fuzzy time series have been developed during the last decade to improve the forecast accuracy. many algorithms have been applied in this approach of forecasting such as high order time invariant fuzzy time series. in this paper, we present a hybrid algorithm to deal with the forecasting problem based on time variant fuzzy time series and particle swarm optimization algorithm, as a highly effici...

in this article, a multi-objective memetic algorithm (ma) for rule learning is proposed. prediction accuracy and interpretation are two measures that conflict with each other. in this approach, we consider accuracy and interpretation of rules sets. additionally, individual classifiers face other problems such as huge sizes, high dimensionality and imbalance classes’ distribution data sets. this...

this paper presents a fuzzy decision-making approach to deal with a clustering supplier problem in a supply chain system. during recent years, determining suitable suppliers in the supply chain has become a key strategic consideration. however, the nature of these decisions is usually complex and unstructured. in general, many quantitative and qualitative factors, such as quality, price, and fl...

Particle Swarm Optimization (PSO) is a metaheuristic that is highly used to solve monoand multi-objective optimization problems. Two well-differentiated PSO versions have been defined – one that operates in a continuous solution space and one for binary spaces. In this paper, a new version of the Binary PSO algorithm is presented. This version improves its operation by a suitable positioning of...

This paper presents a binary gravitational search algorithm (BGSA) is applied to solve the problem of optimal allotment of DG sets and Shunt capacitors in radial distribution systems. The problem is formulated as a nonlinear constrained single-objective optimization problem where the total line loss (TLL) and the total voltage deviations (TVD) are to be minimized separately by incorporating opt...

The use of support vector machines for multi-category problems is still an open field to research. Most of the published works use the one-against-rest strategy, but with a one-against-one approach results can be improved. To avoid testing with all the binary classifiers there are some methods like the Decision Directed Acyclic Graph based on a decision tree. In this work we propose an optimiza...

An optimized design of printed hybrid fractal tree (PHFT) antenna based on binary fractal tree geometry is presented and discussed. In this work miniaturization has been achieved by inserting a modified Koch curve in the conventional fractal binary tree. The geometrical descriptors of the proposed antenna have been synthesized using Bacterial Foraging Optimization (BFO) and Particle Swarm Optim...

to achieve an excellent thermal-mechanical performance of cmcs, it is necessary to analyze and design the thickness of the multi-layered interphases for an optimized trs distribution. an optimization was performed with a new version of the particle swarm optimization, the bsg-starcraft radius pso linked to a finite element software.

Particle swarm optimization (PSO) is used in several combinatorial optimization problems. In this work, particle swarms are used to solve quadratic programming problems with quadratic constraints. The central idea is to use PSO to move in the direction towards optimal solution rather than searching the entire feasible region. Binary classification is posed as a quadratically constrained quadrat...