Journal ArticleDOI
Quasi-oppositional Biogeography-based Optimization for Multi-objective Optimal Power Flow
Reads0
Chats0
TLDR
The results show that the new quasi-oppositional biogeography-based optimization algorithm outperforms the other techniques in terms of convergence speed and global search ability.Abstract:
This article develops an efficient and reliable evolutionary programming algorithm, namely quasi-oppositional biogeography-based optimization, for solving optimal power flow problems. To improve the simulation results as well as the speed of convergence, opposition-based learning is incorporated in the original biogeography-based optimization algorithm. In order to investigate the performance, the proposed scheme is applied on optimal power flow problems of standard 26-bus, IEEE 118-bus, and IEEE 300-bus systems; and comparisons among mixed-integer particle swarm optimization, evolutionary programming, the genetic algorithm, original biogeography-based optimization, and quasi-oppositional biogeography-based optimization are presented. The results show that the new quasi-oppositional biogeography-based optimization algorithm outperforms the other techniques in terms of convergence speed and global search ability.read more
Citations
More filters
Journal ArticleDOI
Multi-objective quasi-oppositional teaching learning based optimization for optimal location of distributed generator in radial distribution systems
Sneha Sultana,Provas Kumar Roy +1 more
TL;DR: A novel quasi-oppositional teaching learning based optimization (QOTLBO) methodology in order to find the optimal location of distributed generator to simultaneously optimize power loss, voltage stability index and voltage deviation of radial distribution network is presented.
Journal ArticleDOI
Opposition based learning: A literature review
TL;DR: This survey has been conducted on three classes of OBL attempts: a) theoretical, including the mathematical theorems and fundamental definitions, b) developmental, focusing on the design of the special OBL-based schemes, and c) real-world applications, which includes a comprehensive set of promising directions.
Journal ArticleDOI
Optimal reactive power dispatch using quasi-oppositional teaching learning based optimization
Barun Mandal,Provas Kumar Roy +1 more
TL;DR: Results demonstrate superiority in terms of solution quality of the proposed QOTLBO approach over original TLBO and other optimization techniques and confirm its potential to solve the ORPD problem.
Journal ArticleDOI
Grey wolf optimization applied to economic load dispatch problems
TL;DR: The results confirm the potential and effectiveness of the proposed GWO algorithm compared to various other methods available in the literature and proves that the GWO is a very effective optimization technique for solving various ELD problems.
Journal ArticleDOI
Teaching learning based optimization for short-term hydrothermal scheduling problem considering valve point effect and prohibited discharge constraint
TL;DR: In this article, a teaching learning based optimization (TLBO) algorithm is proposed to solve short-term hydrothermal scheduling (HTS) problem considering nonlinearities like valve point loading effects of the thermal unit and prohibited discharge zone of water reservoir of the hydro plants.
References
More filters
Journal ArticleDOI
Biogeography-Based Optimization
TL;DR: This paper discusses natural biogeography and its mathematics, and then discusses how it can be used to solve optimization problems, and sees that BBO has features in common with other biology-based optimization methods, such as GAs and particle swarm optimization (PSO).
Proceedings ArticleDOI
Opposition-Based Learning: A New Scheme for Machine Intelligence
TL;DR: Opposition-based learning as a new scheme for machine intelligence is introduced and possibilities for extensions of existing learning algorithms are discussed.
Journal ArticleDOI
An interior point nonlinear programming for optimal power flow problems with a novel data structure
TL;DR: In this article, a new interior point nonlinear programming algorithm for optimal power flow problems (OPF) based on the perturbed KKT conditions of the primal problem is presented. But the algorithm is not suitable for large-scale systems.
Journal ArticleDOI
Quadratically Convergent Optimal Power Flow
TL;DR: A newly developed sparse implementation of an optimization method using exact second derivatives is applied to the optimal power flow problem, and an option to add shunt capacitors in the event of hopeless infeasibility guarantees an optimal solution for many difficult to solve systems.
Journal ArticleDOI
Opposition versus randomness in soft computing techniques
TL;DR: This paper mathematically and experimentally proves that the simultaneous consideration of randomness and opposition is more advantageous than pure randomness, and applies that to accelerate differential evolution (DE).