Transitional Particle Swarm Optimization

A new variation of particle swarm optimization (PSO) termed as transitional PSO (T-PSO) is proposed here. T-PSO attempts to improve PSO via its iteration strategy. Traditionally, PSO adopts either the synchronous or the asynchronous iteration strategy. Both of these iteration strategies have their own strengths and weaknesses. The synchronous strategy has reputation of better exploitation while asynchronous strategy is stronger in exploration. The particles of T-PSO start with asynchronous update to encourage more exploration at the start of the search. If no better solution is found for a number of iteration, the iteration strategy is changed to synchronous update to allow fine tuning by the particles. The results show that T-PSO is ranked better than the traditional PSOs

Awareness and Readiness of Malaysian University Students for Emotion Recognition System

Emotion Recognition System (ERS) identifies human emotion like happiness, sadness, anger, disgust and fear. These emotions can be detected via various modalities such as facial expression analysis, voice intonation, and physiological signals like the brain’s electroencephalogram (EEG) and heart’s electrocardiogram (ECG).  The emotion recognition system allows machines to recognized human emotions and reacts to it. It offers broad areas of application, from smart home automation to entertainment recommendation system to driving assistance and to automated security system. It is a promising and interesting field to be explored especially as we are moving towards industrial revolution 5.0. Therefore, a survey was conducted on the awareness and readiness of the usage of emotion recognition system among Malaysian youths, specifically among university students. The findings are presented here. Overall, positive orientation towards the technology is observed among the participants and they are ready for its adoptio

Ant-colony and nature-inspired heuristic models for NOMA systems: a review

The increasing computational complexity in scheduling the large number of users for non-orthogonal multiple access (NOMA) system and future cellular networks lead to the need for scheduling models with relatively lower computational complexity such as heuristic models. The main objective of this paper is to conduct a concise study on ant-colony optimization (ACO) methods and potential nature-inspired heuristic models for NOMA implementation in future high-speed networks. The issues, challenges and future work of ACO and other related heuristic models in NOMA are concisely reviewed. The throughput result of the proposed ACO method is observed to be close to the maximum theoretical value and stands 44% higher than that of the existing method. This result demonstrates the effectiveness of ACO implementation for NOMA user scheduling and grouping

An oppositional learning prediction operator for simulated kalman filter

Simulated Kalman filter (SKF) is a recent metaheuristic optimization algorithm established in 2015. In the present study, we introduce a prediction operator in SKF to prolong its exploration and to avoid premature convergence. The proposed prediction operator is based on oppositional learning. The results show that using CEC2014 as benchmark problems, the SKF algorithm with oppositional learning prediction operator outperforms the original SKF algorithm in most cases

Radar performance analysis in the presence of sea clutter

Radar has been used for decades for surveillance purposes, originally meant for target detection and early warning. During the early days, radar detector has been developed by assuming the radar clutter is Gaussian distributed. However, as modern technology emerges, the radar distribution is seen to deviates from the Gaussian assumption. Thus, detectors designed based on Gaussian assumption are no longer optimum for detection in non-Gaussian nature. Lots of researches have been carried out for optimum target detection in non-Gaussian clutter distributions. Neyman- Pearson detector is proven to be the best detector for radar detection due to the unknown cost and prior probabilities. The theory of target detection in Gaussian distributed clutter has been well established and the closed form of the detection performances can be easily obtained. However, that is not the case in non-Gaussian clutter distributions. Thus, this thesis aims to serve as a basis in understanding performance analysis of target detection in the presence of sea clutter. In the thesis, the performance model in terms of ROC plots of probability of detection against signal to noise ratio for different sea clutter distributions are obtained and analyzed

