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IAES International Journal of Artificial Intelligence (IJ-AI)

IJAI
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Published by Institute of Advanced Engineering and Science

ISSN : 20894872 EISSN : 22528938 DOI : -

Core Subject : Science, Engineering,

Computer Science & IT Engineering

IAES International Journal of Artificial Intelligence (IJ-AI) publishes articles in the field of artificial intelligence (AI). The scope covers all artificial intelligence area and its application in the following topics: neural networks; fuzzy logic; simulated biological evolution algorithms (like genetic algorithm, ant colony optimization, etc); reasoning and evolution; intelligence applications; computer vision and speech understanding; multimedia and cognitive informatics, data mining and machine learning tools, heuristic and AI planning strategies and tools, computational theories of learning; technology and computing (like particle swarm optimization); intelligent system architectures; knowledge representation; bioinformatics; natural language processing; multiagent systems; etc.

Arjuna Subject : -

Articles 1,722 Documents

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Optimal Placement of SVC Using Fuzzy and Firefly Algorithm P.SURESH BABU; P.B. CHENNAIAH; M. SREEHARI
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 4, No 4: December 2015
Publisher : Institute of Advanced Engineering and Science

Voltage stability is major phenomena in any power system network for reliability and continuity operation. But the tight operation of power system due to overloading or fault on the system which is evitable and major threat to the power system. So it is necessary to maintain the voltages within the constraints at the overloading conditions also by placing of Static VAR Compensator (SVC) at optimal locations. New approaches are used to find the placement and size of the SVC at different locations. Fuzzy is used to find the location and the size of the SVC is fined by the Firefly algorithm. This paper considers different loading conditions of the power system network (125,150,175over loading conditions). From the results we can conclude that the power losses are reduced and the voltages can be maintained within the limits .IEEE 14 bus, IEEE 30 bus system is taken for the implementing the above techniques.

Multi-Operator Genetic Algorithm for Dynamic Optimization Problems Al-khafaji Amen
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 6, No 3: September 2017
Publisher : Institute of Advanced Engineering and Science

Maintaining population diversity is the most notable challenge in solving dynamic optimization problems (DOPs). Therefore, the objective of an efficient dynamic optimization algorithm is to track the optimum in these uncertain environments, and to locate the best solution. In this work, we propose a framework that is based on multi operators embedded in genetic algorithms (GA) and these operators are heuristic and arithmetic crossovers operators. The rationale behind this is to address the convergence problem and to maintain the diversity. The performance of the proposed framework is tested on the well-known dynamic optimization functions i.e., OneMax, Plateau, Royal Road and Deceptive. Empirical results show the superiority of the proposed algorithm when compared to state-of-the-art algorithms from the literature.

Design and Implementation of Fuzzy Position Control System for Tracking Applications and Performance Comparison with Conventional PID Nader Jamali Soufi Amlashi
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 1, No 1: March 2012
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (4718.673 KB)

This paper was written to demonstrate importance of a fuzzy logic controller in act over conventional methods with the help of an experimental model. Also, an efficient simulation model for fuzzy logic controlled DC motor drives using Matlab/Simulink is presented. The design and real-time implementation on a microcontroller presented. The scope of this paper is to apply direct digital control technique in position control system. Two types of controller namely PID and fuzzy logic controller will be used to control the output response. The performance of the designed fuzzy and classic PID position controllers for DC motor is compared and investigated. Digital signal Microcontroller ATMega16 is also tested to control the position of DC motor. Finally, the result shows that the fuzzy logic approach has minimum overshoot, and minimum transient and steady state parameters, which shows the more effectiveness and efficiency of FLC than conventional PID model to control the position of the motor. Conventional controllers have poorer performances due to the non-linear features of DC motors like saturation and friction.DOI: http://dx.doi.org/10.11591/ij-ai.v1i1.409

Integrated Algorithm for Decreasing Active Power Loss Lenin Kanagasabai
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 7, No 1: March 2018
Publisher : Institute of Advanced Engineering and Science

This paper projects an Integrated Algorithm (IA) for solving optimal reactive power problem. Quick convergence of the Cuckoo Search (CS), the vibrant root change of the Firefly Algorithm (FA), and the incessant position modernization of the Particle Swarm Optimization (PSO) has been combined to form the Integrated Algorithm (IA). In order to evaluate the efficiency of the proposed Integrated Algorithm (IA), it has been tested in standard IEEE 57,118 bus systems and compared to other standard reported algorithms. Simulation results show that Integrated Algorithm (IA) is considerably reduced the real power loss and voltage profile within the limits.

