The nonlinear theories of load forecasting, such as the applications of neural network and chaos, have recently made considerable progress. Generally, it is an effective method to combine phase space restructures theory with artificial neural networks (ANN) model for load forecasting. But, they are not so effective to forecast attractors with higher embedded dimension. The paper proposes a new ...

Symbol detection in multi-input multi-output (MIMO) communication systems using different particle swarm optimization (PSO) algorithms is presented. This approach is particularly attractive as particle swarm intelligence is well suited for real-time applications, where low complexity and fast convergence is of absolute importance. While an optimal maximum likelihood (ML) detection using an exha...

This paper presents a practical model for load frequency control (LFC) of two-area interconnected power system. The proposed model is a non-linear power system and is framed with thermal generating units including security constraint governor dead band imposed by the governor. A Proportional Integral Derivative (PID) controller is used for the design and analysis of the proposed model. The Part...

Recycling waste products is an environmental-friendly activity that can bring benefits to accompany, saving manufacturing costs and improving economic efficiency. For the beer industry, recycling bottles can reduce manufacturing costs and reduce the industry’s carbon footprint. This paper presents a model for a multi-objective collection-distribution center location and allocation problem in a ...

This paper presents a new approach for solution of the economic load dispatch (ELD) problem with valve-point effect using a modified particle swarm optimization (MPSO) technique. The practical ELD problems have non-smooth cost function with equality and inequality constraints, which make the problem of finding the global optimum difficult when using any mathematical approaches. In this paper, a...

In this work, we propose a method for self-organized task partitioning and allocation. The current state of the art provides neither an abstract understanding of task allocation problems in self-organized swarm systems, nor tools for modeling and designing such systems. We present the first steps towards a unified method capable of exactly this. We analyse the problem and provide a structured a...

Data mining is the process of discovering new relevant information in terms of patterns from large amount of data. Association rule mining is one of very important data mining techniques. Swarm optimization is a new subfield of artificial intelligence which studies the cooperative performance of simple agents. In this paper, proposed a new efficient algorithm for exploring high-class associatio...

This paper presents a new efficient approach to Economic Load Dispatch (ELD) problems with non-convex cost functions using PSO-ANN. The practical ELD problems have non-smooth cost functions considering valve point loading effects with equality and inequality constraints that make the problem of finding the global optimum difficult using any traditional mathematical approach. Therefore, Particle...

Human-robot interfaces for interacting with hundreds of autonomous robots must be very different from single-robot interfaces. The central design challenge is developing techniques to maintain, program, and interact with the robots without having to handle them individually. This requires robots that can support hands-free operation, which drives many other aspects of the design. This paper pre...

This paper proposes a new image thresholding segmentation approach using the heuristic method, Convergent Heterogeneous Particle Swarm Optimization algorithm. The proposed algorithm incorporates a new strategy of searching the problem space by dividing the swarm into subswarms. Each subswarm particles search for better solution separately lead to better exploitation while they cooperate with ea...