8,158 research outputs found
Swarm Intelligence Based Multi-phase OPF For Peak Power Loss Reduction In A Smart Grid
Recently there has been increasing interest in improving smart grids
efficiency using computational intelligence. A key challenge in future smart
grid is designing Optimal Power Flow tool to solve important planning problems
including optimal DG capacities. Although, a number of OPF tools exists for
balanced networks there is a lack of research for unbalanced multi-phase
distribution networks. In this paper, a new OPF technique has been proposed for
the DG capacity planning of a smart grid. During the formulation of the
proposed algorithm, multi-phase power distribution system is considered which
has unbalanced loadings, voltage control and reactive power compensation
devices. The proposed algorithm is built upon a co-simulation framework that
optimizes the objective by adapting a constriction factor Particle Swarm
optimization. The proposed multi-phase OPF technique is validated using IEEE
8500-node benchmark distribution system.Comment: IEEE PES GM 2014, Washington DC, US
Meta-heuristic algorithms in car engine design: a literature survey
Meta-heuristic algorithms are often inspired by natural phenomena, including the evolution of species in Darwinian natural selection theory, ant behaviors in biology, flock behaviors of some birds, and annealing in metallurgy. Due to their great potential in solving difficult optimization problems, meta-heuristic algorithms have found their way into automobile engine design. There are different optimization problems arising in different areas of car engine management including calibration, control system, fault diagnosis, and modeling. In this paper we review the state-of-the-art applications of different meta-heuristic algorithms in engine management systems. The review covers a wide range of research, including the application of meta-heuristic algorithms in engine calibration, optimizing engine control systems, engine fault diagnosis, and optimizing different parts of engines and modeling. The meta-heuristic algorithms reviewed in this paper include evolutionary algorithms, evolution strategy, evolutionary programming, genetic programming, differential evolution, estimation of distribution algorithm, ant colony optimization, particle swarm optimization, memetic algorithms, and artificial immune system
Evolution Reinforces Cooperation with the Emergence of Self-Recognition Mechanisms: an empirical study of the Moran process for the iterated Prisoner's dilemma
We present insights and empirical results from an extensive numerical study
of the evolutionary dynamics of the iterated prisoner's dilemma. Fixation
probabilities for Moran processes are obtained for all pairs of 164 different
strategies including classics such as TitForTat, zero determinant strategies,
and many more sophisticated strategies. Players with long memories and
sophisticated behaviours outperform many strategies that perform well in a two
player setting. Moreover we introduce several strategies trained with
evolutionary algorithms to excel at the Moran process. These strategies are
excellent invaders and resistors of invasion and in some cases naturally evolve
handshaking mechanisms to resist invasion. The best invaders were those trained
to maximize total payoff while the best resistors invoke handshake mechanisms.
This suggests that while maximizing individual payoff can lead to the evolution
of cooperation through invasion, the relatively weak invasion resistance of
payoff maximizing strategies are not as evolutionarily stable as strategies
employing handshake mechanisms
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