This paper suggests novel optimal approach by progressive mapping search method (PMSM) of neural network aided particle swarm optimization (PSO) that can obtain global optimal solution easily and speed searching time up by PMSM. The PMSM by NN and PSO has an important role as navigation when PSO is going to search all areas to have an optimal solution, it can help to increase searching capabili...

The fuel consumption of a simulation model of a real Hybrid Electric Vehicle is optimized on a standardized driving cycle using metaheuristics (PSO, ES, GA). Search space discretization and metamodels are considered for reducing the number of required, time-expensive simulations. Two hybrid metaheuristics for combining the discussed methods are presented. In experiments it is shown that the use...

Yin-Yang-pair optimization (YYPO) is one of the latest metaheuristic algorithms (MA) proposed in 2015 that tries to inspire the philosophy of balance between conflicting concepts. Particle swarm optimizer (PSO) is one of the first population-based MA inspired by social behaviors of birds. In spite of PSO, the YYPO is not a nature inspired optimizer. It has a low complexity and starts with only ...

The process of learning Bayesian networks includes structure learning and parameters learning. During the process, learning the structure of Bayesian networks based on a large database is a NP hard problem. The paper presents a new hybrid algorithm by integrating the algorithms of MMPC (max-min parents and children), PSO (particle swarm optimization) and GA (genetic algorithm) effectively. In t...

Short Term Load Forecasting (STLF) is a power system operating procedures that have an important role in terms of realizing the economic electric production. This research focuses on the application of hybrid PSO-ANN algorithm in STLF. Load data grouped by the type of weekdays and holidays. Consumption of electricity load in West Java Indonesia, used as input to the learning algorithm PSO-ANN. ...

Data mining plays a very important role in the analysis of diseases and clustering approach makes it easier to classify the data collected in respective groups. Medicine companies and medical appliance manufacturer are benefitted from these data analysis. Now a days, this is done at a very large scale and has been named as big data analysis in which data size is of many terabytes. Optimization ...

This investigate proposed a innovative Improved Hybrid PSO-GA (IHPG) algorithm which it combined the advantages of the PSO algorithm and GA algorithm. The IHPG algorithm uses the velocity and position update rules of the PSO algorithm and the GA algorithm in selection, crossover and mutation thought. This study explores the quality monitoring experiment by three existing neural network approach...

Particle Swarm Optimization (PSO) is a popular algorithm used extensively in continuous optimization. One of its well-known drawbacks is its propensity for premature convergence. Many techniques have been proposed for alleviating this problem. One of the popular and promising approaches is low-level hybridization (LLH) of PSO with Genetic Algorithm (GA). Nevertheless, the LLH implementation is ...

This paper presents a Hybrid Particle Swarm Optimization (HPSO) method for solving the Task Assignment Problem (TAP) which is an np-hard problem. Particle Swarm Optimization (PSO) is a recently developed population based heuristic optimization technique. The algorithm has been developed to dynamically schedule heterogeneous tasks on to heterogeneous processors in a distributed setup. Load balan...

In this work, an experimental study to evaluate the parameter vector utility brought by an automated tuning tool, so called Hybrid Automatized Tuning procedure (HATp) is given. The experimental work uses the inertia weight and number of iterations from the algorithm PSO; it compares those parameters from tuning by analogy and empirical studies. The task of PSO is to select users to exploit conc...