Saiful Izwan Suliman

Artificial immune-based optimization technique for solving economic dispatch in power system

This paper presents an Artificial Immune-based optimization technique for solving the economic dispatch problem in a power system. The main role of electrical power utility is to ensure that electrical energy requirement from the customer is served. However in doing so, the power utility has also to ensure that the electrical power is generated with minimum cost. Hence, for economic operation of the system, the total demand must be appropriately shared among the generating units with an objective to minimize the total generation cost for the system. Economic Dispatch is a procedure to determine the electrical power to be generated by the committed generating units in a power system so that the total generation cost of the system is minimized, while satisfying the load demand simultaneously. The proposed technique implemented Clonal Selection algorithm with several cloning, mutation and selection approaches. These approaches were tested and compared in order to determine the best strategy for solving the economic dispatch problem. The feasibility of the proposed techniques was demonstrated on a system with 18 generating units at various loading conditions. The results show that Artificial Immune System optimization technique that employed adaptive cloning, selective mutation and pair-wise tournament selection has provided the best result in terms of cost minimization and least execution time. A comparative study with λ-iteration optimization method and Genetic Algorithm was also presented.

Artificial immune system based machine learning for voltage stability prediction in power system

Voltage instability has recently become a challenging problem for many power system operators. This phenomenon has been reported to be responsible for severe low voltage condition leading to major blackouts. This paper presents the application of Artificial Immune Systems (AIS) for online voltage stability evaluation that could be used as early warning system to the power system operator so that necessary action could be taken in order to avoid the occurrence of voltage collapse. Key features of the proposed method are the implementation of clonal selection principle that has the capability in performing pattern recognition task. The proposed technique was tested on the IEEE 30 bus power system and the results shows that fast performance with accurate evaluation for voltage stability condition has been obtained.

An effective AIS-based model for frequency assignment in mobile communication

Having an effective method for frequency assignment is very important in mobile communications. Artificial Immune Systems, a relatively new methodology for solving optimization-related problems, is investigated in this study to design a cellular frequency assignment model. In this framework, mobile hosts are allowed to continue interacting with the cells that they are currently in, even when the number of frequencies is insufficient. When the number of users increases, it is almost impossible to foresee future frequency demands in a cell. Often cellular networks are faced with insufficient frequencies in cells, whilst other cells have more frequencies available than they need. Our model works by allocating the unused frequencies from the cells with less demand to the overloaded cells. The model demonstrates that it manages to reuse the available free-conflict frequencies efficiently.

Artificial immune algorithm in solving the channel assignment task

This paper investigates the potential of Artificial Immune Systems (AIS) in solving the channel allocation problem in wireless communication. The main objective is to find the minimum number of channels to satisfy the demands of a network without violating any given constraints. The proposed methodology is applied to new datasets which were generated based on user requirements. These new datasets consist of two hundred problem instances, which vary from easy and small to complex and difficult problem settings. Comparisons with other methods, i.e. randomized-based heuristic and Genetic Algorithm, are carried out to evaluate the performance of the AIS on this problem. AIS is shown to be an efficient methodology for this problem.

