نتایج جستجو برای: bee colony optimization algorithm
تعداد نتایج: 1024006 فیلتر نتایج به سال:
This paper proposes a new artificial bee colony algorithm with quantum theory and the chaotic local search strategy (QCABC), and uses it to solve the optimal power flow (OPF) problem. Under the quantum computing theory, the QCABC algorithm encodes each individual with quantum bits to form a corresponding quantum bit string. By determining each quantum bits value, we can get the value of the ind...
Permanent magnet synchronous generators (PMSGs) are novel generators which can be used in high-performance wind farms. High efficiency and flexibility in producing electricity from variable rotation make them good candidate for wind power applications. Furthermore, because these kinds of generators have no excitation winding, there is no copper loss on rotor; hence, they can operate at high pow...
Artificial Bee Colony (ABC) is a swarm optimization technique. This algorithm generally used to solve nonlinear and complex problems. ABC is one of the simplest and up to date population based probabilistic strategy for global optimization. Analogous to other population based algorithms, ABC also has some drawbacks computationally pricey due to its sluggish temperament of search procedure. The ...
Artificial bee colony (ABC) algorithm is an optimization algorithm based on a particular intelligent behaviour of honeybee swarms. This work compares the performance of ABC algorithm with that of differential evolution (DE), particle swarm optimization (PSO) and evolutionary algorithm (EA) for multi-dimensional numeric problems. The simulation results show that the performance of ABC algorithm ...
Artificial Bee Colony (ABC) algorithm is a relatively new swarm intelligence algorithm that has attracted great deal of attention from researchers in recent years with the advantage of less control parameters and strong global optimization ability. However, there is still an insufficiency in ABC regarding its solution search equation, which is good at exploration but poor at exploitation. This ...
One of the essential parts of the software development process is software testing as it ensures the delivery of a good quality and reliable software. Various techniques and algorithms have been developed to carry out the testing process. This paper deals with utility of the nature based algorithms namely Genetic Algorithm, Ant Colony Optimization algorithm and Artificial Bee Colony algorithm i...
This article presents a hybrid evolutionary algorithm (PSABC) based on Artificial Bee Colony (ABC) and particle swarm optimization (PSO). Both of the algorithms are co-operative, population-based global search swarm intelligence met heuristics. The core of this algorithm is using PSO to optimize the fitness value of population in ABC. For Evaluation purpose, the proposed algorithm is tested on ...
Seeker optimization algorithm (SOA) is a novel search algorithm based on simulating the act of human searching, which has been shown to be a promising candidate among search algorithms for unconstrained function optimization. In this article we propose a modified seeker optimization algorithm. In order to enhance the performance of SOA, our proposed approach uses two search equations for produc...
Clustering is the unsupervised learning in which the data is divided into similar groups (cluster) without any prior knowledge. The emerging swarm-based algorithms become an alternative to the conventional clustering methods to enhance the quality of results. Artificial Bee Colony (ABC) Algorithm is one of the Swarm Intelligent based optimization algorithm that exhibit foraging properties of a ...
many real world problems can be modelled as an optimization problem. evolutionary algorithms are used to solve these problems. ant colony algorithm is a class of evolutionary algorithms that have been inspired of some specific ants looking for food in the nature. these ants leave trail pheromone on the ground to mark good ways that can be followed by other members of the group. ant colony optim...
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