Simulated Kalman Filter algorithms for solving optimization problems

Optimization is an important process in solving most engineering problems. Unfortunately, many practical optimization problems cannot be solved to optimality within reasonable computational effort. Optimization in drill path for example, can lead to a significant time reduction in the overall manufacturing process, thus reducing a significant amount of total production costs. Reduction of the total travelling time of the drilling machine in particular, is the most crucial issue in large production of electronics manufacturing industries involving printed circuit board (PCB). When the exact solution is not an option or probably unnecessary, one may use metaheuristic approach to obtain a near-optimal solution in some reasonable computational time. In this research, two novel estimation-based metaheuristic optimization algorithms, named as Simulated Kalman Filter (SKF), and single-solution Simulated Kalman Filter (ssSKF) algorithms are introduced for global optimization problems. These algorithms are inspired by the estimation capability of the well-known Kalman filter estimation method. Kalman filter, named after its developer, is a very rare algorithm that is provable to be an optimal linear Gaussian estimator. Its optimality has inspired the development of a metaheuristic algorithm called Heuristic Kalman Algorithm (HKA) in 2009. Applications and improvements to the HKA algorithm suggest that optimization algorithm based on estimation principle has a huge potential in solving a wide variety of optimization problems. However, the HKA algorithm has its own flaws. Although it was introduced as a population-based stochastic optimization algorithm, HKA is not exactly a population-based algorithm because it initializes and updates only a single solution. The computation in HKA also becomes expensive when dealing with high dimension. Last but not least, HKA has a very high dependency on the Gaussian assumption. The proposed population-based SKF algorithm and the single solution-based SKF algorithm use the scalar model of discrete Kalman filter algorithm as the search strategy to overcome these flaws. In principle, the optimization problem is regarded as a state estimation process. Each agent acts as a Kalman filter and finds solution to the optimization problem using a standard Kalman Filter framework which comprises of prediction, simulated measurement, and estimation phase that uses the best-so-far solution as a reference. The algorithms are evaluated using 30 benchmark functions of the CEC2014 benchmark suite, and then applied to solve PCB drill path optimization case study. The Wilcoxon signed ranked statistical test shows that the ssSKF algorithm that uses an adaptive local neighbourhood in the prediction phase performs statistically better than the SKF algorithm that uses the last estimated state as its prediction, especially in solving high dimensional functions. Benchmarking with recent algorithms tested on the CEC2014 benchmark suite shows that all compared algorithms perform statistically on par considering their average performance. The Friedman test ranked ssSKF and SKF algorithm in the third and fourth rank respectively when they are being benchmarked against three state-of-the-art algorithms that competed in the CEC2014 competition. In the benchmarking of the SKF and ssSKF algorithms’ performance in solving the 14-hole PCB drill path optimization case study with recent implementations, on average, both algorithms show the ability to converge to the optimal solution at a smaller number of function evaluations compared to the Gravitational Search Algorithm (GSA), Cuckoo Search (CS), and Intelligent Water Drop (IWD), although fall-short to the Taguchi- Genetic Algorithm optimization algorithm

A binder additional process in urea granule fertilizer by using adaptive fuzzy logic control

This paper presents the effect of binder feed rate addition towards ammonia gas released during urea granulation process. The binder feed rate as manipulated variable with few other constant parameters such as pressure and temperature of fluidized bed granulator. These parameters, binder flow rate and NH3 emission were used to indicate the function ability of the designated fuzzy logic. The performance index of this study is then defined with percentage error from experimental value and actual value. An adaptive Fuzzy Logic Controller (FLC) is proposed to control the system conditions closed to the reference values. As binder flow rate increases the higher is the emission of NH3. The average of error percentage for whole project was 6.91%. The highest and lowest error in percentages are 81.5 and 0 respectively. The result shows that the proposed method can be efficiently implemented in the real-time determination and control of optimal conditions for granulation processes with efficient energy and to minimize the amount of ammonia gas (NH3) release to the environment

Residential Comfort Index Maximization Using Simulated Kalman Filter Algorithms

Comfort has always been one of the main aspects researchers focus on in building smart homes. Thermal comfort, visual comfort, and air quality are the parameters of most interest. This paper proposes two Simulated Kalman Filter (SKF) algorithms to maximize the residential comfort index based on these three parameters. A population-based SKF and a single solution-based SKF. A dataset consisting of 48 environmental values is used for this purpose. The performance of the Simulated Kalman Filter algorithms is benchmarked against the Artificial Bee Colony algorithm, Firefly Algorithm, Genetic Algorithm, and Ant Colony Optimization algorithm. Friedman and Holm's statistical analysis shows that both algorithms outperformed others by quite a significant gap. Furthermore, the single solution-based SKF performed better than the original population-based SKF algorithm despite converging slower. In summary, Simulated Kalman Filter algorithms have proven to be a promising approach to ensuring optimal comfort for residential users

Single-Solution Simulated Kalman Filter Algorithm for Global Optimisation Problems

This paper introduces single-solution Simulated Kalman Filter (ssSKF), a new single-agent optimisa- tion algorithm inspired by Kalman Filter, for solving real-valued numerical optimisation problems. In comparison, the proposed ssSKF algorithm supersedes the original population-based Simulated Kalman Filter (SKF) algorithm by operating with only a single agent, and having less parameters to be tuned. In the proposed ssSKF algorithm, the initialisation parameters are not constants, but, are produced by random numbers taken from a normal dis- tribution in the range of [0, 1], thus excluding them from tuning requirement. In order to balance between the exploration and exploitation in ssSKF, the proposed algorithm uses an adaptive neighbourhood mechanism during its prediction step. The proposed ssSKF algorithm is tested using the 30 benchmark functions of CEC 2014, and its performance is compared to the original SKF algorithm, Black Hole (BH) algorithm, Particle Swarm Optimisation (PSO) algorithm, Grey Wolf Optimiser (GWO) algorithm, and Genetic Algorithm (GA). The results show that the proposed ssSKF algorithm is a promising approach and able to outperform GWO and GA algorithms, significantly