A Multi-Agent Task Scheduling In University Environment Tariq Mahmood; M. Shahid Farid
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 1, No 4: December 2012
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (420.682 KB)

Task scheduling problems are involved in almost every field of life from industry, where scheduling of employees on different machines with different shifts with respect to various constraints, to universities where scheduling involved in time tabling of classes and faculty, in examination scheduling, laboratory scheduling, staff scheduling and so on. Scheduling problem involves scheduling of different resources under various constraints to attain optimal results. In this paper we present a multi-agent based solution to Task Scheduling Problem (TSP) in university environment. It involves two main scheduling problmes; first, time tabling probelm (TTP) and second examination scheduling problem (ESP). In time tabling problem, a time table of classes is consturcted subject to different constraints; like rooms, subjects, teachers, degrees and semester with in a degree program. in examination scheduling problem is central to scheduling issue to every university. In ESP, the schedule of the examination of different courses of different degrees invigilated by different faculty members each with his/her availability constraints, is carried out. The problem is even worse when students of different degrees takes a shared course and when there are add-drops students in a course. In this case, the complexity of the scheduling problem doubles, now scheduling has to done with respect to the constraints of faculty, degree and also to decrease the number of clashes in examination. An agent based solution to TSP is proposed in this paper which is also implemented and tested over different scenarios and optimal results are achieved in negligible amount of time.DOI: http://dx.doi.org/10.11591/ij-ai.v1i4.708

Overview of Model Free Adaptive (MFA) Control Technology Al Smadi Takialddin; Osman Ibrahim Al-Agha; Khalid Adnan Alsmadi
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 7, No 4: December 2018
Publisher : Institute of Advanced Engineering and Science

Model-Free Adaptive (MFA) control is a technology that has made a major impact on the automatic control industry. MFA control users have successfully solved many industry-wide control problems in various applications and achieved significant economic benefits. Now, the challenge is extending the many advantages of MFA control technology to diverse and fragmented markets, which could benefit from its unique capabilities. Since single-loop MFA controllers can directly replace legacy PID controllers without the need for "system" redesign (plugand play), they are readily embeddable in various instruments, equipment, and smart control valves. This alleviates concerns relative to cost of change and also makes MFA an appealing tool for OEM applications on a large scale.

Design of Multi-Criteria Spatial Decision Support System (MC-SDSS) for Animal Production Hesham Ahmed Hassan; Hazem Mokhtar El-Bakry; Hamada Gaber Abd Allah
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 2, No 3: September 2013
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (719.872 KB)

This paper presents a Multi-criteria spatial decision support system (MC-SDSS) as a tool for decision making and planning. MC-SDSS can be used to assess different criteria with different weights. We believe that such tool can be utilized to help policy/decision makers to improve animal production in Egypt. MC-SDSS facilitates the integration of the exploration and evaluation phases of the decision-making process in a transparent and interactive system that allows policy/decision makers to carry out the analyses without advanced geographical information system (GIS) or multiple criteria decision analysis (MCDA) training. We use weighted overlay method to support data spatial analysis, and then visualize and analyze different factors such as "Diseases", "Climate", "Veterinary care" and "Economical factors" which affect the animal production in Egypt. Policy/Decision makers can change their weights and parameters with this tool for their different study areas. Moreover they can use final suitability maps from this tool.DOI: http://dx.doi.org/10.11591/ij-ai.v2i3.1948

Shrinkage of real power loss by enriched brain storm optimization algorithm K. Lenin
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 8, No 1: March 2019
Publisher : Institute of Advanced Engineering and Science