IMLANNs for Congestion Management in Power System

Received Mar 3, 2018 Revised Apr 11, 2018 Accepted Apr 21, 2018 Paintball has gained a huge popularity in Malaysia with growing number of tournaments organized nationwide. Currently, Ideal Pro Event, one of the paintball organizer found difficulties to pair a suitable opponent to against one another in a tournament. This is largely due to the manual matchmaking method that only randomly matches one team with another. Consequently, it is crucial to ensure a balanced tournament bracket where eventual winners and losers not facing one another in the very first round. This study proposes an intelligent matchmaking using Particle Swarm Optimization (PSO) and tournament management system for paintball organizers. PSO is a swarm intelligence algorithm that optimizes problems by gradually improving its current solutions, therefore countenancing the tournament bracket to be continually improved until the best is produced. Indirectly, through the development of the system, it is consider as an intelligence business idea since it able to save time and enhance the company productivity. This algorithm has been tested using 3 size of population; 100, 1000 and 10,000. As a result, the speed of convergence is consistent and has not been affected through big population.N. N. S. Abdul Rahman, N.M. Saad, A. R. Abdullah, M. R. M. Hassan, M. S. S. M. Basir, N. S. M. Noor 1,2,4,6Faculty of Electronic and Computer Engineering, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, 76100 Durian Tunggal, Melaka, Malaysia 2,3Center for Robotics and Industrial Automation, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, 76100 Durian Tunggal, Melaka, Malaysia 3,5Faculty of Electrical Engineering, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, 76100 Durian Tunggal, Melaka, MalaysiaLight rail transit (LRT), or fast tram is urban public transport using rolling stock similar to a tramway, but operating at a higher capacity, and often on an exclusive right-of-way. Indonesia as one of developing countries has been developed the LRT in two cities of Indonesia, Palembang and Jakarta. There are opinions toward the development of LRT, negative and positive opinions. To reveal the level of LRT development acceptance, this research uses machine learning approach to analyze the data which is gathered through social media. By conducting this paper, the data is modeled and classified in order to analyze the social sentiment towards the LRT development.Mohamad, S., Nasir, F.M., Sunar, M.S., Isa, K., Hanifa, R.M., Shah, S.M., Ribuan, M.N., Ahmad, A. 1,4,6,7,8Faculty of Electrical and Electronic Engineering, Universiti Tun Hussein Onn Malaysia, Johor, Malaysia 1,2,3UTM-IRDA Digital Media Centre, Media and Game Innovation Centre of Excellence, Universiti Teknologi Malaysia, Johor, Malaysia 1,2,3Faculty of Computing, Universiti Teknologi Malaysia, Johor, Malaysia 5Centre for Diploma Studies, Universiti Tun Hussein Onn Malaysia, Johor, Malaysia 6Research Centre for Applied Electromagnetics, Universiti Tun Hussein Onn Malaysia, Johor, MalaysiaReceived Jan 31, 2018 Revised Apr 21, 2018 Accepted Apr 30, 2018 Bluetooth is an emerging mobile ad-hoc network that accredits wireless communication to connect various short range devices. A single hop network called piconet is the basic communication topology of bluetooth which allows only eight active devices for communication among them seven are active slaves controlled by one master. Multiple piconets are interconnected through a common node, known as Relay, to form a massive network called as Scatternet. It is obvious that the performance of Scatternet scheduling is highly dependent and directly proportionate with the performance of the Relay node. In contrary, by reducing the number of Relays, it may lead to poor performance, since every Relay has to perform and support several piconet connections. The primary focus of this study is to observe the performance metrics that affects the inter-piconet scheduling since the Relay node’s role is like switch between multiple piconets. In this paper, we address and analyze the performance issues to be taken into consideration for efficient data flow in Scatternet based on Relay node.

An efficient constructive heuristic for optimizing frequency allocation in cellular network

Managing resources in wireless communication is a crucial task to ensure demand is satisfied at a minimal operational cost. This includes managing constraints such as co-frequency, adjacent frequency and co-site so that interference is avoided. Any occurrence of interference will affect the quality of communication conducted between parties involved. Therefore, a good and efficient method is required to achieve this objective. In this paper, a simple randomized constructive heuristic was investigated for good quality solutions. The proposed method was utilized on new problem instances; varying in difficulties and complexity environment. Two hundred problem instances were generated for this purpose. The results obtained demonstrate the efficiency of the proposed method on this problem domain.

Implementing immune-based algorithm for bandwidth spectrum allocation in wireless communication

Having an effective technique for bandwidth allocation is very important in wireless communications. Artificial Immune Systems, a relatively new meta-heuristic for solving optimization-related problems, is investigated in this study to design a cellular channel allocation framework. In this study, mobile hosts are allowed to continue interacting with the cells that they are currently in, even when the number of channel is insufficient. When the number of mobile hosts increases, it is almost impossible to foresee future channel demands in a wireless network. Often cellular networks are faced with insufficient channels in cells, whilst other cells have more channels available than they need. Our model works by allocating the unused channels from the cells with less demand to the overloaded cells. The model demonstrates that it manages to re-use the available free-conflict channels efficiently. Comparisons with other existing method (i.e. genetic algorithm) were conducted to evaluate the performance of our model.

A permutation based technique for channel assignment problem

Channel assignment problem is an example of NP-complete problem in which the complexity and level of difficulty depends on the input parameters of a particular condition. Therefore, a good technique with an efficient optimization approach will produce a good assignment if not optimum. This paper proposes the use of permutation technique adopted from evolutionary strategy in producing various number of cell orders for channel assignment task. Subject to three different constraints, the objective is to find the minimum number of channels used in order to satisfy various numbers of demands from each cell. The proposed algorithm was tested on standard benchmark problems and the results obtained show the effectiveness of a simple yet efficient algorithm with the help of an improved shift mutation technique that are normally applied in evolutionary strategy.