IJSIR 2010 - Application of Foraging Theory Based Optimization Algorithms to Power Systems Engineering for the International Journal of Swarm Intelligence Research (ISSN: 1947-9263)

Special Issue On: Application of Foraging Theory Based Optimization Algorithms to Power Systems Engineering

Guest Editors
B. K. Panigrahi and Swagatam Das

Introduction

Foraging means the act of searching for food and it forms an integral part of the daily life of most of the living creatures. Natural organisms forage in such a way as to maximize their energy intake per unit time. In other words, they strive to find, capture, and consume food containing the most calories while expending the least amount of time possible in doing so. Thus, considering all the constraints presented by their own physiology such as sensing and cognitive capabilities, environment (e.g. density of prey, risks from predators, and physical characteristics of the search space) etc., the natural foraging strategy that these organisms follow, can lead to optimization. Inspired by the conceptions emerging from foraging theory and sometimes based on their wide generalizations, in recent past, a handful of algorithms have been developed for solving nearly intractable numerical and combinatorial optimization problems in engineering. Most prominent among them are the Ant Systems (AS) and Ant Colony Optimization (ACO), Bacterial Foraging Optimization (BFO), and the algorithms based on bees social foraging. They have recently been shown to produce superior results in a wide variety of real-world applications. Some of the algorithms even use the concept of foraging in much more general sense, e.g. human beings forage for information, musician forage for a perfect state of harmony of the notes being played (E.g. the Harmony Search Algorithm). During the last five years, research on and with the foraging- based optimization techniques like ACO, BFOA, Bees Foraging, Artificial Bee Colony etc. has reached a very promising state.

Electric power system optimization problems are fairly diverse and they can be categorized in terms of the objective function characteristics and/or type of constraints. They are commonly referred to as linear, nonlinear, integer, and/or mixed integer constrained optimization problems. Traditionally, a derivative-based optimization technique is utilized to tackle a specific problem based on its formulation which requires differentiability among many other things. However, the foraging theory-based techniques can be easily adapted to suit various categories of optimization problems with minor modifications. This key attribute makes them the general purpose optimizers that solve a wide range of optimization problems. Their applications in electric power systems are similar to those in different research fields once a common formulation is established. However, the parameter tuning required for most efficient running of these algorithms might be different from one application to another and requires a good attention from the practitioners.

Objective Of The Special Issue

This special issue aims at bringing researchers from academia and industry together to report and review the latest progresses in this rapidly emerged field, to explore future directions of research, and to publicize the foraging based algorithms and its application to Power Engineering to a wider audience.

Recommended Topics

Topics to be discussed in this special issue include (but are not limited to) the following:

Theoretical and empirical study of single and multi-objective variants of foraging-based algorithms like:

The Ant Colony Optimization (ACO)
The Bacterial Foraging Optimization Algorithm (BFOA)
Honey Bee Social Foraging Algorithms
Adaptive Bird Flocking Algorithm
Harmony Search (HS)
for efficient application to the following areas in power systems:

Reactive Power and Voltage Control
Economic Dispatch (ED)
Power System Reliability and Security
Generation Expansion Problem (GEP)
Online state estimation
Load Flow and Optimal Power Flow
Power System Identification and Control
Generator Maintenance and Unit-Commitment Scheduling
Short-Term Load Forecasting (STLF)
Power Quality Classification
Submission

Prospective authors should note that only original and previously unpublished articles will be considered. Interested authors must consult the journal’s guidelines for manuscript submissions at http://www.igi-global.com/development/author_info/... prior to submission. All article submissions will be forwarded to at least 3 members of the Editorial Review Board of the journal for double-blind, peer review. Final decision regarding acceptance/revision/rejection will be based on the reviews received from the reviewers. All submissions must be forwarded electronically.