGSA (LHLSGA) performed better than the other regression methods. In the third part of the study, the accuracy of the LHLGSA and HLGSA methods was examined in river flow estimation using nearby river flow data. On the basis of results of all applications, it was found that LHLGSA and HLGSA could be successfully used in prediction and estimation of river flow.

Weight Minimization of Helical Compression Spring Using Gravitational Search Algorithm (GSA)

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.773-774.277 ◽

2015 ◽

Vol 773-774 ◽

pp. 277-281 ◽

Cited By ~ 2

Author(s):

Noor Hafizah Amer ◽

Nurhidayati Ahmad ◽

Amar Faiz Zainal Abidin

Keyword(s):

Search Algorithm ◽

Pso Algorithm ◽

Optimization Method ◽

Standard Case ◽

Weight Minimization ◽

Compression Spring ◽

Parametric Studies ◽

Gravitational Search ◽

Compression spring is one of the most common mechanical componet being used in most mechanisms. Many criteria and constraints should be considered in designing and specifying the spring dimensions. Therefore, it has been one of the standard case studies considered to test a new optimisation algorithm. This paper introduced an optimization method named Gravitational search Algorithm (GSA) to solve the problem of weight minimization of spring. From previous studies, weight minimization of a spring has been investigated by many researcher using various optimization algorithm technique. The result of this study were compared to one of the previous studies using Particle Swarm Optimization (PSO) algorithm. Also, parametric studies were conducted to select the best values of GSA parameters, beta and epsilon. From the results obtained, it was observed that the optimum dimensions and weight obtained by GSA are better than the values obtained by PSO. The best values of beta and epsilon was found to be 0.6 and 0.01 respectively.

Interactive gravitational search algorithm and pattern search algorithms for practical dynamic economic dispatch

International Transactions on Electrical Energy Systems ◽

10.1002/etep.1961 ◽

2014 ◽

Vol 25 (10) ◽

pp. 2289-2309 ◽

Cited By ~ 10

Author(s):

Belkacem Mahdad ◽

Kamel Srairi

Keyword(s):

Search Algorithm ◽

Economic Dispatch ◽

Search Algorithms ◽

Pattern Search ◽

Gravitational Search ◽

Dynamic Economic Dispatch

Intelligent gravitational search random forest algorithm for fake news detection

International Journal of Modern Physics C ◽

10.1142/s012918312250084x ◽

2021 ◽

Author(s):

Rathika Natarajan ◽

Abolfazl Mehbodniya ◽

Kantilal Pitambar Rane ◽

Sonika Jindal ◽

Mohammed Faez Hasan ◽

...

Keyword(s):

Social Media ◽

Random Forest ◽

Search Algorithm ◽

Solution Space ◽

Automated System ◽

Local Optimum ◽

Fake News ◽

Online Social Media ◽

Gravitational Search

Online social media has made the process of disseminating news so quick that people have shifted their way of accessing news from traditional journalism and press to online social media sources. The rapid rotation of news on social media makes it challenging to evaluate its reliability. Fake news not only erodes public trust but also subverts their opinions. An intelligent automated system is required to detect fake news as there is a tenuous difference between fake and real news. This paper proposes an intelligent gravitational search random forest (IGSRF) algorithm to be employed to detect fake news. The IGSRF algorithm amalgamates the Intelligent Gravitational Search Algorithm (IGSA) and the Random Forest (RF) algorithm. The IGSA is an improved intelligent variant of the classical gravitational search algorithm (GSA) that adds information about the best and worst gravitational mass agents in order to retain the exploitation ability of agents at later iterations and thus avoid the trapping of the classical GSA in local optimum. In the proposed IGSRF algorithm, all the intelligent mass agents determine the solution by generating decision trees (DT) with a random subset of attributes following the hypothesis of random forest. The mass agents generate the collection of solutions from solution space using random proportional rules. The comprehensive prediction to decide the class of news (fake or real) is determined by all the agents following the attributes of random forest. The performance of the proposed algorithm is determined for the FakeNewsNet dataset, which has sub-categories of BuzzFeed and PolitiFact news categories. To analyze the effectiveness of the proposed algorithm, the results are also evaluated with decision tree and random forest algorithms. The proposed IGSRF algorithm has attained superlative results compared to the DT, RF and state-of-the-art techniques.

Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence ◽

Front-to-End Bidirectional Heuristic Search with Near-Optimal Node Expansions

10.24963/ijcai.2017/69 ◽

2017 ◽

Cited By ~ 5

Author(s):

Jingwei Chen ◽

Robert C. Holte ◽

Sandra Zilles ◽

Nathan R. Sturtevant

Keyword(s):

Heuristic Search ◽

Search Algorithm ◽

Optimal Solution ◽

Search Algorithms ◽

Worst Case ◽

Heuristic Search Algorithm ◽

Bidirectional Search ◽

Optimal Node ◽

Minimum Number ◽

It is well-known that any admissible unidirectional heuristic search algorithm must expand all states whose f-value is smaller than the optimal solution cost when using a consistent heuristic. Such states are called “surely expanded” (s.e.). A recent study characterized s.e. pairs of states for bidirectional search with consistent heuristics: if a pair of states is s.e. then at least one of the two states must be expanded. This paper derives a lower bound, VC, on the minimum number of expansions required to cover all s.e. pairs, and present a new admissible front-to-end bidirectional heuristic search algorithm, Near-Optimal Bidirectional Search (NBS), that is guaranteed to do no more than 2VC expansions. We further prove that no admissible front-to-end algorithm has a worst case better than 2VC. Experimental results show that NBS competes with or outperforms existing bidirectional search algorithms, and often outperforms A* as well.