scholarly journals A Novel Geo-Inspired Earthquake Optimization Algorithm

2018 ◽  
Author(s):  
Efrain Mendez ◽  
Alexandro Ortiz ◽  
Pedro Ponce ◽  
Arturo Molina
Keyword(s):  
Dynamic Balance ◽  
Engineering Application ◽  
Bat Algorithm ◽  
Search Space ◽  
Optimization Method ◽  
The Novel ◽  
Exploration And Exploitation ◽  
Swarm Optimization ◽  
The Earth ◽  
Early Results

A novel metaheuristic optimization method is proposed based on an earthquake that is a geology phenomenon. The novel Earthquake Algorithm (EA) proposed, adapts the principle of propagation of geology waves P and S through the earth material composed by random density to ensure the dynamic balance between exploration and exploitation, in order to reach the best solution to optimization complex problems by searching for the optimum into the search space. The performance and validation of the EA are compared against the Bat Algorithm (BA) and the Particle Swarm Optimization (PSO) by using 10 diverse benchmark functions. In addition, an experimental engineering application is implemented to evaluate the proposed algorithm. Early results show a feasibility of the proposed method with a clearly constancy and stability. It is important highlight the fact that the main purpose of this paper is to present a new line of research, which is opened from the novel EA.

scholarly journals Random adjustment - based Chaotic Metaheuristic algorithms for image contrast enhancement

2017 ◽  
Vol 10 (2) ◽  
pp. 67
Author(s):  
Vina Ayumi ◽  
L.M. Rasdi Rere ◽  
Mohamad Ivan Fanany ◽  
Aniati Murni Arymurthy
Keyword(s):  
Contrast Enhancement ◽  
Harmony Search ◽  
Search Space ◽  
Optimization Method ◽  
Large Solution ◽  
Swarm Optimization ◽  
Image Contrast Enhancement ◽  
Simulation Results ◽  
Better Than

Metaheuristic algorithm is a powerful optimization method, in which it can solve problemsby exploring the ordinarily large solution search space of these instances, that are believed tobe hard in general. However, the performances of these algorithms signicantly depend onthe setting of their parameter, while is not easy to set them accurately as well as completelyrelying on the problem's characteristic. To ne-tune the parameters automatically, manymethods have been proposed to address this challenge, including fuzzy logic, chaos, randomadjustment and others. All of these methods for many years have been developed indepen-dently for automatic setting of metaheuristic parameters, and integration of two or more ofthese methods has not yet much conducted. Thus, a method that provides advantage fromcombining chaos and random adjustment is proposed. Some popular metaheuristic algo-rithms are used to test the performance of the proposed method, i.e. simulated annealing,particle swarm optimization, dierential evolution, and harmony search. As a case study ofthis research is contrast enhancement for images of Cameraman, Lena, Boat and Rice. Ingeneral, the simulation results show that the proposed methods are better than the originalmetaheuristic, chaotic metaheuristic, and metaheuristic by random adjustment.

scholarly journals Research on Dynamic Balance of Spindle Rotor System Based on Particle Swarm Optimization

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Zhan Wang ◽  
Dongzheng Li ◽  
Zinan Wang ◽  
Aoxiang Liu ◽  
Ruiyao Tao
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Model ◽  
Dynamic Balance ◽  
Particle Swarm ◽  
Optimization Method ◽  
Rotor System ◽  
Mode Shapes ◽  
Swarm Optimization ◽  
Nonlinear Dynamic Characteristics ◽  
System Mode

