نتایج جستجو برای: artificial bee colony abc algorithm
تعداد نتایج: 1071629 فیلتر نتایج به سال:
Nowadays computers are used to solve a variety and multitude of complex problems facing in every sphere of peoples’ life. However, many of the problems are intractable in nature exact algorithm might need centuries to manage with formidable challenges. In such cases heuristic or in a broader sense meta-heuristic algorithms that find an approximate solution but have acceptable time and space com...
Artificial Bee Colony (ABC) algorithm simulates the foraging behavior of honey bee colonies. ABC is an optimization technique, which is used in finding the best solution from all feasible solutions. However, there is still an insufficiency in ABC regarding improvement in exploitation and convergence speed. In order to improve the performance of ABC we embedded PSO into ABC. As PSO has memory, k...
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 ...
Artificial bee colony (ABC) algorithm, inspired by the intelligent foraging behavior of honey bees, was proposed by Karaboga. It has been shown to be superior to some conventional intelligent algorithms such as genetic algorithm (GA), artificial colony optimization (ACO), and particle swarm optimization (PSO). However, the ABC still has some limitations. For example, ABC can easily get trapped ...
The artificial bee colony algorithm (ABC), a population based algorithm, provides solutions with better accuracy compared to other competitive population based algorithms. However, it suffers from slow convergence speed. We suggest modifications in search strategy of ABC to improve overall performance and named this modified algorithm, Efficient ABC algorithm (EABC). The performance of EABC is ...
Artificial Bee Colony (ABC) algorithm is very interesting population based swarm optimization technique. This technique is motivated by means of extraordinary nature of honey bees. ABC algorithm commonly used to get to the bottom of nonlinear and complex problems. Comparable to other population based strategies, ABC also has some negative aspects. It is computationally steep because of its slug...
In this paper, Integrated Resource Planning (IRP) problem is formulated as an optimization problem in which the objective function is to minimize the total investment, operating, and outage (energy not served) costs of power system as well as the cost of DSM programs. Generation system reliability is assessed and provided by means of EENS and LOLP indices. For this sake, energy not served cost ...
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 ...
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 ...
In this paper, an improved method based on evolutionary algorithm for speech signal denoising is proposed. In this approach, the stochastic global optimization techniques such as Artificial Bee Colony(ABC), Cuckoo Search (CS)algorithm, and Particle Swarm Optimization (PSO) technique are exploited for learning the parameters of adaptive filtering function required for optimum performance. It was...
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