scholarly journals Comment on “An Investigation into the Performance of Particle Swarm Optimization with Various Chaotic Maps”

2015 ◽  
Vol 2015 ◽  
pp. 1-3
Author(s):  
Yudong Zhang ◽  
Genlin Ji ◽  
Zhengchao Dong ◽  
Shuihua Wang ◽  
Preetha Phillips
Keyword(s):  
Particle Swarm Optimization ◽  
Chaotic Maps ◽  
Particle Swarm ◽  
Gauss Map ◽  
Floating Point ◽  
Swarm Optimization ◽  
The Difference ◽  
Definition Of

This paper researched three definitions of Gauss map and found that the definition of “Gauss map” in the paper of Arasomwan and Adewumi may be incoherent with other publications. In addition, we analyzed the difference of continuous Gauss map and the floating-point Gauss map, and we pointed out that the floating-point simulation behaved significantly differently from the continuous Gauss map.

scholarly journals Developing an approach for decision making units integration using multi objective particle swarm optimization

2014 ◽  
Vol 4 (1) ◽  
pp. 48 ◽  
Author(s):  
Abdorrahman Haeri ◽  
Kamran Rezaie ◽  
Seyed Morteza Hatefi
Keyword(s):  
Decision Making ◽  
Particle Swarm Optimization ◽  
Performance Measures ◽  
Particle Swarm ◽  
Final Decision ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Decision Making Unit ◽  
The Difference ◽  
Decision Making Units

In recent years, integration between companies, suppliers or organizational departments attracted much attention. Decision making about integration encounters with major concerns. One of these concerns is which units should be integrated and what is the effect of integration on performance measures. In this paper the problem of decision making unit (DMU) integration is considered. It is tried to integrate DMUs so that the considered criteria are satisfied. In this research two criteria are considered that are mean of efficiencies of DMUs and the difference between DMUs that have largest and smallest efficiencies. For this purpose multi objective particle swarm optimization (MOPSO) is applied. A case with 17 DMUs is considered. The results show that integration has increased both considered criteria effectively.  Additionally this approach can presents different alternatives for decision maker (DM) that enables DM to select the final decision for integration.

scholarly journals A Novel Power Losses Reduction Method Based on a Particle Swarm Optimization Algorithm Using STATCOM

Energies ◽  
2018 ◽  
Vol 11 (10) ◽  
pp. 2851 ◽  
Author(s):  
Valeriya Tuzikova ◽  
Josef Tlusty ◽  
Zdenek Muller
Keyword(s):  
Particle Swarm Optimization ◽  
Power Flow ◽  
Particle Swarm ◽  
Electric Power Industry ◽  
Main Function ◽  
Power Losses ◽  
Swarm Optimization ◽  
Flexible Ac Transmission ◽  
Definition Of ◽  
Ac Transmission

In the modern electric power industry, Flexible AC Transmission Systems (FACTS) have a special place. In connection with the increased interest in the development of “smart energy”, the use of such devices is becoming especially urgent. Their main function is the ability to manage modes in real time: maintain the necessary level of voltage in the grids, control the power flow, increase the capacity of power lines and increase the static and dynamic stability of the power grid. The problem of system reliability and stability is related to the task of definitions and optimizations and planning indicators, design and exploitation. The main aim of this article is the definition of the best placement of the STATCOM compensator in case to provide stability and reliability of the grid with the minimization of the power losses, using Particle Swarm Optimization algorithms. All calculations were performed in MATLAB.

scholarly journals An Investigation into the Performance of Particle Swarm Optimization with Various Chaotic Maps

2014 ◽  
Vol 2014 ◽  
pp. 1-17 ◽  
Author(s):  
Akugbe Martins Arasomwan ◽  
Aderemi Oluyinka Adewumi
Keyword(s):  
Particle Swarm Optimization ◽  
Chaotic Maps ◽  
Chaotic Map ◽  
Logistic Map ◽  
Particle Swarm ◽  
Test Problems ◽  
Swarm Optimization ◽  
Inertia Weight ◽  
Mathematical Problems ◽  
Logistic Chaotic Map

This paper experimentally investigates the effect of nine chaotic maps on the performance of two Particle Swarm Optimization (PSO) variants, namely, Random Inertia Weight PSO (RIW-PSO) and Linear Decreasing Inertia Weight PSO (LDIW-PSO) algorithms. The applications of logistic chaotic map by researchers to these variants have led to Chaotic Random Inertia Weight PSO (CRIW-PSO) and Chaotic Linear Decreasing Inertia Weight PSO (CDIW-PSO) with improved optimizing capability due to better global search mobility. However, there are many other chaotic maps in literature which could perhaps enhance the performances of RIW-PSO and LDIW-PSO more than logistic map. Some benchmark mathematical problems well-studied in literature were used to verify the performances of RIW-PSO and LDIW-PSO variants using the nine chaotic maps in comparison with logistic chaotic map. Results show that the performances of these two variants were improved more by many of the chaotic maps than by logistic map in many of the test problems. The best performance, in terms of function evaluations, was obtained by the two variants using Intermittency chaotic map. Results in this paper provide a platform for informative decision making when selecting chaotic maps to be used in the inertia weight formula of LDIW-PSO and RIW-PSO.