The dynamic balance is a significant issue for the nonlinear dynamic characteristics of the spindle rotor system. However, there is a problem that the dynamic balance is lacking detailed study on optimization method. In the paper, a modal dynamic balance optimization model of spindle rotor system is proposed, which can intend to improve the accuracy of spindle rotor system modal dynamic balance. Because the multiorder unbalance components are the main spindle rotor system mode shapes, the particle swarm optimization (PSO) method is adopted. The sum of squares of residual vibration after balancing is taken as the optimization objective, and the correction is presented as the optimization variable in the optimization model. The optimal correction weight of every unbalance component is calculated through a modal matrix equation of PSO. The vibration amplitude that is greatly reduced after optimization balance is presented under different conditions. The balancing effect shows a better dynamic characteristic than that of traditional methods. And the fluctuation range of the axis track of the rotor system also shows reductive phenomenon. The proposed optimization spindle rotor system model is well verified through experiments. It can contribute a theoretical optimization foundation for available dynamic balance in spindle rotor system.

A Novel Behavior Fusion Method for Intelligent Vehicle Routing Planning

2014 ◽  
Vol 644-650 ◽  
pp. 363-366
Author(s):  
Hua Jin ◽  
Tao Ning ◽  
Bo Yin
Keyword(s):  
Quantum Particle ◽  
Phase Particle ◽  
Particle Swarm ◽  
Search Space ◽  
Experimental Result ◽  
External Information ◽  
The Novel ◽  
Swarm Optimization ◽  
Quantum Particle Swarm Optimization ◽  
Novel Method

In order to improve the convergence of VRP, a novel method is proposed on the basis of behavior fusion and quantum particle swarm optimization (QPSO).The external information which has been obtained through multi-sensor is divided into several sub-phase particle swarm according to the character of optimal variable. It can be seen from the experimental result that the novel QPSO can enhance the security of obstacle avoidance and improve the convergence reliability and convergence speed in the high-dimensional search space. Finally, the proposed method is compared with the existing algorithms and the results verified its effectiveness.

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.

PSO-based group-oriented crow search algorithm (PGCSA)

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Sudeepa Das ◽  
Tirath Prasad Sahu ◽  
Rekh Ram Janghel
Keyword(s):  
Design Methodology ◽  
Search Algorithm ◽  
Search Space ◽  
Benchmark Problems ◽  
Exploration And Exploitation ◽  
Swarm Optimization ◽  
Content Type ◽  
Engineering Problems ◽  
Novel Approaches ◽  
Two Phases

Purpose The purpose of this paper is to modify the crow search algorithm (CSA) to enhance both exploration and exploitation capability by including two novel approaches. The positions of the crows are updated in two approaches based on awareness probability (AP). With AP, the position of a crow is updated by considering its velocity, calculated in a similar fashion to particle swarm optimization (PSO) to enhance the exploiting capability. Without AP, the crows are subdivided into groups by considering their weights, and the crows are updated by conceding leaders of the groups distributed over the search space to enhance the exploring capability. The performance of the proposed PSO-based group-oriented CSA (PGCSA) is realized by exploring the solution of benchmark equations. Further, the proposed PGCSA algorithm is validated over recently published algorithms by solving engineering problems. Design/methodology/approach In this paper, two novel approaches are implemented in two phases of CSA (with and without AP), which have been entitled the PGCSA algorithm to solve engineering benchmark problems. Findings The proposed algorithm is applied with two types of problems such as eight benchmark equations without constraint and six engineering problems. Originality/value The PGCSA algorithm is proposed with superior competence to solve engineering problems. The proposed algorithm is substantiated hypothetically by using a paired t-test.

scholarly journals Embedding parts in shape grammars using a parallel particle swarm optimization method on graphics processing units

2018 ◽  
Vol 32 (3) ◽  
pp. 256-268 ◽  
Author(s):  
Hacer Yalim Keles
Keyword(s):  
Particle Swarm Optimization ◽  
Graphics Processing Units ◽  
Particle Swarm ◽  
Search Space ◽  
Optimization Method ◽  
Processing Unit ◽  
Shape Grammars ◽  
Swarm Optimization ◽  
Parallel Particle Swarm Optimization ◽  
Graphics Processing