This paper proposes Enriched Brain Storm Optimization (EBSO) algorithm is used for soving reactive power problem. Human being are the most intellectual creature in this world. Unsurprisingly, optimization algorithm stimulated by human being inspired problem solving procedure should be advanced than the optimization algorithms enthused by collective deeds of ants, bee, etc. In this paper, we commence a new Enriched brain storm optimization algorithm, which was enthused by the human brainstorming course of action. In the projected Enriched Brain Storm Optimization (EBSO) algorithm, the vibrant clustering strategy is used to perk up the kmeans clustering process. The most important view of the vibrant clustering strategy is that; regularly execute the k-means clustering after a definite number of generations, so that the swapping of information wrap all ideas in the clusters to accomplish suitable searching capability. This new approach leads to wonderful results with little computational efforts. In order to evaluate the efficiency of the proposed Enriched Brain Storm Optimization (EBSO) algorithm, has been tested standard IEEE 118 & practical 191 bus test systems and compared to other standard reported algorithms. Simulation results show that Enriched Brain Storm Optimization (EBSO) algorithm is superior to other algorithms in reducing the real power loss.

A Fast Genetic Algorithm for Solving University Scheduling Problem Mortaza Abbaszadeh; Saeed Saeedvand
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 3, No 1: March 2014
Publisher : Institute of Advanced Engineering and Science

University course timetabling is a NP-hard problem which is very difficult to solve by conventional methods, we know scheduling problem is one of the Nondeterministic Polynomial (NP) problems. This means, solving NP problems through normal algorithm is a time-consuming process (it takes days or months with available equipment) which makes it impossible to be solved through a normal algorithm like this. In purposed algorithm the problem of university class scheduling is solved through a new chromosome structure and modifying the normal genetic methods which really improves the solution in this case. We include lecturer, class and course information in presented algorithm, with all their Constraints, and it creates optimized scheduling table for weekly program of university after creating primary population of chromosomes and running genetic operators. In the final part of this paper we conclude from the results of input data analysis that the results have high efficiency compared with other algorithms considering maximum Constraints.

Multi-verse optimization based evolutionary programming technique for power scheduling in loss minimization scheme Muhamad Hazim Lokman; Ismail Musirin; Saiful Izwan Suliman; Hadi Suyono; Rini Nur Hasanah; Sharifah Azma Syed Mustafa; Mohamed Zellagui
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 8, No 3: September 2019
Publisher : Institute of Advanced Engineering and Science

The growth of computational intelligence technology has witnessed its application in numerous fields. Power system study is not left behind as far as computational intelligence trend is concerned. In power system community, optimization process is one of the crucial efforts for most remedial action to maintain the power system security. Basically, power scheduling refers to prior to fact action (such as scheduling generators to generate certain powers for next week). Power scheduling process is one of the most important routines in power systems. Scheduling of generators in a power transmission system is an important scheme; especially its offline studies to identify the security status of the system. This determines the cost effectiveness in power system planning. This paper investigates the performance of multi-verse based evolutionary programming (lowest EP) technique in the application of power system scheduling to ensure loss is gained by the system. Losses in the system can be controlled through this implementation which can be realized through the validation on a chosen reliability test system as the main model. Validation on IEEE 30-Bus Reliability Test System resulted that both techniques are reliable and robust in addressing this issue.

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Home Page

OAI Link

Editorial Team

Contact

Reviewer

Google Scholar

Contact Name
Imam Much Ibnu Subroto

Contact Email
imam@unissula.ac.id

Phone
-

Journal Mail Official
ijai@iaesjournal.com

Editorial Address
-

Location
Kota yogyakarta,

Daerah istimewa yogyakarta

INDONESIA

Abstract

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Home Page

OAI Link

Editorial Team

Contact

Reviewer

Google Scholar

Contact Name Imam Much Ibnu Subroto

Contact Email imam@unissula.ac.id

Phone -

Journal Mail Official ijai@iaesjournal.com

Editorial Address -

Location Kota yogyakarta, Daerah istimewa yogyakarta INDONESIA

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Contact Name
Imam Much Ibnu Subroto

Contact Email
imam@unissula.ac.id

Phone
-

Journal Mail Official
ijai@iaesjournal.com

Editorial Address
-

Location
Kota yogyakarta,

Daerah istimewa yogyakarta

INDONESIA