Comparison of evolutionary techniques for the optimization of machining fixture layout under dynamic conditions

2017 ◽  
Vol 232 (12) ◽  
pp. 2145-2158 ◽  
Author(s):  
M Sabareeswaran ◽  
KP Padmanaban ◽  
KA Sundararaman
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Natural Frequency ◽  
End Milling ◽  
Excitation Frequency ◽  
Particle Swarm ◽  
Machining Accuracy ◽  
Swarm Optimization ◽  
Fixture Layout ◽  
The Difference

Modern manufacturing industries are striving to improve the machining accuracy and productivity to reduce the rejection rate and unit cost of the machined parts. The properly designed fixture layout enables the designer to minimize the vibration so that the requisite machining accuracy can be achieved. During machining, especially in end milling, the intermittent engagement of multitooth cutter induces vibration on the workpiece. When the excitation frequency of multitooth cutter coincides with any one of the natural frequencies of the fixtured workpiece, it leads to the condition of resonance. The vibration increases under these circumstances, which degrades the machining accuracy and surface finish of the machined workpiece. Hence, the issues related to the design of fixture layout are to be addressed by recognizing the dynamic behavior of the fixture–workpiece system. In this research paper, finite element method is utilized to simulate the end milling operation and to determine the natural frequency of the workpiece. The main focus is to maximize the difference between natural frequency of the fixtured workpiece and excitation frequency of the cutter to minimize the vibration on the workpiece. Two different evolutionary techniques genetic algorithm and particle swarm optimization are employed to maximize the difference between these frequencies by optimizing the machining fixture layout. The performance of genetic algorithm and particle swarm optimization on the fixture layout optimization is compared. The comparison of results concludes that particle swarm optimization is the most appropriate approach than the genetic algorithm in achieving the better results.

A Discrete Particle Swarm Optimization Approach to Optimize the Assembly Sequence of Mechanical Product

2012 ◽  
Vol 490-495 ◽  
pp. 203-207
Author(s):  
Zhong Bo Zhang ◽  
Chuan Yong Huang
Keyword(s):  
Particle Swarm Optimization ◽  
Fitness Function ◽  
Particle Swarm ◽  
Assembly Sequence Planning ◽  
Optimization Approach ◽  
Assembly Sequence ◽  
Swarm Optimization ◽  
Discrete Pso ◽  
The Difference ◽  
Assembly Tool

The aim of assembly sequence planning (ASP) is to achieve the best assembly sequence which assembly cost and time used is less. The geometrical feasibility of an assembly sequence is validated by the interference matrix of the product. The number of assembly tool changes and the number of assembly operation type changes are considered in the fitness function. To establish the mapping relation between ASP and particle swarm optimization (PSO) approach, some definitions of position, velocity and operator of particles are proposed. The difference of the proposed discrete PSO (DPSO) algorithm with the other algorithm is the emphasis on the geometrical feasibility of the assembly sequence. The geometrical feasibility is verified at the first and the every iteration. The performance and feasibility of the proposed algorithm is verified via a simplified engine assembly case.

scholarly journals Deep Loving - The Friend of Deep Learning

2020 ◽  
pp. 51-53
Author(s):  
Satish Gajawada ◽  
Hassan M. H. Mustafa
Keyword(s):  
Artificial Intelligence ◽  
Particle Swarm Optimization ◽  
Deep Learning ◽  
Literature Review ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Swarm Optimization ◽  
Definition Of

Artificial Intelligence and Deep Learning are good fields of research. Recently, the brother of Artificial Intelligence titled "Artificial Satisfaction" was introduced in literature [10]. In this article, we coin the term “Deep Loving”. After the publication of this article, "Deep Loving" will be considered as the friend of Deep Learning. Proposing a new field is different from proposing a new algorithm. In this paper, we strongly focus on defining and introducing "Deep Loving Field" to Research Scientists across the globe. The future of the "Deep Loving" field is predicted by showing few future opportunities in this new field. The definition of Deep Learning is shown followed by a literature review of the "Deep Loving" field. The World's First Deep Loving Algorithm (WFDLA) is designed and implemented in this work by adding Deep Loving concepts to Particle Swarm Optimization Algorithm. Results obtained by WFDLA are compared with the PSO algorithm.

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