Differential Evoluiton - Particle Swarm Optimization

This paper shows the Particle Swarm Optimization algorithm with a Differential Evolution. Each candidate solution is sampled uniformly in [!5,5] D, whereDdenotes the search space dimension, and the evolution is performed with a classical PSO algorithm and a classical DE/x/1 algorithm according to a random threshold

Hybrid - Particle Swarm Optimization and Differential Evolution for Reduction of Real Power Loss and Preservation of Voltage Stability Limits

In this paper, a Hybrid algorithm based on - Particle Swarm Optimization (PSO) and Differential Evolution (DE) is used for solving reactive power dispatch problem. It needs progressing the population to create the individual optimal positions by means of the PSO algorithm, and then the algorithm come in DE phase and progresses the individual optimal positions by smearing the DE algorithm. In order to comprehend co-evolution of DE and PSO algorithm, an information-sharing mechanism is presented, which progresses the capability of the algorithm to fence out of the local optimum. Additionally, in optimization procedure, we espouse the hybrid inertia weight stratagem, time-varying acceleration coefficients tactic and arbitrary scaling factor stratagem. The proposed Hybrid algorithm based on - Particle Swarm Optimization and Differential Evolution (H-PSDE) has been tested on standard IEEE 30, 57,118 bus test systems and simulation results show clearly about the better performance of the proposed algorithm in reducing the real power loss. Keywords:Optimal Reactive Power; Transmission loss; Particle Swarm Optimization; Differential Evolution; Global Search; Local Search; Inertia Weight

Recognizing Complex Human Activities using Hybrid Feature Selections based on an Accelerometer Sensor

Wearable sensor technology is evolving in parallel with the demand for human activity monitoring applications. According to World Health Organization (WHO), the percentage of health problems occurring in the world population, such as diabetes, heart problem, and high blood pressure rapidly increases from year-to-year. Hence, regular exercise, at least twice a week, is encouraged for everyone, especially for adults and the elderly. An accelerometer sensor is preferable, due to privacy concerns and the low cost of installation. It is embedded within smartphones to monitor the amount of physical activity performed. One of the limitations of the various classifications is to deal with the large dimension of the feature space. Practically speaking, a large amount of memory space is demanded along with high processor performance to process a large number of features. Hence, the dimension of the features is required to be minimized by selecting the most relevant feature before it is classified. In order to tackle this issue, the hybrid feature selection using Relief-f and differential evolution is proposed. The public domain activity dataset from Physical Activity for Ageing People (PAMAP2) is used in the experimentation to identify the quality of the proposed method. Our experimental results show outstanding performance to recognize different types of physical activities with a minimum number of features. Subsequently, our findings indicate that the wrist is the best sensor placement to recognize the different types of human activity. The performance of our work also been compared with several state-of-the-art of features for selection algorithms

Large-Scale Evolutionary Optimization Using Multi-Layer Strategy Differential Evolution

Differential evolution (DE) has been extensively used in optimization studies since its development in 1995 because of its reputation as an effective global optimizer. DE is a population-based meta-heuristic technique that develops numerical vectors to solve optimization problems. DE strategies have a significant impact on DE performance and play a vital role in achieving stochastic global optimization. However, DE is highly dependent on the control parameters involved. In practice, the fine-tuning of these parameters is not always easy. Here, we discuss the improvements and developments that have been made to DE algorithms. The Multi-Layer Strategies Differential Evolution (MLSDE) algorithm, which finds optimal solutions for large scale problems. To solve large scale problems were grouped different strategies together and applied them to date set. Furthermore, these strategies were applied to selected vectors to strengthen the exploration ability of the algorithm. Extensive computational analysis was also carried out to evaluate the performance of the proposed algorithm on a set of well-known CEC 2015 benchmark functions. This benchmark was utilized for the assessment and performance evaluation of the proposed algorithm