AbstractEmbedding emergent parts in shape grammars is computationally challenging. The first challenge is the representation of shapes, which needs to enable reinterpretation of parts regardless of the creation history of the shapes. The second challenge is the relevant part searching algorithm that provides an extensive exploration of the design space–time efficiently. In this work, we propose a novel method to solve both problems; we treat shapes as they are and use a parallel particle swarm optimization-based algorithm to compute emergent parts. The execution time of the proposed method is improved substantially by dividing the search space into small parts and carrying out searches in each part concurrently using a graphics processing unit. The experiments show that the proposed implementation detects emergent parts accurately and time efficiently.

scholarly journals Dynamic Neighborhood-Based Particle Swarm Optimization for Multimodal Problems

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Xu-Tao Zhang ◽  
Biao Xu ◽  
Wei Zhang ◽  
Jun Zhang ◽  
Xin-fang Ji
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Search Space ◽  
Multimodal Optimization ◽  
Exploration And Exploitation ◽  
Swarm Optimization ◽  
Benchmark Instances ◽  
Multimodal Problems

Various black-box optimization problems in real world can be classified as multimodal optimization problems. Neighborhood information plays an important role in improving the performance of an evolutionary algorithm when dealing with such problems. In view of this, we propose a particle swarm optimization algorithm based on dynamic neighborhood to solve the multimodal optimization problem. In this paper, a dynamic ε-neighborhood selection mechanism is first defined to balance the exploration and exploitation of the algorithm. Then, based on the information provided by the neighborhoods, four different particle position updating strategies are designed to further support the algorithm’s exploration and exploitation of the search space. Finally, the proposed algorithm is compared with 7 state-of-the-art multimodal algorithms on 8 benchmark instances. The experimental results reveal that the proposed algorithm is superior to the compared ones and is an effective method to tackle multimodal optimization problems.

Diversity and Mechanisms in Swarm Intelligence

2014 ◽  
Vol 5 (2) ◽  
pp. 1-12 ◽  
Author(s):  
Xin-She Yang
Keyword(s):  
Swarm Intelligence ◽  
Review Paper ◽  
Bat Algorithm ◽  
Cuckoo Search ◽  
Parameter Tuning ◽  
Parameter Control ◽  
Exploration And Exploitation ◽  
Swarm Optimization ◽  
Open Questions ◽  
New Algorithms

Swarm intelligence based algorithms such as particle swarm optimization have become popular in the last two decades. Various new algorithms such as cuckoo search and bat algorithm also show promising efficiency. In all these algorithms, it is essential to maintain the balance of exploration and exploitation by controlling directly and indirectly the diversity of the population. Different algorithms may use different mechanisms to control such diversity. In this review paper, the author reviews and analyzes the roles of diversity and relevant mechanisms in swarm intelligence. The author also discuss parameter tuning and parameter control. In addition, the author highlights some key open questions in swarm intelligence.

Sistem Kontrol Optimal Fuzzy-Particle Swarm Optimization

2018 ◽  
Vol 9 (1) ◽  
pp. 1-5
Author(s):  
Fachrudin Hunaini ◽  
Imam Robandi ◽  
Nyoman Sutantra
Keyword(s):  
Fuzzy Logic ◽  
Particle Swarm Optimization ◽  
Control System ◽  
Membership Function ◽  
Fuzzy Logic Control ◽  
Particle Swarm ◽  
Optimization Method ◽  
Swarm Optimization ◽  
Motion Error ◽  
Logic Control

Fuzzy Logic Control (FLC) is a reliable control system for controlling nonlinear systems, but to obtain optimal fuzzy logic control results, optimal Membership Function parameters are needed. Therefore in this paper Particle Swarm Optimization (PSO) is used as a fast and accurate optimization method to determine Membership Function parameters. The optimal control system simulation is carried out on the automatic steering system of the vehicle model and the results obtained are the vehicle's lateral motion error can be minimized so that the movement of the vehicle can always be maintained on the expected trajectory

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